Package: a4 Version: 1.55.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats, hgu95av2.db License: GPL-3 MD5sum: 62328ffcea69c884d330b102c101d1ed NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: devel git_last_commit: c714e25 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4_1.55.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 88 Package: a4Base Version: 1.55.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 3212771821859920375603eac3f30d05 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: devel git_last_commit: d78b7ff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4Base_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Base_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Base_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Base_1.55.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 79 Package: a4Classif Version: 1.55.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: b7af5109bfba4f239d13ee4ce5e7b36a NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: devel git_last_commit: 37f77ac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4Classif_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Classif_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Classif_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Classif_1.55.0.tgz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 33 Package: a4Core Version: 1.55.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d2d0ce867df2a7200bb7c0d600fda437 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: devel git_last_commit: 85fe6a4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4Core_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Core_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Core_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Core_1.55.0.tgz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 20 Package: a4Preproc Version: 1.55.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 8f055b54a4e208d211798f8e4a856e44 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: devel git_last_commit: 2b1a86f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4Preproc_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Preproc_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Preproc_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Preproc_1.55.0.tgz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 7 Package: a4Reporting Version: 1.55.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: aa19f45ff368856ba2f39588ce2caa92 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: devel git_last_commit: d4dd26b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/a4Reporting_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/a4Reporting_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/a4Reporting_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/a4Reporting_1.55.0.tgz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABarray Version: 1.75.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: b91d31a146a163cdbc7793d6637c94ee NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: devel git_last_commit: efb1f10 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ABarray_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ABarray_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ABarray_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ABarray_1.75.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: abseqR Version: 1.25.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: cbcbd1e25dac5dcad144c35023f81e0d NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: devel git_last_commit: 5c67450 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/abseqR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/abseqR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/abseqR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/abseqR_1.25.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 110 Package: ABSSeq Version: 1.61.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: c0631aebbe2996b0cb40d0238b9a9360 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: devel git_last_commit: 15bbde7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ABSSeq_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ABSSeq_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ABSSeq_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ABSSeq_1.61.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 10 Package: acde Version: 1.37.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 1fdbcd7b2160ac4e33fe02fbe835e187 NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: devel git_last_commit: 863c5e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/acde_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/acde_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/acde_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/acde_1.37.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R dependencyCount: 3 Package: ACE Version: 1.25.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f824ec9c06fde87add1827987bfb7efa NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: devel git_last_commit: 0696f37 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ACE_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ACE_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ACE_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ACE_1.25.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 89 Package: aCGH Version: 1.85.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: x64 MD5sum: 2064ffce40ff66812e8e0ddfe976ce13 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: devel git_last_commit: 061926d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/aCGH_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aCGH_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aCGH_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aCGH_1.85.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2 dependencyCount: 17 Package: ACME Version: 2.63.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: x64 MD5sum: d8506f8f4d510bd085bd48ac6be7f8a6 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: devel git_last_commit: e012f98 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ACME_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ACME_2.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ACME_2.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ACME_2.63.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 7 Package: ADaCGH2 Version: 2.47.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: x64 MD5sum: 40e18f1bab050760822b058e18fed82f NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan. Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: devel git_last_commit: a79cd26 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ADaCGH2_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADaCGH2_2.47.0.zip vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 100 Package: ADAM Version: 1.23.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat, rmarkdown, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: dbaeb5e5dc6fde0fc81ca76bd1168a3f NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression, Microarray Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: devel git_last_commit: 6c1c9e3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ADAM_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADAM_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADAM_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADAM_1.23.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 94 Package: ADAMgui Version: 1.23.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: markdown, BiocStyle License: GPL (>= 2) MD5sum: 632bb08472109113b822cb93159fc85d NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: devel git_last_commit: 9cd8b2c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ADAMgui_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADAMgui_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADAMgui_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADAMgui_1.23.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 163 Package: adductomicsR Version: 1.23.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 Archs: x64 MD5sum: 99db89d82b1f61e0659c477b5ba9378d NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: devel git_last_commit: 3c1217c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/adductomicsR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adductomicsR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adductomicsR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adductomicsR_1.23.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 137 Package: ADImpute Version: 1.17.0 Depends: R (>= 4.0) Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 3af750cdbed698a4793c355a1cd64aa9 NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] (ORCID: ) Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: devel git_last_commit: 1b9a5c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ADImpute_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ADImpute_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ADImpute_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ADImpute_1.17.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 66 Package: adSplit Version: 1.77.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) MD5sum: 46c1eec6c7802ba3e57455ab6d21be48 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: devel git_last_commit: 7c97ce7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/adSplit_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adSplit_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adSplit_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adSplit_1.77.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 55 Package: adverSCarial Version: 1.5.0 Depends: R (>= 3.5.0) Imports: gtools, S4Vectors, methods, DelayedArray Suggests: knitr, RUnit, BiocGenerics, TENxPBMCData, CHETAH, stringr, LoomExperiment License: MIT + file LICENSE Archs: x64 MD5sum: 6bbbd73bec9bcd776c6ae522a26fa844 NeedsCompilation: no Title: adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks Description: adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks. biocViews: Software, SingleCell, Transcriptomics, Classification Author: Ghislain FIEVET [aut, cre] (ORCID: ), Sébastien HERGALANT [aut] (ORCID: ) Maintainer: Ghislain FIEVET VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/adverSCarial git_branch: devel git_last_commit: 9d206a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/adverSCarial_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/adverSCarial_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/adverSCarial_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/adverSCarial_1.5.0.tgz vignettes: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.html, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.html, vignettes/adverSCarial/inst/doc/vign03_adapt_classifier.html, vignettes/adverSCarial/inst/doc/vign04_advRandWalkMinChange.html vignetteTitles: Vign01_adverSCarial, Vign02_overView_analysis, Vign03_adapt_classifiers, Vign04_advRandWalkMinChange hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.R, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.R dependencyCount: 24 Package: AffiXcan Version: 1.25.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 44f4b48e88792591ae07616c16f3f4f5 NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: devel git_last_commit: 091b100 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AffiXcan_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AffiXcan_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AffiXcan_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AffiXcan_1.25.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 67 Package: affxparser Version: 1.79.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) MD5sum: 603f98a35bdbf6aa3b8e69b354fa7873 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: devel git_last_commit: 25ddf5a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affxparser_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affxparser_1.79.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, EventPointer, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.85.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools, hgu95av2cdf License: LGPL (>= 2.0) Archs: x64 MD5sum: e2ed8c20e2244d7cab0f55040ef55a0a NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affy BugReports: https://github.com/rafalab/affy/issues git_url: https://git.bioconductor.org/packages/affy git_branch: devel git_last_commit: 501ff30 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affy_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affy_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affy_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affy_1.85.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, Cormotif, DrugVsDisease, ExiMiR, frmaTools, gcrma, maskBAD, panp, qpcrNorm, SCAN.UPC, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, CLL, curatedBladderData, ecoliLeucine, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, Cormotif, Doscheda, ffpe, frma, gcrma, GEOsubmission, Harshlight, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred, seeker suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, ath1121501frmavecs, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 12 Package: affycomp Version: 1.83.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) Archs: x64 MD5sum: e7caeee2cfffbe3e4efb6295d80da4d5 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affycomp BugReports: https://github.com/rafalab/affycomp/issues git_url: https://git.bioconductor.org/packages/affycomp git_branch: devel git_last_commit: 0f0f8ff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affycomp_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affycomp_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affycomp_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affycomp_1.83.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 7 Package: affyContam Version: 1.65.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 5f69f9c8d81c1ae9b131af2a78a3a0c3 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: devel git_last_commit: c1f4c9d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affyContam_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyContam_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyContam_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyContam_1.65.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R importsMe: arrayMvout dependencyCount: 15 Package: affycoretools Version: 1.79.0 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 02f716e8ea8953abdebb5fca14376e95 NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: devel git_last_commit: b82bcab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affycoretools_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affycoretools_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affycoretools_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affycoretools_1.79.0.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R suggestsMe: EnMCB dependencyCount: 198 Package: affyILM Version: 1.59.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 899577820895ea4a6f024dd6eb354010 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: devel git_last_commit: 3485abc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/affyILM_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyILM_1.59.0.zip vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 34 Package: affyio Version: 1.77.1 Depends: R (>= 2.6.0) Imports: methods License: LGPL (>= 2) MD5sum: 4acc9392b5bb5c0e13c76db85cf821b6 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: devel git_last_commit: 78cc925 git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/affyio_1.77.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyio_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyio_1.77.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyio_1.77.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 1 Package: affylmGUI Version: 1.81.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) Archs: x64 MD5sum: c72be9ad085cb64042a95827f248dae4 NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: devel git_last_commit: cc3b06c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/affylmGUI_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/affylmGUI_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affylmGUI_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affylmGUI_1.81.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 59 Package: affyPLM Version: 1.83.2 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS, hgu95av2cdf License: GPL (>= 2) MD5sum: ce330636678a97047455eb2fddee171d NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: devel git_last_commit: 38ee0fa git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/affyPLM_1.83.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/affyPLM_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/affyPLM_1.83.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/affyPLM_1.83.2.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: bapred importsMe: affylmGUI, arrayQualityMetrics, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 33 Package: AffyRNADegradation Version: 1.53.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample, hgu133acdf License: GPL-2 Archs: x64 MD5sum: 3599f6abb051706653aff1ce0a2ea6f3 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: devel git_last_commit: 7b43dc2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AffyRNADegradation_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AffyRNADegradation_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AffyRNADegradation_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AffyRNADegradation_1.53.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 13 Package: AGDEX Version: 1.55.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: ba8c9fb26f509d903503a8ef8bd21748 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: devel git_last_commit: 8b30309 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AGDEX_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AGDEX_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AGDEX_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AGDEX_1.55.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 51 Package: aggregateBioVar Version: 1.17.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: a77febdade5c51413201a8ce965d4b8e NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (ORCID: ), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: devel git_last_commit: 76d82e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/aggregateBioVar_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aggregateBioVar_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aggregateBioVar_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aggregateBioVar_1.17.0.tgz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 49 Package: agilp Version: 3.39.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 179d01c4b27c42c27006960a75ccc1ed NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: devel git_last_commit: 669a614 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/agilp_3.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/agilp_3.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/agilp_3.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/agilp_3.39.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.57.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 20fe00fa29c7140dd6e8091be3df4b06 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: devel git_last_commit: 0da1667 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AgiMicroRna_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AgiMicroRna_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AgiMicroRna_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AgiMicroRna_2.57.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 199 Package: AHMassBank Version: 1.7.0 Depends: R (>= 4.2) Imports: AnnotationHubData (>= 1.5.24) Suggests: BiocStyle, knitr, AnnotationHub (>= 2.7.13), rmarkdown, methods, CompoundDb (>= 1.1.4) License: Artistic-2.0 MD5sum: da7a9bea47b6f2a115478a81358e0bbf NeedsCompilation: no Title: MassBank Annotation Resources for AnnotationHub Description: Supplies AnnotationHub with MassBank metabolite/compound annotations bundled in CompDb SQLite databases. CompDb SQLite databases contain general compound annotation as well as fragment spectra representing fragmentation patterns of compounds' ions. MassBank data is retrieved from https://massbank.eu/MassBank and processed using helper functions from the CompoundDb Bioconductor package into redistributable SQLite databases. biocViews: MassSpectrometry, AnnotationHubSoftware Author: Johannes Rainer [cre] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/jorainer/AHMassBank VignetteBuilder: knitr BugReports: https://github.com/jorainer/AHMassBank/issues git_url: https://git.bioconductor.org/packages/AHMassBank git_branch: devel git_last_commit: 4e9a223 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AHMassBank_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AHMassBank_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AHMassBank_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AHMassBank_1.7.0.tgz vignettes: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.html vignetteTitles: Provide EnsDb databases for AnnotationHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.R dependencyCount: 124 Package: AIMS Version: 1.39.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2d090c5f9fbd79113cf5af9852d2a97d NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: devel git_last_commit: 0d17e7f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AIMS_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AIMS_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AIMS_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AIMS_1.39.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 12 Package: airpart Version: 1.15.0 Depends: R (>= 4.1) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater, stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue, dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2, ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid, grDevices, graphics, utils, pbapply Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0), gplots, tidyr License: GPL-2 MD5sum: 34d66622d66550a67adf5f672ab270e4 NeedsCompilation: no Title: Differential cell-type-specific allelic imbalance Description: Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets. biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing, GeneRegulation, GeneExpression, Transcription, TranscriptomeVariant, CellBiology, FunctionalGenomics, DifferentialExpression, GraphAndNetwork, Regression, Clustering, QualityControl Author: Wancen Mu [aut, cre] (ORCID: ), Michael Love [aut, ctb] (ORCID: ) Maintainer: Wancen Mu URL: https://github.com/Wancen/airpart VignetteBuilder: knitr BugReports: https://github.com/Wancen/airpart/issues git_url: https://git.bioconductor.org/packages/airpart git_branch: devel git_last_commit: 68bab81 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/airpart_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/airpart_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/airpart_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/airpart_1.15.0.tgz vignettes: vignettes/airpart/inst/doc/airpart.html vignetteTitles: Differential allelic imbalance with airpart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/airpart/inst/doc/airpart.R dependencyCount: 142 Package: alabaster Version: 1.7.0 Depends: alabaster.base Imports: alabaster.matrix, alabaster.ranges, alabaster.se, alabaster.sce, alabaster.spatial, alabaster.string, alabaster.vcf, alabaster.bumpy, alabaster.mae Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3498f82efcc02b1de884a3f017420dfa NeedsCompilation: no Title: Umbrella for the Alabaster Framework Description: Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster git_branch: devel git_last_commit: 1516307 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster_1.7.0.tgz vignettes: vignettes/alabaster/inst/doc/userguide.html vignetteTitles: alabaster umbrella hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster/inst/doc/userguide.R dependencyCount: 120 Package: alabaster.base Version: 1.7.2 Imports: alabaster.schemas, methods, utils, S4Vectors, rhdf5 (>= 2.47.6), jsonlite, jsonvalidate, Rcpp LinkingTo: Rcpp, assorthead (>= 1.1.2), Rhdf5lib Suggests: BiocStyle, rmarkdown, knitr, testthat, digest, Matrix, alabaster.matrix License: MIT + file LICENSE MD5sum: 314e1aea6c001e2f8e46f2552d6c9c2c NeedsCompilation: yes Title: Save Bioconductor Objects to File Description: Save Bioconductor data structures into file artifacts, and load them back into memory. This is a more robust and portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/alabaster.base SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/alabaster.base/issues git_url: https://git.bioconductor.org/packages/alabaster.base git_branch: devel git_last_commit: b34827c git_last_commit_date: 2024-11-11 Date/Publication: 2024-11-12 source.ver: src/contrib/alabaster.base_1.7.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.base_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.base_1.7.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.base_1.7.2.tgz vignettes: vignettes/alabaster.base/inst/doc/userguide.html vignetteTitles: Saving and loading artifacts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.base/inst/doc/userguide.R dependsOnMe: alabaster, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.sce, alabaster.se, alabaster.spatial, alabaster.string, alabaster.vcf importsMe: celldex, scRNAseq dependencyCount: 18 Package: alabaster.bumpy Version: 1.7.0 Depends: BumpyMatrix, alabaster.base Imports: methods, rhdf5, Matrix, BiocGenerics, S4Vectors, IRanges Suggests: BiocStyle, rmarkdown, knitr, testthat, jsonlite License: MIT + file LICENSE Archs: x64 MD5sum: 89b42726caf0473e08a8cd1e210f8f64 NeedsCompilation: no Title: Save and Load BumpyMatrices to/from file Description: Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.bumpy git_branch: devel git_last_commit: ab0a1f1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.bumpy_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.bumpy_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.bumpy_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.bumpy_1.7.0.tgz vignettes: vignettes/alabaster.bumpy/inst/doc/userguide.html vignetteTitles: Saving and loading BumpyMatrices hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.bumpy/inst/doc/userguide.R importsMe: alabaster dependencyCount: 25 Package: alabaster.files Version: 1.5.0 Depends: alabaster.base, Imports: methods, S4Vectors, BiocGenerics, Rsamtools Suggests: BiocStyle, rmarkdown, knitr, testthat, VariantAnnotation, rtracklayer, Biostrings License: MIT + file LICENSE MD5sum: 8f875b47d8e22a030d50ff7d8b1faea7 NeedsCompilation: no Title: Wrappers to Save Common File Formats Description: Save common bioinformatics file formats within the alabaster framework. This includes BAM, BED, VCF, bigWig, bigBed, FASTQ, FASTA and so on. We save and load additional metadata for each file, and we support linkage between each file and its corresponding index. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.files git_branch: devel git_last_commit: 2563297 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.files_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.files_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.files_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.files_1.5.0.tgz vignettes: vignettes/alabaster.files/inst/doc/userguide.html vignetteTitles: Saving common file formats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.files/inst/doc/userguide.R dependencyCount: 49 Package: alabaster.mae Version: 1.7.0 Depends: MultiAssayExperiment, alabaster.base Imports: methods, alabaster.se, S4Vectors, jsonlite, rhdf5 Suggests: testthat, knitr, SummarizedExperiment, BiocParallel, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 5a08ba1123e9fa889e6f35d0a0d993c8 NeedsCompilation: no Title: Load and Save MultiAssayExperiments Description: Save MultiAssayExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.mae git_branch: devel git_last_commit: f6e4c7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.mae_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.mae_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.mae_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.mae_1.7.0.tgz vignettes: vignettes/alabaster.mae/inst/doc/userguide.html vignetteTitles: Saving and loading MultiAssayExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.mae/inst/doc/userguide.R importsMe: alabaster dependencyCount: 71 Package: alabaster.matrix Version: 1.7.4 Depends: alabaster.base Imports: methods, BiocGenerics, S4Vectors, DelayedArray (>= 0.33.3), S4Arrays, SparseArray (>= 1.5.22), rhdf5 (>= 2.47.1), HDF5Array, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, chihaya, BiocSingular, ResidualMatrix License: MIT + file LICENSE MD5sum: ae975e4134d3cbad669f70772b8f15a1 NeedsCompilation: yes Title: Load and Save Artifacts from File Description: Save matrices, arrays and similar objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.matrix git_branch: devel git_last_commit: 617b1d5 git_last_commit_date: 2024-12-03 Date/Publication: 2024-12-03 source.ver: src/contrib/alabaster.matrix_1.7.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.matrix_1.7.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.matrix_1.7.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.matrix_1.7.4.tgz vignettes: vignettes/alabaster.matrix/inst/doc/userguide.html vignetteTitles: Saving and loading arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.matrix/inst/doc/userguide.R importsMe: alabaster, alabaster.se, celldex, scMultiome, scRNAseq suggestsMe: alabaster.base dependencyCount: 35 Package: alabaster.ranges Version: 1.7.0 Depends: GenomicRanges, alabaster.base Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomeInfoDb, rhdf5 Suggests: testthat, knitr, BiocStyle, jsonlite License: MIT + file LICENSE Archs: x64 MD5sum: 9e13e0cdd4626ea45ba1fb995246811b NeedsCompilation: no Title: Load and Save Ranges-related Artifacts from File Description: Save GenomicRanges, IRanges and related data structures into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.ranges git_branch: devel git_last_commit: 2f66649 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.ranges_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.ranges_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.ranges_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.ranges_1.7.0.tgz vignettes: vignettes/alabaster.ranges/inst/doc/userguide.html vignetteTitles: Saving and loading genomic ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.ranges/inst/doc/userguide.R importsMe: alabaster, alabaster.se dependencyCount: 33 Package: alabaster.sce Version: 1.7.0 Depends: SingleCellExperiment, alabaster.base Imports: methods, alabaster.se, jsonlite Suggests: knitr, testthat, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: adc1d44686eba44b850ea9886760c9db NeedsCompilation: no Title: Load and Save SingleCellExperiment from File Description: Save SingleCellExperiment into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.sce git_branch: devel git_last_commit: 458bd1e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.sce_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.sce_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.sce_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.sce_1.7.0.tgz vignettes: vignettes/alabaster.sce/inst/doc/userguide.html vignetteTitles: Saving and loading SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.sce/inst/doc/userguide.R importsMe: alabaster, alabaster.spatial, scRNAseq dependencyCount: 51 Package: alabaster.schemas Version: 1.7.0 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: cf4f1861a12577e03c67957b3681b9fc NeedsCompilation: no Title: Schemas for the Alabaster Framework Description: Stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base. We use a separate package instead of storing the schemas in alabaster.base itself, to avoid conflating management of the schemas with code maintenence. biocViews: DataRepresentation, DataImport Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.schemas git_branch: devel git_last_commit: 6197bd9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.schemas_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.schemas_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.schemas_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.schemas_1.7.0.tgz vignettes: vignettes/alabaster.schemas/inst/doc/userguide.html vignetteTitles: Metadata schemas for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: alabaster.base dependencyCount: 0 Package: alabaster.se Version: 1.7.0 Depends: SummarizedExperiment, alabaster.base Imports: methods, alabaster.ranges, alabaster.matrix, BiocGenerics, S4Vectors, IRanges, GenomicRanges, jsonlite Suggests: rmarkdown, knitr, testthat, BiocStyle License: MIT + file LICENSE MD5sum: e51c4d196f906972636979db7a27b6af NeedsCompilation: no Title: Load and Save SummarizedExperiments from File Description: Save SummarizedExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.se git_branch: devel git_last_commit: a745efa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.se_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.se_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.se_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.se_1.7.0.tgz vignettes: vignettes/alabaster.se/inst/doc/userguide.html vignetteTitles: Saving and loading SummarizedExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.se/inst/doc/userguide.R importsMe: alabaster, alabaster.mae, alabaster.sce, alabaster.vcf, celldex dependencyCount: 49 Package: alabaster.spatial Version: 1.7.1 Depends: SpatialExperiment, alabaster.base Imports: methods, utils, grDevices, S4Vectors, alabaster.sce, rhdf5 Suggests: testthat, knitr, rmarkdown, BiocStyle, DropletUtils, magick, png, digest License: MIT + file LICENSE MD5sum: 8ded4b4af5eaa74f04c88e89f38c41bb NeedsCompilation: no Title: Save and Load Spatial 'Omics Data to/from File Description: Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.spatial git_branch: devel git_last_commit: f82c877 git_last_commit_date: 2024-11-09 Date/Publication: 2024-11-10 source.ver: src/contrib/alabaster.spatial_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.spatial_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.spatial_1.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.spatial_1.7.1.tgz vignettes: vignettes/alabaster.spatial/inst/doc/userguide.html vignetteTitles: Saving spatial experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.spatial/inst/doc/userguide.R importsMe: alabaster dependencyCount: 86 Package: alabaster.string Version: 1.7.0 Depends: Biostrings, alabaster.base Imports: utils, methods, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, testthat License: MIT + file LICENSE MD5sum: dce38c1eecc7fce7496bd9ef69bf499f NeedsCompilation: no Title: Save and Load Biostrings to/from File Description: Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.string git_branch: devel git_last_commit: 383a583 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.string_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.string_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.string_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alabaster.string_1.7.0.tgz vignettes: vignettes/alabaster.string/inst/doc/userguide.html vignetteTitles: Saving and loading XStringSets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.string/inst/doc/userguide.R importsMe: alabaster, alabaster.vcf dependencyCount: 35 Package: alabaster.vcf Version: 1.7.0 Depends: alabaster.base, VariantAnnotation Imports: methods, S4Vectors, alabaster.se, alabaster.string, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 8eca20e77f60ae14ca434fc28f6b27e7 NeedsCompilation: no Title: Save and Load Variant Data to/from File Description: Save variant calling SummarizedExperiment to file and load them back as VCF objects. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.vcf git_branch: devel git_last_commit: 1e871d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/alabaster.vcf_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/alabaster.vcf_1.7.0.zip vignettes: vignettes/alabaster.vcf/inst/doc/userguide.html vignetteTitles: Saving and loading VCFs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.vcf/inst/doc/userguide.R importsMe: alabaster dependencyCount: 94 Package: ALDEx2 Version: 1.39.0 Depends: methods, stats, zCompositions, lattice, latticeExtra Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest, directlabels Suggests: testthat, BiocStyle, knitr, rmarkdown, purrr, ggpattern, ggplot2, cowplot, tidyverse, magick License: GPL (>=3) MD5sum: 636e554a88f263e444e1cf898ab7f6b7 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology, Scale simulation, Posterior p-value Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng, Michelle Nixon Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: devel git_last_commit: f7a6017 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ALDEx2_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ALDEx2_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ALDEx2_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ALDEx2_1.39.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html, vignettes/ALDEx2/inst/doc/scaleSim_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data, Incorporating Scale Uncertainty into ALDEx2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R, vignettes/ALDEx2/inst/doc/scaleSim_vignette.R dependsOnMe: omicplotR importsMe: aIc suggestsMe: pctax dependencyCount: 69 Package: alevinQC Version: 1.23.1 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2 (>= 3.4.0), GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager License: MIT + file LICENSE MD5sum: 20e53e85b938e7a835089b28b8a70591 NeedsCompilation: yes Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin, alevin-fry, or simpleaf run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: devel git_last_commit: 59a281c git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/alevinQC_1.23.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/alevinQC_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/alevinQC_1.23.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/alevinQC_1.23.1.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 90 Package: AllelicImbalance Version: 1.45.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: c9b02c68aba1cbeb935264381056b295 NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: devel git_last_commit: ffd47ab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AllelicImbalance_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AllelicImbalance_1.45.0.zip vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 163 Package: AlphaBeta Version: 1.21.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 559dd5e89c1c1c25c8dc14e595a98718 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: devel git_last_commit: 3bef2e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AlphaBeta_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlphaBeta_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlphaBeta_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlphaBeta_1.21.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 94 Package: AlphaMissenseR Version: 1.3.0 Depends: R (>= 4.3.0), dplyr Imports: rjsoncons (>= 1.0.1), DBI, duckdb (>= 0.9.1), rlang, curl, BiocFileCache, spdl, memoise, BiocBaseUtils, utils, stats, tools, methods, whisker, ggplot2 Suggests: BiocManager, BiocGenerics, GenomicRanges, GenomeInfoDb, AnnotationHub, ExperimentHub, ensembldb, httr, tidyr, r3dmol, bio3d, shiny, shiny.gosling, ggdist, gghalves, colorspace, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: e772d272577cfefc887cc990dfe3c214 NeedsCompilation: no Title: Accessing AlphaMissense Data Resources in R Description: The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo include, for instance, 71M variants across hg19 and hg38 genome builds. The 'AlphaMissenseR' package allows ready access to the data, downloading individual files to DuckDB databases for exploration and integration into *R* and *Bioconductor* workflows. biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction, Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology Author: Martin Morgan [aut, cre] (ORCID: ), Tram Nguyen [aut] (ORCID: ), Tyrone Lee [ctb], Nitesh Turaga [ctb], Chan Zuckerberg Initiative DAF CZF2019-002443 [fnd], NIH NCI ITCR U24CA180996 [fnd], NIH NCI IOTN U24CA232979 [fnd], NIH NCI ARTNet U24CA274159 [fnd] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/AlphaMissenseR/ VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/AlphaMissenseR/issues git_url: https://git.bioconductor.org/packages/AlphaMissenseR git_branch: devel git_last_commit: 2fc3e2c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AlphaMissenseR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlphaMissenseR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlphaMissenseR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlphaMissenseR_1.3.0.tgz vignettes: vignettes/AlphaMissenseR/inst/doc/alphafold.html, vignettes/AlphaMissenseR/inst/doc/benchmarking.html, vignettes/AlphaMissenseR/inst/doc/clinvar.html, vignettes/AlphaMissenseR/inst/doc/introduction.html, vignettes/AlphaMissenseR/inst/doc/issues.html vignetteTitles: B. AlphaFold Integration, D. Benchmarking with ProteinGym, C. ClinVar Integration, A. Introduction, E. Issues & Solutions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AlphaMissenseR/inst/doc/alphafold.R, vignettes/AlphaMissenseR/inst/doc/benchmarking.R, vignettes/AlphaMissenseR/inst/doc/clinvar.R, vignettes/AlphaMissenseR/inst/doc/introduction.R, vignettes/AlphaMissenseR/inst/doc/issues.R dependencyCount: 70 Package: AlpsNMR Version: 4.9.0 Depends: R (>= 4.2) Imports: utils, generics, graphics, stats, grDevices, cli, magrittr (>= 1.5), dplyr (>= 1.1.0), signal (>= 0.7-6), rlang (>= 0.3.0.1), scales (>= 1.2.0), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), tidyselect, readxl (>= 1.1.0), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.22.0), matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0), BiocParallel (>= 1.34.0) Suggests: ASICS, BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5), GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr, NMRphasing, plotly (>= 4.7.1), progressr, SummarizedExperiment, S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>= 2.0.4) License: MIT + file LICENSE MD5sum: 7c81600d1049b38a8e56b0211adb2045 NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre] (ORCID: ), Francisco Madrid Gambin [aut] (ORCID: ), Luis Fernandez [aut] (ORCID: ), Laura López Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (ORCID: ), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph], Miller Jack [ctb] (ORCID: , Autophase wrapper, ASICS export) Maintainer: Sergio Oller Moreno URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR VignetteBuilder: knitr BugReports: https://github.com/sipss/AlpsNMR/issues git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: devel git_last_commit: f8b70f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AlpsNMR_4.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AlpsNMR_4.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AlpsNMR_4.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AlpsNMR_4.9.0.tgz vignettes: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.pdf, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.pdf, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.pdf vignetteTitles: Vignette 01: Introduction to AlpsNMR (start here), Older Introduction to AlpsNMR (soft-deprecated API), Vignette 02: Handling metadata and annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.R, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.R, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.R dependencyCount: 132 Package: altcdfenvs Version: 2.69.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 0989cc77613dbcdda34a14b05d1dbd7c NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: devel git_last_commit: 6869520 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/altcdfenvs_2.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/altcdfenvs_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/altcdfenvs_2.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/altcdfenvs_2.69.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight dependencyCount: 34 Package: AMARETTO Version: 1.23.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr, BiocStyle License: Apache License (== 2.0) + file LICENSE MD5sum: 5eb58c200e109e726ccef47a351fe306 NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: devel git_last_commit: 6b4ead5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AMARETTO_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AMARETTO_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AMARETTO_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AMARETTO_1.23.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 153 Package: AMOUNTAIN Version: 1.33.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) MD5sum: 5c60b53754b1a87ba32e4f5154adb4bd NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: devel git_last_commit: 03af056 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AMOUNTAIN_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AMOUNTAIN_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AMOUNTAIN_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AMOUNTAIN_1.33.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.29.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), pwalign, data.table (>= 1.10.4-3) Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 3.3.4), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), cluster (>= 2.1.4) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: x64 MD5sum: dc731008fd6965c3dbf708433c4a1621 NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: devel git_last_commit: 555fe1e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/amplican_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/amplican_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/amplican_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/amplican_1.29.0.tgz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 128 Package: Anaquin Version: 2.31.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 0e856eb1dadc4b595d4b250aaa1a1b7b NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: devel git_last_commit: 6b70356 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Anaquin_2.31.0.tar.gz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 93 Package: ANCOMBC Version: 2.9.0 Depends: R (>= 4.3.0) Imports: stats, CVXR, DescTools, Hmisc, MASS, Matrix, Rdpack, doParallel, doRNG, energy, foreach, gtools, lme4, lmerTest, multcomp, nloptr, parallel, utils Suggests: mia (>= 1.6.0), DT, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, dplyr, knitr, magrittr, microbiome, phyloseq, rmarkdown, testthat, tidyr, tidyverse License: Artistic-2.0 Archs: x64 MD5sum: 16aa86319a4bbed42f36f6bbd2070049 NeedsCompilation: no Title: Microbiome differential abudance and correlation analyses with bias correction Description: ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (ORCID: ) Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues git_url: https://git.bioconductor.org/packages/ANCOMBC git_branch: devel git_last_commit: 6c44bf5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ANCOMBC_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ANCOMBC_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ANCOMBC_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ANCOMBC_2.9.0.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html, vignettes/ANCOMBC/inst/doc/ANCOMBC.html, vignettes/ANCOMBC/inst/doc/ANCOMBC2.html, vignettes/ANCOMBC/inst/doc/data_sanity_check.html, vignettes/ANCOMBC/inst/doc/SECOM.html vignetteTitles: ANCOM Tutorial, ANCOM-BC Tutorial, ANCOM-BC2 Tutorial, Tutorial on Data Sanity and Integrity Checks, SECOM Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOM.R, vignettes/ANCOMBC/inst/doc/ANCOMBC.R, vignettes/ANCOMBC/inst/doc/ANCOMBC2.R, vignettes/ANCOMBC/inst/doc/data_sanity_check.R, vignettes/ANCOMBC/inst/doc/SECOM.R suggestsMe: MiscMetabar dependencyCount: 137 Package: AneuFinder Version: 1.35.0 Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, reshape2, ggdendro, ggrepel, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: b44169ab3c45d8d56cd9409a9e8e34ba NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: ImmunoOncology, Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: devel git_last_commit: 42b7837 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AneuFinder_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AneuFinder_1.35.0.zip vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R dependencyCount: 94 Package: ANF Version: 1.29.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 29bdbbbfac526413f9b46da404d69136 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: devel git_last_commit: 25ef886 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ANF_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ANF_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ANF_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ANF_1.29.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 24 Package: animalcules Version: 1.23.0 Depends: R (>= 4.3.0) Imports: ape, assertthat, caret, covr, DESeq2, dplyr, DT, forcats, ggforce, ggplot2, GUniFrac, lattice, limma, magrittr, Matrix, methods, MultiAssayExperiment, plotly, rentrez, reshape2, ROCit, S4Vectors (>= 0.23.19), scales, shiny, shinyjs, stats, SummarizedExperiment, tibble, tidyr, tsne, umap, utils, vegan, XML Suggests: BiocStyle, biomformat, devtools, glmnet, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: ae926f45efd1b1e112d25ba7e99074a6 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Jessica McClintock [cre], Yue Zhao [aut] (ORCID: ), Anthony Federico [aut] (ORCID: ), W. Evan Johnson [aut] (ORCID: ) Maintainer: Jessica McClintock URL: https://github.com/wejlab/animalcules VignetteBuilder: knitr BugReports: https://github.com/wejlab/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: devel git_last_commit: 1f256fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/animalcules_1.23.0.tar.gz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R importsMe: LegATo dependencyCount: 193 Package: annaffy Version: 1.79.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 96c539beb1dc9ff298ce8e4115cf0e37 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: devel git_last_commit: 5617a63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/annaffy_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annaffy_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annaffy_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annaffy_1.79.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: metaMA dependencyCount: 48 Package: annmap Version: 1.49.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: 7fb24c28a01bf519188c5b4b2c573640 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. The annmap database can be downloaded from: https://figshare.manchester.ac.uk/account/articles/16685071 biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: devel git_last_commit: da770e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/annmap_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annmap_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annmap_1.49.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 72 Package: annotate Version: 1.85.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 28437ee87b94a9df3e1ffd22e5cae322 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotate git_branch: devel git_last_commit: 732426e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/annotate_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotate_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annotate_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annotate_1.85.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf, vignettes/annotate/inst/doc/chromLOC.html, vignettes/annotate/inst/doc/useDataPkgs.html vignetteTitles: Annotation Overview, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Affymetrix Probe Level Data, HowTo: Build and use chromosomal information, Using Bioconductor's Annotation Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLOC.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, geneplotter, GSEABase, idiogram, MineICA, MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats, lumi, methylumi, MGFR, phenoTest, qpgraph, SGCP, tigre, UMI4Cats, easyDifferentialGeneCoexpression, geneExpressionFromGEO, GOxploreR suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, metagenomeSeq, MLP, pageRank, PhosR, RnBeads, siggenes, SummarizedExperiment, 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rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, SomaScan.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI dependencyCount: 48 Package: AnnotationDbi Version: 1.69.0 Depends: R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: d5469c79e59a01c6e7fb8b2ad598601f NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: devel git_last_commit: faea5ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnnotationDbi_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationDbi_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationDbi_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationDbi_1.69.0.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category, ChromHeatMap, customProDB, EGSEA, EpiTxDb, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, SomaScan.db, soybeanprobe, sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, bioCancer, BiocSet, biomaRt, BioNAR, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, compEpiTools, consensusDE, CoSIA, crisprDesign, CrispRVariants, cTRAP, Damsel, debrowser, derfinder, DominoEffect, DOSE, doubletrouble, EasyCellType, EDASeq, EnrichmentBrowser, ensembldb, EpiMix, epimutacions, erma, esATAC, FRASER, funOmics, GA4GHshiny, gage, gDNAx, genefilter, geneplotter, GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR, ggbio, gINTomics, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, goSTAG, GOstats, goTools, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, interactiveDisplay, isomiRs, IVAS, karyoploteR, keggorthology, LRBaseDbi, lumi, magpie, mastR, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MineICA, MiRaGE, mirIntegrator, MIRit, miRNAmeConverter, missMethyl, MLP, MOSClip, mosdef, MSnID, multiGSEA, multiMiR, NanoMethViz, NetSAM, ORFik, Organism.dplyr, OutSplice, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, qpgraph, QuasR, RAIDS, ReactomePA, REDseq, regutools, rGREAT, rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch, simplifyEnrichment, SMITE, SubCellBarCode, SVMDO, TCGAutils, tenXplore, TFutils, tigre, trackViewer, TRESS, tricycle, txcutr, txdbmaker, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, HDO.db, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, HPO.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, MPO.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, g3viz, jetset, Mega2R, MOCHA, netgsa, pathfindR, prioGene, ProFAST, RCPA, RobLoxBioC, seeker, WGCNA suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gatom, gCrisprTools, GeDi, GeneRegionScan, GenomicPlot, GenomicRanges, ggkegg, gsean, hpar, iNETgrate, iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc, OUTRIDER, pathlinkR, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, scDotPlot, simona, SingleCellAlleleExperiment, sparrow, spatialHeatmap, SpliceWiz, SummarizedExperiment, systemPipeR, tidybulk, topconfects, weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, easylabel, genekitr, goat, MARVEL, pagoda2, Platypus, rliger, scITD, SCpubr dependencyCount: 45 Package: AnnotationFilter Version: 1.31.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: ca8b2772a588d11e94749ede832c6134 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: devel git_last_commit: 164eaea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnnotationFilter_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationFilter_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationFilter_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationFilter_1.31.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, CompoundDb, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, CleanUpRNAseq, drugTargetInteractions, ggbio, QFeatures, RAIDS, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, locuszoomr, RNAseqQC suggestsMe: GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 25 Package: AnnotationForge Version: 1.49.0 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db, rmarkdown, BiocStyle, knitr, BiocManager, BiocFileCache, RUnit License: Artistic-2.0 MD5sum: b8150f0487b55cb4cf5885737d22b719 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Hervé Pagès [aut], Madelyn Carlson [ctb] ('Creating probe packages' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: devel git_last_commit: 9b33fc1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnnotationForge_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationForge_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationForge_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationForge_1.49.0.tgz vignettes: vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/makeProbePackage.html, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Creating probe packages, Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 49 Package: AnnotationHub Version: 3.15.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, txdbmaker, MSnbase, mzR, Biostrings, CompoundDb, keras, ensembldb, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 19e24ec71967bcdb4d997d297b840382 NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: devel git_last_commit: 7a750df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnnotationHub_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationHub_3.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationHub_3.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnnotationHub_3.15.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, LRcell, octad, AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39, rGenomeTracksData, synaptome.data, UCSCRepeatMasker, MetaGxBreast, MetaGxOvarian, NestLink, scMultiome, sesameData, tartare, annotation, sequencing, OSCA.workflows, SingleRBook importsMe: annotatr, atena, BiocHubsShiny, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, DMRcate, dmrseq, EpiCompare, EpiMix, epimutacions, epiregulon, gDNAx, GenomicScores, GRaNIE, GSEABenchmarkeR, gwascat, iSEEhub, MACSr, meshes, MetaboAnnotation, MethReg, Moonlight2R, MSnID, OGRE, ontoProc, orthos, partCNV, psichomics, regutools, REMP, scanMiRApp, scAnnotatR, scmeth, scTensor, singleCellTK, SpliceWiz, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, EPICv2manifest, grasp2db, HPO.db, metaboliteIDmapping, MPO.db, synaptome.db, TENET.AnnotationHub, adductData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, crisprScoreData, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, easierData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GenomicDistributionsData, HCAData, HiBED, HiContactsData, HMP16SData, mcsurvdata, MerfishData, MetaGxPancreas, MouseAgingData, msigdb, orthosData, scpdata, scRNAseq, SFEData, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TCGAWorkflow, RNAseqQC, SeedMatchR suggestsMe: AHMassBank, AlphaMissenseR, autonomics, BgeeCall, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, factR, GenomicRanges, Glimma, GOSemSim, HiCool, LRBaseDbi, lute, maser, MIRA, motifTestR, MSnbase, multicrispr, nullranges, OrganismDbi, plotgardener, raer, recountmethylation, satuRn, scTensor, simona, TCGAbiolinks, TCGAutils, tidyCoverage, VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, BioPlex, CoSIAdata, HarmonizedTCGAData, homosapienDEE2CellScore, ProteinGymR, GeneSelectR, locuszoomr dependencyCount: 65 Package: AnnotationHubData Version: 1.37.0 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 322757a2bdff450c52d2c710c528986a NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: devel git_last_commit: 1e4d239 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnnotationHubData_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnnotationHubData_1.37.0.zip vignettes: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHMassBank, AHEnsDbs, EuPathDB suggestsMe: HubPub, EPICv2manifest, GenomicState, TENET.AnnotationHub, homosapienDEE2CellScore, humanHippocampus2024, smokingMouse dependencyCount: 123 Package: annotationTools Version: 1.81.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: 4d1d3f136db70fbe1e2ac925ecb2efc7 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: devel git_last_commit: ad67b95 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/annotationTools_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotationTools_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/annotationTools_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/annotationTools_1.81.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: CoSIA dependencyCount: 7 Package: annotatr Version: 1.33.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.23.10), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: d41adc950c49f9d23983a7dc0d86c8e6 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: devel git_last_commit: d81bd7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/annotatr_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/annotatr_1.33.0.zip vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: annotatr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth, ExpHunterSuite suggestsMe: borealis, ramr dependencyCount: 123 Package: anota Version: 1.55.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 19a80e33348fe2d21e93636d83e909e1 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: devel git_last_commit: c705686 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/anota_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/anota_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/anota_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/anota_1.55.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 48 Package: anota2seq Version: 1.29.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: b139eaca9dca1bd1200684276ee51103 NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Ola Larsson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: devel git_last_commit: 9a745aa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/anota2seq_1.29.0.tar.gz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 87 Package: antiProfiles Version: 1.47.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 682ab8563cc1ede647a99c9bbb627000 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: devel git_last_commit: d930a6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/antiProfiles_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/antiProfiles_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/antiProfiles_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/antiProfiles_1.47.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVIL Version: 1.19.4 Depends: R (>= 3.6), dplyr, AnVILBase Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), yaml, tibble, tidyselect, tidyr, rlang, shiny, DT, miniUI, htmltools, BiocBaseUtils Suggests: parallel, knitr, rmarkdown, testthat, withr, readr, BiocStyle, devtools, AnVILAz, AnVILGCP, lifecycle License: Artistic-2.0 MD5sum: 3ae7a3c4d541ef43fc4cc8e4da02fe69 NeedsCompilation: no Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and developer functionality. For the end-user, AnVIL provides fast binary package installation, utitlities for working with Terra / AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programatic access to the Terra, Leonardo, Rawls, and Dockstore RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R. biocViews: Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Yubo Cheng [aut], Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: devel git_last_commit: 8e47721 git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-18 source.ver: src/contrib/AnVIL_1.19.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVIL_1.19.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVIL_1.19.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVIL_1.19.4.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html, vignettes/AnVIL/inst/doc/RunningWorkflow.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package, Running an AnVIL workflow within R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R, vignettes/AnVIL/inst/doc/RunningWorkflow.R dependsOnMe: cBioPortalData importsMe: AnVILPublish, AnVILWorkflow, terraTCGAdata suggestsMe: AnVILBase, AnVILGCP dependencyCount: 75 Package: AnVILAz Version: 1.1.0 Imports: AnVILBase, BiocBaseUtils, curl, httr2, jsonlite, methods, rjsoncons, tibble, utils Suggests: BiocStyle, dplyr, knitr, readr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 8bd1154025900ddfdc7b49d26ff3c6aa NeedsCompilation: no Title: R / Bioconductor Support for the AnVIL Azure Platform Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVILAz package supports end-users and developers using the AnVIL platform in the Azure cloud. The package provides a programmatic interface to AnVIL resources, including workspaces, notebooks, tables, and workflows. The package also provides utilities for managing resources, including copying files to and from Azure Blob Storage, and creating shared access signatures (SAS) for secure access to Azure resources. biocViews: Software, Infrastructure, ThirdPartyClient Author: Martin Morgan [aut, ctb] (ORCID: ), Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILAz SystemRequirements: az, azcopy VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILAz/issues git_url: https://git.bioconductor.org/packages/AnVILAz git_branch: devel git_last_commit: 3d01105 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnVILAz_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILAz_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILAz_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILAz_1.1.0.tgz vignettes: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.html, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.html vignetteTitles: Working with Workspaces on AnVIL Azure, Introduction to the AnVILAz package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.R, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.R suggestsMe: AnVIL, AnVILBase dependencyCount: 34 Package: AnVILBase Version: 1.1.0 Imports: httr, httr2, dplyr, jsonlite, methods, tibble Suggests: AnVIL, AnVILAz, AnVILGCP, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), tinytest License: Artistic-2.0 MD5sum: 9345b146bff0fdecb9dcb0c1ca4af3ea NeedsCompilation: no Title: Generic functions for interacting with the AnVIL ecosystem Description: Provides generic functions for interacting with the AnVIL ecosystem. Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb] (ORCID: ), NIH NHGRI U24HG004059 [fnd] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILBase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILBase/issues git_url: https://git.bioconductor.org/packages/AnVILBase git_branch: devel git_last_commit: 95da536 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnVILBase_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILBase_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBase_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILBase_1.1.0.tgz vignettes: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.html vignetteTitles: Introduction to AnVILBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.R dependsOnMe: AnVIL, AnVILWorkflow importsMe: AnVILAz, AnVILGCP suggestsMe: terraTCGAdata dependencyCount: 30 Package: AnVILBilling Version: 1.17.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 7061c6f5c9868455060600043cfb01dc NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: devel git_last_commit: 67011bd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnVILBilling_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILBilling_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBilling_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILBilling_1.17.0.tgz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 98 Package: AnVILGCP Version: 1.1.1 Imports: AnVILBase, BiocBaseUtils, dplyr, httr, jsonlite, methods, rlang, stats, tibble, tidyr, utils Suggests: AnVIL, BiocStyle, httr2, knitr, rmarkdown, testthat, withr License: Artistic-2.0 MD5sum: befd6ae4d3fac2e1d92d9f7a1dba997d NeedsCompilation: no Title: The GCP R Client for the AnVIL Description: The package provides a set of functions to interact with the Google Cloud Platform (GCP) services on the AnVIL platform. The package is designed to work with the AnVIL package. User-level interaction with this package should be minimal. biocViews: Software, Infrastructure, ThirdPartyClient, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Nitesh Turaga [aut], Martin Morgan [aut] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILGCP SystemRequirements: gsutil, gcloud VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILGCP/issues git_url: https://git.bioconductor.org/packages/AnVILGCP git_branch: devel git_last_commit: 4a02116 git_last_commit_date: 2024-10-30 Date/Publication: 2024-10-30 source.ver: src/contrib/AnVILGCP_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILGCP_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILGCP_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILGCP_1.1.1.tgz vignettes: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.html vignetteTitles: Working with AnVIL on GCP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.R dependsOnMe: AnVILWorkflow, terraTCGAdata importsMe: AnVILPublish suggestsMe: AnVIL, AnVILBase dependencyCount: 38 Package: AnVILPublish Version: 1.17.0 Imports: AnVIL, AnVILGCP, BiocBaseUtils, BiocManager, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 647bc3017ad65d68da1f7d914070113e NeedsCompilation: no Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Vincent Carey [ctb] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: devel git_last_commit: ff5bfe9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnVILPublish_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AnVILPublish_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AnVILPublish_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AnVILPublish_1.17.0.tgz vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html vignetteTitles: Publishing R / Bioconductor packages to AnVIL Workspaces hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R dependencyCount: 88 Package: AnVILWorkflow Version: 1.7.1 Depends: R (>= 4.4.0), AnVILGCP, AnVILBase, httr Imports: AnVIL, dplyr, jsonlite, rlang, tibble, tidyr, utils, methods, plyr, stringr Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 5d3ba276d271fa5e3c5d9a5a9442692d NeedsCompilation: no Title: Run workflows implemented in Terra/AnVIL workspace Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The main cloud-based genomics platform deported by the AnVIL project is Terra. The AnVILWorkflow package allows remote access to Terra implemented workflows, enabling end-user to utilize Terra/ AnVIL provided resources - such as data, workflows, and flexible/scalble computing resources - through the conventional R functions. biocViews: Infrastructure, Software Author: Sehyun Oh [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ), Kai Gravel-Pucillo [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/AnVILWorkflow VignetteBuilder: knitr BugReports: https://github.com/shbrief/AnVILWorkflow/issues git_url: https://git.bioconductor.org/packages/AnVILWorkflow git_branch: devel git_last_commit: 87eb1ab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AnVILWorkflow_1.7.1.tar.gz vignettes: vignettes/AnVILWorkflow/inst/doc/salmon.html vignetteTitles: Quickstart - RNAseq analysis using salmon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILWorkflow/inst/doc/salmon.R dependencyCount: 78 Package: APAlyzer Version: 1.21.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools, HybridMTest Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 MD5sum: 766fc387726984bb62e761772eb56146 NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (ORCID: ), Bin Tian [aut], Wei-Chun Chen [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: devel git_last_commit: 7a59a45 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/APAlyzer_1.21.0.tar.gz vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R dependencyCount: 123 Package: apComplex Version: 2.73.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 968189ac370600e04608e3128e110923 NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: devel git_last_commit: f895ff9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/apComplex_2.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/apComplex_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/apComplex_2.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/apComplex_2.73.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependencyCount: 52 Package: apeglm Version: 1.29.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 MD5sum: bfadc0ed7ad9917e3b012df2ba06bb31 NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: devel git_last_commit: 9318bfd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/apeglm_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/apeglm_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/apeglm_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/apeglm_1.29.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: airpart, debrowser, DiffBind, ERSSA, phantasus, Rmmquant, TEKRABber suggestsMe: bambu, DESeq2, extraChIPs, fishpond, NanoporeRNASeq, RNAseqQC dependencyCount: 48 Package: APL Version: 1.11.2 Depends: R (>= 4.4.0) Imports: Matrix, RSpectra, ggrepel, ggplot2, viridisLite, plotly, SeuratObject, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, sparseMatrixStats, testthat License: GPL (>= 3) Archs: x64 MD5sum: e3d117025e223f053b527febdfa3d508 NeedsCompilation: no Title: Association Plots Description: APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest. biocViews: StatisticalMethod, DimensionReduction, SingleCell, Sequencing, RNASeq, GeneExpression Author: Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin Vingron [aut] Maintainer: Clemens Kohl URL: https://vingronlab.github.io/APL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: devel git_last_commit: e04cb82 git_last_commit_date: 2024-11-04 Date/Publication: 2024-11-05 source.ver: src/contrib/APL_1.11.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/APL_1.11.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/APL_1.11.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/APL_1.11.2.tgz vignettes: vignettes/APL/inst/doc/APL.html vignetteTitles: Analyzing data with APL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APL/inst/doc/APL.R dependencyCount: 129 Package: appreci8R Version: 1.25.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db, utils License: LGPL-3 MD5sum: b134a7e941b76e387e22d21beaaf56ce NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: devel git_last_commit: 2946ad7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/appreci8R_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/appreci8R_1.25.0.zip vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 165 Package: aroma.light Version: 3.37.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: 5b69addf90fa7b82878c0aa94cf00532 NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: devel git_last_commit: 8bc7bbb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/aroma.light_3.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/aroma.light_3.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/aroma.light_3.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/aroma.light_3.37.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.67.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: oligo, limma, httr, utils, jsonlite, rlang, tools, methods Suggests: affy License: Artistic-2.0 MD5sum: 8856aeb5a97653cc4a442ef6f3801fcb NeedsCompilation: no Title: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet. biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert, Jose Marugan Maintainer: Jose Marugan git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: devel git_last_commit: 2b97e7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/ArrayExpress_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ArrayExpress_1.67.0.zip vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease, maEndToEnd suggestsMe: bapred, seeker dependencyCount: 67 Package: arrayMvout Version: 1.65.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy Imports: mdqc, affyContam, lumi Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 876c688ac8b6e581baef58b25b8b3453 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: devel git_last_commit: 18d3025 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/arrayMvout_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/arrayMvout_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/arrayMvout_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/arrayMvout_1.65.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 171 Package: arrayQuality Version: 1.85.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 6bce6f31b56b8c436237b9aee286ea92 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: devel git_last_commit: b1c3550 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/arrayQuality_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/arrayQuality_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/arrayQuality_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/arrayQuality_1.85.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: arrayQualityMetrics Version: 3.63.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 384ed988d0ed3372cdb59bfa5f3b3d70 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: devel git_last_commit: c608533 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/arrayQualityMetrics_3.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/arrayQualityMetrics_3.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/arrayQualityMetrics_3.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/arrayQualityMetrics_3.63.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R dependsOnMe: maEndToEnd dependencyCount: 136 Package: ARRmNormalization Version: 1.47.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 98f1a1b6dd113b1cbb1ca42b316334e6 NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: devel git_last_commit: 5281457 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ARRmNormalization_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ARRmNormalization_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ARRmNormalization_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ARRmNormalization_1.47.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.25.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table, dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR, knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 17efac8bcded7804837f19c96fe3262c NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (ORCID: ), Alexandre Rosa Campos [aut, ctb] (ORCID: ), John Von Dollen [aut], Nevan Krogan [aut] (ORCID: ), Danielle Swaney [aut, ctb] (ORCID: ) Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: devel git_last_commit: 10716cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/artMS_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/artMS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/artMS_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/artMS_1.25.0.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 146 Package: ASAFE Version: 1.33.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 78adca6885dd4645bd03fc5276c379b3 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: devel git_last_commit: b28315b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASAFE_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASAFE_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASAFE_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASAFE_1.33.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.51.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) MD5sum: 07aef7e57dfe97cfabf7ac0f3cde18d2 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: devel git_last_commit: 951d216 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASEB_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASEB_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASEB_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASEB_1.51.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.41.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 Archs: x64 MD5sum: a184d8d96778d7a748f28db22e7da39c NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: devel git_last_commit: 3ac7ee1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASGSCA_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASGSCA_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASGSCA_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASGSCA_1.41.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: ASICS Version: 2.23.0 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 5fe9b743a1dc45048f6aa15074829dd6 NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: devel git_last_commit: 034bc20 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASICS_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASICS_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASICS_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASICS_2.23.0.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R suggestsMe: AlpsNMR dependencyCount: 131 Package: ASpli Version: 2.17.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics, pbmcapply, txdbmaker License: GPL MD5sum: 157f0527f269762c874416e83220e86e NeedsCompilation: no Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Ariel Chernomoretz git_url: https://git.bioconductor.org/packages/ASpli git_branch: devel git_last_commit: 5b28a12 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASpli_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASpli_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASpli_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASpli_2.17.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R dependencyCount: 174 Package: AssessORF Version: 1.25.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown, RSQLite (>= 1.1) License: GPL-3 MD5sum: 039e462f05d0e7a327fb116bab8207f4 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: devel git_last_commit: 504f577 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AssessORF_1.25.0.tar.gz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R dependencyCount: 29 Package: ASSET Version: 2.25.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 124b4fd01505ebdb16c9ac72a63bf7d8 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant. biocViews: StatisticalMethod, SNP, GenomeWideAssociation, MultipleComparison Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: devel git_last_commit: 081f9f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASSET_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASSET_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASSET_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASSET_2.25.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 27 Package: ASSIGN Version: 1.43.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ffef0fd0ccdeced68898a695c76e60b0 NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: devel git_last_commit: e4797e8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASSIGN_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASSIGN_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASSIGN_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASSIGN_1.43.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 100 Package: assorthead Version: 1.1.7 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 9f8c8ba54f521546bcf7f246dafb600f NeedsCompilation: no Title: Assorted Header-Only C++ Libraries Description: Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. biocViews: SingleCell, QualityControl, Normalization, DataRepresentation, DataImport, DifferentialExpression, Alignment Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/LTLA/assorthead VignetteBuilder: knitr BugReports: https://github.com/LTLA/assorthead/issues git_url: https://git.bioconductor.org/packages/assorthead git_branch: devel git_last_commit: 50c1a94 git_last_commit_date: 2024-12-20 Date/Publication: 2024-12-20 source.ver: src/contrib/assorthead_1.1.7.tar.gz win.binary.ver: bin/windows/contrib/4.5/assorthead_1.1.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/assorthead_1.1.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/assorthead_1.1.7.tgz vignettes: vignettes/assorthead/inst/doc/userguide.html vignetteTitles: User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/assorthead/inst/doc/userguide.R linksToMe: alabaster.base, beachmat, beachmat.hdf5, BiocNeighbors, BiocSingular, bluster, bsseq, epiregulon, glmGamPoi, scrapper, SingleR dependencyCount: 0 Package: ASURAT Version: 1.11.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp (>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize, grid, grDevices, graphics LinkingTo: Rcpp Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi, BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 8dbf2451372b8c87c754210855ce6a16 NeedsCompilation: yes Title: Functional annotation-driven unsupervised clustering for single-cell data Description: ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs). biocViews: GeneExpression, SingleCell, Sequencing, Clustering, GeneSignaling Author: Keita Iida [aut, cre] (ORCID: ), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASURAT git_branch: devel git_last_commit: 433833d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ASURAT_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ASURAT_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ASURAT_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ASURAT_1.11.0.tgz vignettes: vignettes/ASURAT/inst/doc/ASURAT.html vignetteTitles: ASURAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASURAT/inst/doc/ASURAT.R dependencyCount: 59 Package: ATACseqQC Version: 1.31.0 Depends: R (>= 3.5.0), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR, BiocParallel Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 9c1a54d63fb3e172f3cc7bebbe403f6e NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: devel git_last_commit: dcc0b6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ATACseqQC_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ATACseqQC_1.31.0.zip vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R suggestsMe: ATACseqTFEA dependencyCount: 189 Package: ATACseqTFEA Version: 1.9.0 Depends: R (>= 4.2) Imports: BiocGenerics, S4Vectors, IRanges, Matrix, GenomicRanges, GenomicAlignments, GenomeInfoDb, SummarizedExperiment, Rsamtools, motifmatchr, TFBSTools, stats, pracma, ggplot2, ggrepel, dplyr, limma, methods, rtracklayer Suggests: BSgenome.Drerio.UCSC.danRer10, knitr, testthat, ATACseqQC, rmarkdown, BiocStyle License: GPL-3 MD5sum: f4c623a8be30975a924fb0966d54d7aa NeedsCompilation: no Title: Transcription Factor Enrichment Analysis for ATAC-seq Description: Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions. biocViews: Sequencing, DNASeq, ATACSeq, MNaseSeq, GeneRegulation Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/ATACseqTFEA VignetteBuilder: knitr BugReports: https://github.com/jianhong/ATACseqTFEA/issues git_url: https://git.bioconductor.org/packages/ATACseqTFEA git_branch: devel git_last_commit: a628ae2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ATACseqTFEA_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ATACseqTFEA_1.9.0.zip vignettes: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.html vignetteTitles: ATACseqTFEA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.R dependencyCount: 133 Package: atena Version: 1.13.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, MatrixGenerics, BiocParallel, S4Vectors, IRanges, GenomicFeatures, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats, AnnotationHub, matrixStats, cli Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit, TxDb.Dmelanogaster.UCSC.dm6.ensGene, RColorBrewer License: Artistic-2.0 MD5sum: 1fab4bf559cf56d156964892c504500a NeedsCompilation: no Title: Analysis of Transposable Elements Description: Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/rcastelo/atena VignetteBuilder: knitr BugReports: https://github.com/rcastelo/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: devel git_last_commit: 0778c4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/atena_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/atena_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/atena_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/atena_1.13.0.tgz vignettes: vignettes/atena/inst/doc/atena.html vignetteTitles: An introduction to the atena package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atena/inst/doc/atena.R dependencyCount: 100 Package: atSNP Version: 1.23.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 6656fa02ff06add1a723bde24144336e NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: devel git_last_commit: 38e28cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/atSNP_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/atSNP_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/atSNP_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/atSNP_1.23.0.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 167 Package: attract Version: 1.59.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) Archs: x64 MD5sum: 264fdbcea205735ab3a1381afdb3454e NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: devel git_last_commit: 33404ba git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/attract_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/attract_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/attract_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/attract_1.59.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 73 Package: AUCell Version: 1.29.0 Imports: DelayedArray, DelayedMatrixStats, data.table, graphics, grDevices, GSEABase, Matrix, methods, mixtools, R.utils, stats, SummarizedExperiment, BiocGenerics, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: 103051bd69a9ca5c471c19bd390de490 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Gert Hulselmans URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: devel git_last_commit: 1ef38fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AUCell_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AUCell_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AUCell_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AUCell_1.29.0.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R importsMe: epiregulon, escape, RcisTarget, scFeatures suggestsMe: decoupleR, scDiagnostics, SCpubr dependencyCount: 121 Package: autonomics Version: 1.15.36 Depends: R (>= 4.0) Imports: abind, BiocFileCache, BiocGenerics, bit64, cluster, codingMatrices, colorspace, data.table, dplyr, edgeR, ggforce, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, RColorBrewer, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, tidyr, tidyselect, tools, utils, vsn Suggests: affy, AnnotationDbi, AnnotationHub, apcluster, Biobase, BiocManager, BiocStyle, Biostrings, diagram, DBI, e1071, ensembldb, GenomicDataCommons, GenomicRanges, GEOquery, hgu95av2.db, ICSNP, jsonlite, knitr, lme4, lmerTest, MASS, patchwork, mixOmics, mpm, nlme, OlinkAnalyze, org.Hs.eg.db, org.Mm.eg.db, pcaMethods, pheatmap, progeny, propagate, RCurl, RSQLite, remotes, rmarkdown, ropls, Rsubread, readODS, rtracklayer, statmod, survival, survminer, testthat, UniProt.ws, writexl, XML License: GPL-3 MD5sum: 7d78f5fe5d87f22936de446d6b40f00e NeedsCompilation: no Title: Unified Statistical Modeling of Omics Data Description: This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs. biocViews: Software, DataImport, Preprocessing, DimensionReduction, PrincipalComponent, Regression, DifferentialExpression, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, RNASeq, Microarray, Proteomics, Metabolomics, MassSpectrometry, Author: Aditya Bhagwat [aut, cre], Richard Cotton [ctb], Shahina Hayat [ctb], Laure Cougnaud [ctb], Witold Szymanski [ctb], Vanessa Beutgen [ctb], Willem Ligtenberg [ctb], Hinrich Goehlmann [ctb], Karsten Suhre [ctb], Johannes Graumann [aut, sad] Maintainer: Aditya Bhagwat VignetteBuilder: knitr BugReports: https://gitlab.uni-marburg.de/fb20/ag-graumann/software/autonomics/issues git_url: https://git.bioconductor.org/packages/autonomics git_branch: devel git_last_commit: 5cb516d git_last_commit_date: 2024-12-04 Date/Publication: 2024-12-04 source.ver: src/contrib/autonomics_1.15.36.tar.gz win.binary.ver: bin/windows/contrib/4.5/autonomics_1.15.36.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/autonomics_1.15.36.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/autonomics_1.15.36.tgz vignettes: vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.html vignetteTitles: autonomics_platformaware_analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.R dependencyCount: 115 Package: AWFisher Version: 1.21.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 MD5sum: 0602334dbc6589da5167f454a8b0c648 NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: devel git_last_commit: 83d328b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/AWFisher_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/AWFisher_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/AWFisher_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/AWFisher_1.21.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.15.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 0d6a16c233e2cf9864f31c3541db9641 NeedsCompilation: no Title: Asymmetric Within-Sample Transformation Description: We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts. biocViews: Normalization, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph] (ORCID: ), Stefano Pagnotta [aut, cph] (ORCID: ) Maintainer: Davide Risso URL: https://github.com/drisso/awst VignetteBuilder: knitr BugReports: https://github.com/drisso/awst/issues git_url: https://git.bioconductor.org/packages/awst git_branch: devel git_last_commit: d18c8ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/awst_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/awst_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/awst_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/awst_1.15.0.tgz vignettes: vignettes/awst/inst/doc/awst_intro.html vignetteTitles: Introduction to awst hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/awst/inst/doc/awst_intro.R dependencyCount: 37 Package: BaalChIP Version: 1.33.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b6079c678f35e00f366764f6d8bea167 NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: devel git_last_commit: 6f46c69 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BaalChIP_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BaalChIP_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BaalChIP_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BaalChIP_1.33.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 99 Package: bacon Version: 1.35.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) MD5sum: 06f4df3cdb3d32f4e4208ac8e422e6cc NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: devel git_last_commit: 22a0e4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bacon_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bacon_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bacon_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bacon_1.35.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 46 Package: BADER Version: 1.45.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 MD5sum: 920547d8385d607bd44ab521b2194a7d NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: devel git_last_commit: 2798fff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BADER_1.45.0.tar.gz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.35.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 Archs: x64 MD5sum: 1336ea45fb9b6fc1f2e7d4e93ff78e90 NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: devel git_last_commit: 7025a46 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BadRegionFinder_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BadRegionFinder_1.35.0.zip vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 103 Package: BAGS Version: 2.47.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 MD5sum: 0518901a34fd1ce02204c70610a24643 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: devel git_last_commit: 323f3fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BAGS_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BAGS_2.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BAGS_2.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BAGS_2.47.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 8 Package: ballgown Version: 2.39.0 Depends: R (>= 3.5.0), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr, markdown License: Artistic-2.0 Archs: x64 MD5sum: 5a7b7ddfe9719786ea7426b55d35004a NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: devel git_last_commit: 01a0bbb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ballgown_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ballgown_2.39.0.zip vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R dependsOnMe: VaSP suggestsMe: variancePartition dependencyCount: 90 Package: bambu Version: 3.9.0 Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), BSgenome, IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, purrr, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE MD5sum: 704a50ba91399f59332f7a60002cf36b NeedsCompilation: yes Title: Context-Aware Transcript Quantification from Long Read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, LongRead, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: devel git_last_commit: 0a0bfc1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/bambu_3.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bambu_3.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bambu_3.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bambu_3.9.0.tgz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R importsMe: FLAMES suggestsMe: NanoporeRNASeq dependencyCount: 95 Package: bamsignals Version: 1.39.0 Depends: R (>= 3.5.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 21b24310dd7ac2802c46f29229a635e1 NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: devel git_last_commit: df2b237 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bamsignals_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bamsignals_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bamsignals_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bamsignals_1.39.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, DNAfusion, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 26 Package: BANDITS Version: 1.23.0 Depends: R (>= 4.3.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) Archs: x64 MD5sum: 9d982541e644148f58eed67401af3168 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: devel git_last_commit: 46f9bab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BANDITS_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BANDITS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BANDITS_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BANDITS_1.23.0.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R importsMe: DifferentialRegulation dependencyCount: 85 Package: bandle Version: 1.11.0 Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang, RColorBrewer, gtools, gridExtra, coda (>= 0.19-4) LinkingTo: Rcpp, RcppArmadillo, BH Suggests: testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Archs: x64 MD5sum: 5440ced877a2fce71e7e80523fc6be15 NeedsCompilation: yes Title: An R package for the Bayesian analysis of differential subcellular localisation experiments Description: The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data. biocViews: Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry Author: Oliver M. Crook [aut, cre] (ORCID: ), Lisa Breckels [aut] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/bandle VignetteBuilder: knitr BugReports: https://github.com/ococrook/bandle/issues git_url: https://git.bioconductor.org/packages/bandle git_branch: devel git_last_commit: ca58277 git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-05 source.ver: src/contrib/bandle_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bandle_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bandle_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bandle_1.11.0.tgz vignettes: vignettes/bandle/inst/doc/v01-getting-started.html, vignettes/bandle/inst/doc/v02-workflow.html vignetteTitles: Analysing differential localisation experiments with BANDLE: Vignette 1, Analysing differential localisation experiments with BANDLE: Vignette 2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bandle/inst/doc/v01-getting-started.R, vignettes/bandle/inst/doc/v02-workflow.R dependencyCount: 238 Package: Banksy Version: 1.3.0 Depends: R (>= 4.4.0) Imports: aricode, data.table, dbscan, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, S4Vectors, stats, matrixStats, mclust, igraph, irlba, leidenAlg (>= 1.1.0), utils, uwot, RcppHungarian Suggests: knitr, rmarkdown, pals, scuttle, scater, scran, cowplot, ggplot2, testthat (>= 3.0.0), harmony, Seurat, ExperimentHub, spatialLIBD, BiocStyle License: file LICENSE Archs: x64 MD5sum: 70aad5f8c35fa430d4207cca69e4a028 NeedsCompilation: no Title: Spatial transcriptomic clustering Description: Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation. biocViews: Clustering, Spatial, SingleCell, GeneExpression, DimensionReduction Author: Vipul Singhal [aut], Joseph Lee [aut, cre] (ORCID: ) Maintainer: Joseph Lee URL: https://github.com/prabhakarlab/Banksy VignetteBuilder: knitr BugReports: https://github.com/prabhakarlab/Banksy/issues git_url: https://git.bioconductor.org/packages/Banksy git_branch: devel git_last_commit: abf3265 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/Banksy_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Banksy_1.3.0.zip vignettes: vignettes/Banksy/inst/doc/batch-correction.html, vignettes/Banksy/inst/doc/domain-segment.html, vignettes/Banksy/inst/doc/multi-sample.html, vignettes/Banksy/inst/doc/parameter-selection.html vignetteTitles: Spatial data integration with Harmony (10x Visium Human DLPFC), Domain segmentation (STARmap PLUS mouse brain), Multi-sample analysis (10x Visium Human DLPFC), Parameter selection (VeraFISH Mouse Hippocampus) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Banksy/inst/doc/batch-correction.R, vignettes/Banksy/inst/doc/domain-segment.R, vignettes/Banksy/inst/doc/multi-sample.R, vignettes/Banksy/inst/doc/parameter-selection.R dependencyCount: 111 Package: banocc Version: 1.31.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 38a45c0d9b5eb383145f94a89f33cf7b NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: devel git_last_commit: eef3ada git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/banocc_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/banocc_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/banocc_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/banocc_1.31.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 66 Package: barcodetrackR Version: 1.15.0 Depends: R (>= 4.1) Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges, graphics, grDevices, magrittr, plyr, proxy, RColorBrewer, rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors, tibble, tidyr, vegan, viridis, utils Suggests: BiocStyle, knitr, magick, rmarkdown, testthat License: file LICENSE MD5sum: 30804183e779bef73e3f0ffa6dc834c3 NeedsCompilation: no Title: Functions for Analyzing Cellular Barcoding Data Description: barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies. biocViews: Software, Visualization, Sequencing Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut] Maintainer: Diego Alexander Espinoza URL: https://github.com/dunbarlabNIH/barcodetrackR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/barcodetrackR git_branch: devel git_last_commit: c0c5333 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/barcodetrackR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/barcodetrackR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/barcodetrackR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/barcodetrackR_1.15.0.tgz vignettes: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.html vignetteTitles: barcodetrackR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.R dependencyCount: 103 Package: basecallQC Version: 1.31.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: 9f94546a5874cd0dd73ea7e27e4a05e8 NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: devel git_last_commit: 0ca2dc6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/basecallQC_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basecallQC_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basecallQC_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basecallQC_1.31.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 126 Package: BaseSpaceR Version: 1.51.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 60a85ce955f7f16cd0ba3877739ec5f1 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: devel git_last_commit: 835c641 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BaseSpaceR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BaseSpaceR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BaseSpaceR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BaseSpaceR_1.51.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.43.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 02b070751ffdef4ac7ff5ab2275ce5ac NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: devel git_last_commit: 1f73086 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Basic4Cseq_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Basic4Cseq_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Basic4Cseq_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Basic4Cseq_1.43.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 63 Package: BASiCS Version: 2.19.0 Depends: R (>= 4.2), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment, viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat, reshape2, BiocParallel, posterior, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, magick License: GPL-3 MD5sum: 47d03ece2932df001918e7b196663328 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre] (ORCID: ), Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: devel git_last_commit: 055feb8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BASiCS_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BASiCS_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BASiCS_2.19.0.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R dependsOnMe: BASiCStan suggestsMe: splatter dependencyCount: 142 Package: BASiCStan Version: 1.9.0 Depends: R (>= 4.2), BASiCS, rstan (>= 2.18.1) Imports: methods, glmGamPoi, scran, scuttle, stats, utils, SingleCellExperiment, SummarizedExperiment, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 4bb22cfad535d8e851a31be1229b75d5 NeedsCompilation: yes Title: Stan implementation of BASiCS Description: Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology Author: Alan O'Callaghan [aut, cre], Catalina Vallejos [aut] Maintainer: Alan O'Callaghan URL: https://github.com/Alanocallaghan/BASiCStan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/BASiCStan/issues git_url: https://git.bioconductor.org/packages/BASiCStan git_branch: devel git_last_commit: a071873 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BASiCStan_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BASiCStan_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BASiCStan_1.9.0.tgz vignettes: vignettes/BASiCStan/inst/doc/BASiCStan.html vignetteTitles: An introduction to BASiCStan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCStan/inst/doc/BASiCStan.R dependencyCount: 163 Package: BasicSTARRseq Version: 1.35.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 Archs: x64 MD5sum: fd028ff1976d44806e2f5baadd9c3df7 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: devel git_last_commit: 2f45ad6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BasicSTARRseq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BasicSTARRseq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BasicSTARRseq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BasicSTARRseq_1.35.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 52 Package: basilisk Version: 1.19.0 Depends: reticulate Imports: utils, methods, parallel, dir.expiry, basilisk.utils (>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: c74f04ca170b25eb3090687276e9471b NeedsCompilation: no Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr BugReports: https://github.com/LTLA/basilisk/issues git_url: https://git.bioconductor.org/packages/basilisk git_branch: devel git_last_commit: 7e940de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/basilisk_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basilisk_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basilisk_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basilisk_1.19.0.tgz vignettes: vignettes/basilisk/inst/doc/motivation.html vignetteTitles: Motivation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk/inst/doc/motivation.R dependsOnMe: scviR importsMe: BiocHail, BiocSklearn, cbpManager, cfTools, crisprScore, densvis, FLAMES, HiCool, MACSr, MOFA2, ontoProc, orthos, Pirat, Rcwl, recountmethylation, ReUseData, scifer, scPipe, sketchR, snifter, spatialDE, velociraptor, zellkonverter suggestsMe: basilisk.utils, CuratedAtlasQueryR dependencyCount: 23 Package: basilisk.utils Version: 1.19.0 Imports: utils, methods, tools, dir.expiry Suggests: reticulate, knitr, rmarkdown, BiocStyle, testthat, basilisk License: GPL-3 MD5sum: 9b92c90fefe00444b19cb9e14327776e NeedsCompilation: no Title: Basilisk Installation Utilities Description: Implements utilities for installation of the basilisk package, primarily for creation of the underlying Conda instance. This allows us to avoid re-writing the same R code in both the configure script (for centrally administered R installations) and in the lazy installation mechanism (for distributed package binaries). It is highly unlikely that developers - or, heaven forbid, end-users! - will need to interact with this package directly; they should be using the basilisk package instead. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: devel git_last_commit: f596ce0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/basilisk.utils_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/basilisk.utils_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/basilisk.utils_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/basilisk.utils_1.19.0.tgz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: _basilisk_ installation utilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: basilisk, crisprScore, LimROTS, scifer dependencyCount: 5 Package: batchelor Version: 1.23.0 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, SparseArray, DelayedArray (>= 0.31.5), DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 MD5sum: 4459733830062d871a5f380496b0a88f NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: devel git_last_commit: 045cc75 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/batchelor_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/batchelor_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/batchelor_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/batchelor_1.23.0.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependsOnMe: OSCA.intro, OSCA.workflows importsMe: ChromSCape, mumosa, scMerge, singleCellTK, SCIntRuler suggestsMe: TSCAN, Canek, RaceID dependencyCount: 68 Package: BatchQC Version: 2.3.0 Depends: R (>= 4.4.0) Imports: data.table, DESeq2, dplyr, EBSeq, ggdendro, ggnewscale, ggplot2, limma, matrixStats, pheatmap, RColorBrewer, reader, reshape2, scran, shiny, shinyjs, shinythemes, stats, SummarizedExperiment, sva, S4Vectors, tibble, tidyr, tidyverse, utils Suggests: BiocManager, BiocStyle, bladderbatch, devtools, knitr, lintr, plotly, rmarkdown, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 7945c5c6aa72a5f03070a0a1ff9ecb55 NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, Normalization, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Jessica McClintock [aut, cre] (ORCID: ), W. Evan Johnson [aut] (ORCID: ), Solaiappan Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb], Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan Holmes [aut], Solomon Joseph [ctb] Maintainer: Jessica McClintock URL: https://github.com/wejlab/BatchQC VignetteBuilder: knitr BugReports: https://github.com/wejlab/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: devel git_last_commit: 8029628 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BatchQC_2.3.0.tar.gz vignettes: vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQC_Intro.html vignetteTitles: BatchQC Examples, Introdution to BatchQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_examples.R, vignettes/BatchQC/inst/doc/BatchQC_Intro.R dependencyCount: 208 Package: BayesKnockdown Version: 1.33.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 Archs: x64 MD5sum: 2b10851e3e118c3688b7dd4c3dd422d6 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: devel git_last_commit: ee329ba git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BayesKnockdown_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BayesKnockdown_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BayesKnockdown_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BayesKnockdown_1.33.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 7 Package: BayesSpace Version: 1.17.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, methods, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, magrittr, assertthat, arrow, mclust, RCurl, DirichletReg, xgboost, utils, dplyr, rlang, ggplot2, tibble, rjson, tidyr, scales, microbenchmark, BiocFileCache, BiocSingular, BiocParallel LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE MD5sum: 88f1b1cbc590dca239a4e62a56c017df NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Senbai Kang [aut], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Matt Stone URL: edward130603.github.io/BayesSpace SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: devel git_last_commit: 30b68f9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BayesSpace_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BayesSpace_1.17.0.zip vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R importsMe: RegionalST dependencyCount: 156 Package: bayNorm Version: 1.25.0 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) Archs: x64 MD5sum: 23021fdb29ca4125b43ef68365859b66 NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: devel git_last_commit: 43039ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bayNorm_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bayNorm_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bayNorm_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bayNorm_1.25.0.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 62 Package: baySeq Version: 2.41.0 Depends: R (>= 2.3.0), methods Imports: edgeR, GenomicRanges, abind, parallel, graphics, stats, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 Archs: x64 MD5sum: a96b38865d70d03e6168939f728da2d9 NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, Bayesian, Coverage Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/baySeq BugReports: https://github.com/samgg/baySeq/issues git_url: https://git.bioconductor.org/packages/baySeq git_branch: devel git_last_commit: cf527bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/baySeq_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/baySeq_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/baySeq_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/baySeq_2.41.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, segmentSeq importsMe: riboSeqR dependencyCount: 33 Package: BBCAnalyzer Version: 1.37.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 9779167737fbabaa62a3f5b0bb2fa146 NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: devel git_last_commit: 3cc65d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BBCAnalyzer_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BBCAnalyzer_1.37.0.zip vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 80 Package: BCRANK Version: 1.69.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 MD5sum: 11000adaf73797a7e630b4d625555877 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: devel git_last_commit: 537aaff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BCRANK_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BCRANK_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BCRANK_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BCRANK_1.69.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 26 Package: bcSeq Version: 1.29.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) MD5sum: dcf650b96ba37c187a19acb2243ba4a5 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: devel git_last_commit: 5cae63a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bcSeq_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bcSeq_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bcSeq_1.29.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 30 Package: beachmat Version: 2.23.5 Imports: methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp, assorthead Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array, beachmat.hdf5 License: GPL-3 Archs: x64 MD5sum: 6f2f32f91477432dcb2bf3acf81a9aa2 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, along with a subset of the delayed operations implemented in the DelayedArray package. All other matrix-like objects are supported by calling back into R. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun URL: https://github.com/tatami-inc/beachmat SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/tatami-inc/beachmat/issues git_url: https://git.bioconductor.org/packages/beachmat git_branch: devel git_last_commit: 747ddb1 git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/beachmat_2.23.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/beachmat_2.23.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beachmat_2.23.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beachmat_2.23.5.tgz vignettes: vignettes/beachmat/inst/doc/linking.html vignetteTitles: Developer guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/linking.R importsMe: batchelor, beachmat.hdf5, BiocSingular, bsseq, DropletUtils, epiregulon, glmGamPoi, mumosa, scater, scran, scrapper, scuttle, SingleR suggestsMe: mbkmeans, PCAtools, scCB2 linksToMe: beachmat.hdf5, BiocSingular, bsseq, dreamlet, DropletUtils, epiregulon, glmGamPoi, mbkmeans, PCAtools, scran, scrapper, scuttle, SingleR dependencyCount: 25 Package: beachmat.hdf5 Version: 1.5.1 Imports: methods, beachmat, HDF5Array, DelayedArray, Rcpp LinkingTo: Rcpp, assorthead, beachmat, Rhdf5lib Suggests: testthat, BiocStyle, knitr, rmarkdown, rhdf5, Matrix License: GPL-3 Archs: x64 MD5sum: a841100d53c8f80932640d341fc76dff NeedsCompilation: yes Title: beachmat bindings for HDF5-backed matrices Description: Extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat.hdf5 git_branch: devel git_last_commit: cd7e813 git_last_commit_date: 2024-11-08 Date/Publication: 2024-11-08 source.ver: src/contrib/beachmat.hdf5_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/beachmat.hdf5_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beachmat.hdf5_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beachmat.hdf5_1.5.1.tgz vignettes: vignettes/beachmat.hdf5/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat.hdf5/inst/doc/userguide.R suggestsMe: beachmat, epiregulon, sketchR dependencyCount: 30 Package: beadarray Version: 2.57.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: x64 MD5sum: 02956ab2acc1faa0badbca5bd8551e59 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: devel git_last_commit: 21fcf33 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/beadarray_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/beadarray_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beadarray_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beadarray_2.57.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.html, vignettes/beadarray/inst/doc/beadlevel.html, vignettes/beadarray/inst/doc/beadsummary.html, vignettes/beadarray/inst/doc/ImageProcessing.html vignetteTitles: Introduction to beadarray, Analysis of Bead-level Data using beadarray, Analysis of bead-summary data, Image Analysis with beadarray hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R dependsOnMe: beadarrayExampleData importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: lumi, blimaTestingData, maGUI dependencyCount: 81 Package: BeadDataPackR Version: 1.59.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: x64 MD5sum: 5a8b747f2f49e7de530875c8317317a0 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: devel git_last_commit: c04cfa9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BeadDataPackR_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BeadDataPackR_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BeadDataPackR_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BeadDataPackR_1.59.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEAT Version: 1.45.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) Archs: x64 MD5sum: 2f497cd941fbe43419b072852d902603 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: devel git_last_commit: 72f59ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BEAT_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BEAT_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BEAT_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BEAT_1.45.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 72 Package: BEclear Version: 2.23.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, abind, stats, graphics, utils, methods, dixonTest, ids LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave License: GPL-3 MD5sum: 5849131ee57651736825fcdda68ef89e NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: Livia Rasp [aut, cre] (ORCID: ), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: Livia Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: devel git_last_commit: 4a3e2f4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BEclear_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BEclear_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BEclear_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BEclear_2.23.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 30 Package: beer Version: 1.11.0 Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags Imports: cli, edgeR, BiocParallel, methods, progressr, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, covr, codetools, knitr, rmarkdown, dplyr, ggplot2, spelling License: MIT + file LICENSE Archs: x64 MD5sum: 264840163396abbe89cc55d4dc26e671 NeedsCompilation: no Title: Bayesian Enrichment Estimation in R Description: BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses. biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen URL: https://github.com/athchen/beer/ SystemRequirements: JAGS (4.3.0) VignetteBuilder: knitr BugReports: https://github.com/athchen/beer/issues git_url: https://git.bioconductor.org/packages/beer git_branch: devel git_last_commit: a025d0b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/beer_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/beer_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/beer_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/beer_1.11.0.tgz vignettes: vignettes/beer/inst/doc/beer.html vignetteTitles: beer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beer/inst/doc/beer.R dependencyCount: 86 Package: BERT Version: 1.3.3 Depends: R (>= 4.3.0) Imports: cluster, comprehenr, foreach (>= 1.5.2), invgamma, iterators (>= 1.0.14), janitor (>= 2.2.0), limma (>= 3.46.0), logging (>= 0.10-108), sva (>= 3.38.0), SummarizedExperiment, methods, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: ac7d88402ecfeef96b497ec5397725fb NeedsCompilation: no Title: High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT) Description: Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees. biocViews: BatchEffect, Preprocessing, ExperimentalDesign, QualityControl Author: Yannis Schumann [aut, cre] (ORCID: ), Simon Schlumbohm [aut] (ORCID: ) Maintainer: Yannis Schumann URL: https://github.com/HSU-HPC/BERT/ VignetteBuilder: knitr BugReports: https://github.com/HSU-HPC/BERT/issues git_url: https://git.bioconductor.org/packages/BERT git_branch: devel git_last_commit: 10fd252 git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-20 source.ver: src/contrib/BERT_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/BERT_1.3.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BERT_1.3.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BERT_1.3.3.tgz vignettes: vignettes/BERT/inst/doc/BERT-Vignette.html vignetteTitles: BERT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BERT/inst/doc/BERT-Vignette.R dependencyCount: 102 Package: betaHMM Version: 1.3.1 Depends: R (>= 4.3.0), SummarizedExperiment, S4Vectors, GenomicRanges Imports: stats, ggplot2, scales, methods, pROC, foreach, doParallel, parallel, cowplot, dplyr, tidyr, tidyselect, stringr, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 24f94326460ff3888ef708cc385be7e0 NeedsCompilation: no Title: A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data Description: A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites. biocViews: DNAMethylation, DifferentialMethylation, ImmunoOncology, BiomedicalInformatics, MethylationArray, Software, MultipleComparison, Sequencing, Spatial, Coverage, GeneTarget, HiddenMarkovModel, Microarray Author: Koyel Majumdar [cre, aut] (ORCID: ), Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Isobel Claire Gorley [aut] (ORCID: ), Thomas Brendan Murphy [aut] (ORCID: ), Florence Jaffrezic [aut], Andrea Rau [aut] (ORCID: ) Maintainer: Koyel Majumdar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/betaHMM git_branch: devel git_last_commit: 83d4444 git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/betaHMM_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/betaHMM_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/betaHMM_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/betaHMM_1.3.1.tgz vignettes: vignettes/betaHMM/inst/doc/betaHMM.html vignetteTitles: betaHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betaHMM/inst/doc/betaHMM.R dependencyCount: 79 Package: bettr Version: 1.3.0 Depends: R (>= 4.4.0) Imports: dplyr (>= 1.0), tidyr, ggplot2 (>= 3.4.1), shiny (>= 1.6), tibble, ComplexHeatmap, bslib, rlang, circlize, stats, grid, methods, cowplot, Hmisc, sortable, shinyjqui, grDevices, scales, DT, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: 5d351b8e5102f9b105538147bbcf5a6b NeedsCompilation: no Title: A Better Way To Explore What Is Best Description: bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations. biocViews: Visualization, ShinyApps, GUI Author: Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/federicomarini/bettr VignetteBuilder: knitr BugReports: https://github.com/federicomarini/bettr/issues git_url: https://git.bioconductor.org/packages/bettr git_branch: devel git_last_commit: 717bbe5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bettr_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bettr_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bettr_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bettr_1.3.0.tgz vignettes: vignettes/bettr/inst/doc/bettr.html vignetteTitles: bettr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bettr/inst/doc/bettr.R dependencyCount: 136 Package: BG2 Version: 1.7.0 Depends: R (>= 4.2.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), MASS (>= 7.3-58.1), stats (>= 4.2.2) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: d06334428c613f24a3a048a96ed80a4a NeedsCompilation: no Title: Performs Bayesian GWAS analysis for non-Gaussian data using BG2 Description: This package is built to perform GWAS analysis for non-Gaussian data using BG2. The BG2 method uses penalized quasi-likelihood along with nonlocal priors in a two step manner to identify SNPs in GWAS analysis. The research related to this package was supported in part by National Science Foundation awards DMS 1853549 and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Shuangshuang Xu [aut], Marco Ferreira [aut] (ORCID: ) Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BG2 git_branch: devel git_last_commit: edfff8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BG2_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BG2_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BG2_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BG2_1.7.0.tgz vignettes: vignettes/BG2/inst/doc/BG2.html vignetteTitles: BG2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BG2/inst/doc/BG2.R dependencyCount: 91 Package: BgeeCall Version: 1.23.0 Depends: R (>= 3.6) Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5, txdbmaker Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 + file LICENSE MD5sum: 8da90e529bd64c17c33576be7dc45561 NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: devel git_last_commit: 6e9ba9e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BgeeCall_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BgeeCall_1.23.0.zip vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 113 Package: BgeeDB Version: 2.33.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase, zellkonverter, anndata, HDF5Array, bread Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 7f1c95bcf8dc4719a6a047de1f46dc3f NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: devel git_last_commit: 13c7c42 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BgeeDB_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BgeeDB_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BgeeDB_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BgeeDB_2.33.0.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: BgeeDB,, an R package for retrieval of curated expression datasets and for gene list enrichment tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: RITAN suggestsMe: RITAN dependencyCount: 100 Package: BicARE Version: 1.65.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase, GO.db Imports: methods Suggests: hgu95av2 License: GPL-2 MD5sum: eac5a310d8a0fc33b9987f2a3dc5dc8d NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration. biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: devel git_last_commit: 70d5604 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BicARE_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BicARE_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BicARE_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BicARE_1.65.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R dependsOnMe: RcmdrPlugin.BiclustGUI importsMe: miRSM dependencyCount: 59 Package: BiFET Version: 1.27.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 Archs: x64 MD5sum: c3dd5495961c83e465ef09090940abe1 NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: devel git_last_commit: 68a0c10 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiFET_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiFET_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiFET_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiFET_1.27.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 25 Package: BiGGR Version: 1.43.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE Archs: x64 MD5sum: eda2ca1b253d1ebdf99aa8220d34cf0f NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: devel git_last_commit: bac514a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiGGR_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiGGR_1.43.0.zip vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R dependencyCount: 30 Package: bigmelon Version: 1.33.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: 34b6cebd14e45318d7eb5b1c8e4e07b0 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [cre, aut] Maintainer: Leonard C. Schalkwyk git_url: https://git.bioconductor.org/packages/bigmelon git_branch: devel git_last_commit: 9e460f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bigmelon_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bigmelon_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bigmelon_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bigmelon_1.33.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 174 Package: BindingSiteFinder Version: 2.5.2 Depends: GenomicRanges, R (>= 4.2) Imports: tidyr, tibble, plyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce, GenomeInfoDb, ComplexHeatmap, RColorBrewer, lifecycle, rlang, forcats, dplyr, GenomicFeatures, IRanges, kableExtra, ggdist Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicAlignments, scales, Gviz, xlsx, GGally, patchwork, viridis, ggplotify, SummarizedExperiment, DESeq2, ggpointdensity, ggrastr, ashr, txdbmaker License: Artistic-2.0 MD5sum: 5f1acb0d3d4abbf967450b19e5b7d5ef NeedsCompilation: no Title: Binding site defintion based on iCLIP data Description: Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette. biocViews: Sequencing, GeneExpression, GeneRegulation, FunctionalGenomics, Coverage, DataImport Author: Mirko Brüggemann [aut, cre] (ORCID: ), Melina Klostermann [aut] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: devel git_last_commit: d7f7545 git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-21 source.ver: src/contrib/BindingSiteFinder_2.5.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/BindingSiteFinder_2.5.2.zip vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html vignetteTitles: Definition of binding sites from iCLIP signal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R dependencyCount: 145 Package: bioassayR Version: 1.45.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 3f731a6c089f39ca23d0e77312b07cc8 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: devel git_last_commit: 248466e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bioassayR_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bioassayR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioassayR_1.45.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 84 Package: Biobase Version: 2.67.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr, limma License: Artistic-2.0 MD5sum: bb1f78e974248f618f28dfbc26539935 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman [aut], V. Carey [aut], M. Morgan [aut], S. Falcon [aut], Haleema Khan [ctb] ('esApply' and 'BiobaseDevelopment' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: devel git_last_commit: 06c9ac2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Biobase_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Biobase_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Biobase_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Biobase_2.67.0.tgz vignettes: vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf, vignettes/Biobase/inst/doc/BiobaseDevelopment.html, vignettes/Biobase/inst/doc/esApply.html vignetteTitles: An introduction to Biobase and ExpressionSets, Notes for eSet developers, esApply Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle, beadarray, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, CytoMDS, DFP, diggit, doppelgangR, DSS, dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GEOexplorer, GeomxTools, GEOquery, 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PCAPAM50, PerseusR, RCPA, RobLox, RobLoxBioC, RPPanalyzer, SCdeconR, seAMLess, ssizeRNA, TailRank suggestsMe: AUCell, autonomics, BiocGenerics, CellMapper, clustComp, coseq, cypress, DART, dcanr, dearseq, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, genefu, GENIE3, GenomicPlot, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina, MOSim, msa, multiClust, OSAT, PCAtools, RcisTarget, ribosomeProfilingQC, ROC, RTCGA, scater, scmeth, SeqArray, sparrow, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, tidytof, tkWidgets, TOP, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, dorothea, dyebiasexamples, HMP16SData, homosapienDEE2CellScore, mammaPrintData, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid, CrossValidate, distrDoc, GenAlgo, hexbin, HTSCluster, isatabr, mi4p, MOCHA, Modeler, multiclassPairs, NACHO, ordinalbayes, Patterns, rsconnect, seeker, Seurat, sigminer, SomaDataIO, tinyarray dependencyCount: 6 Package: biobroom Version: 1.39.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, rmarkdown, SummarizedExperiment License: LGPL MD5sum: 718605b97a56dec6715663add12cb227 NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: devel git_last_commit: 360bc04 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biobroom_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biobroom_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biobroom_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biobroom_1.39.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 31 Package: biobtreeR Version: 1.19.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE Archs: x64 MD5sum: 25b9527aa216a5e828aae6799b75bbde NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: devel git_last_commit: 3685204 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biobtreeR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biobtreeR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biobtreeR_1.19.0.tgz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 20 Package: bioCancer Version: 1.35.0 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cBioPortalData, XML(>= 3.98) Imports: R.oo, R.methodsS3, DT (>= 0.3), dplyr (>= 0.7.2), tidyr, shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, AnnotationDbi, shinythemes, Biobase, geNetClassifier, org.Hs.eg.db, org.Bt.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble, GO.db Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: 43e9a8e6f696a8f0790a34dbf65373e5 NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: https://kmezhoud.github.io/bioCancer/ VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: devel git_last_commit: 94ee9bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bioCancer_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bioCancer_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioCancer_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bioCancer_1.35.0.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 254 Package: BioCartaImage Version: 1.5.0 Depends: R (>= 4.3.0) Imports: magick, grid, stats, grDevices, utils Suggests: testthat, knitr, BiocStyle, ragg License: MIT + file LICENSE MD5sum: 2391621ba4351ffa2c100c084b2b60d4 NeedsCompilation: no Title: BioCarta Pathway Images Description: The core functionality of the package is to provide coordinates of genes on the BioCarta pathway images and to provide methods to add self-defined graphics to the genes of interest. biocViews: Software, Pathways, BioCarta, Visualization Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/BioCartaImage VignetteBuilder: knitr BugReports: https://github.com/jokergoo/BioCartaImage/issues git_url: https://git.bioconductor.org/packages/BioCartaImage git_branch: devel git_last_commit: 46b0932 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioCartaImage_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioCartaImage_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioCartaImage_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioCartaImage_1.5.0.tgz vignettes: vignettes/BioCartaImage/inst/doc/BioCartaImage.html vignetteTitles: Customize BioCarta Pathway Images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCartaImage/inst/doc/BioCartaImage.R dependencyCount: 9 Package: BiocBaseUtils Version: 1.9.0 Depends: R (>= 4.2.0) Imports: methods, utils Suggests: knitr, rmarkdown, BiocStyle, tinytest License: Artistic-2.0 MD5sum: dac17bda4b452bb7e95cd3ecb2c90aa3 NeedsCompilation: no Title: General utility functions for developing Bioconductor packages Description: The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues git_url: https://git.bioconductor.org/packages/BiocBaseUtils git_branch: devel git_last_commit: d643df4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocBaseUtils_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocBaseUtils_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocBaseUtils_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocBaseUtils_1.9.0.tgz vignettes: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.html vignetteTitles: BiocBaseUtils Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.R importsMe: AlphaMissenseR, AnVIL, AnVILAz, AnVILGCP, AnVILPublish, BiocCheck, BiocFHIR, cBioPortalData, DNAfusion, iSEEfier, MultiAssayExperiment, RaggedExperiment, TCGAutils, TENxIO, UniProt.ws, VisiumIO, visiumStitched, SingleCellMultiModal suggestsMe: scifer dependencyCount: 2 Package: BiocBook Version: 1.5.0 Depends: R (>= 4.3) Imports: BiocGenerics, available, cli, glue, gert, gh, gitcreds, httr, usethis, dplyr, purrr, tibble, methods, rprojroot, stringr, yaml, tools, utils, rlang, quarto, renv Suggests: BiocStyle, knitr, testthat (>= 3.0.0), rmarkdown License: MIT + file LICENSE MD5sum: cf02774cbbb7cf39e16679ea96bff0c0 NeedsCompilation: no Title: Write, containerize, publish and version Quarto books with Bioconductor Description: A BiocBook can be created by authors (e.g. R developers, but also scientists, teachers, communicators, ...) who wish to 1) write (compile a body of biological and/or bioinformatics knowledge), 2) containerize (provide Docker images to reproduce the examples illustrated in the compendium), 3) publish (deploy an online book to disseminate the compendium), and 4) version (automatically generate specific online book versions and Docker images for specific Bioconductor releases). biocViews: Infrastructure, ReportWriting, Software Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://bioconductor.org/packages/BiocBook VignetteBuilder: knitr BugReports: https://github.com/js2264/BiocBook/issues git_url: https://git.bioconductor.org/packages/BiocBook git_branch: devel git_last_commit: 0148dc3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-20 source.ver: src/contrib/BiocBook_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocBook_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocBook_1.5.0.tgz vignettes: vignettes/BiocBook/inst/doc/BiocBook.html vignetteTitles: BiocBook hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBook/inst/doc/BiocBook.R Package: BiocCheck Version: 1.43.2 Depends: R (>= 4.4.0) Imports: BiocBaseUtils, BiocFileCache, BiocManager, biocViews, callr, cli, codetools, graph, httr2, knitr, methods, rvest, stringdist, tools, utils Suggests: BiocStyle, devtools, gert, jsonlite, rmarkdown, tinytest, usethis License: Artistic-2.0 MD5sum: 4f68ad984c7f55994fdc05517956b3ff NeedsCompilation: no Title: Bioconductor-specific package checks Description: BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocCheck VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocCheck/issues git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: devel git_last_commit: 1aed7d0 git_last_commit_date: 2024-11-08 Date/Publication: 2024-11-08 source.ver: src/contrib/BiocCheck_1.43.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocCheck_1.43.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocCheck_1.43.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocCheck_1.43.2.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: AnnotationHubData, gDRstyle suggestsMe: GEOfastq, packFinder, preciseTAD, SpectralTAD, HMP16SData, scpdata, MainExistingDatasets dependencyCount: 75 Package: BiocFHIR Version: 1.9.0 Depends: R (>= 4.2) Imports: DT, shiny, jsonlite, graph, tidyr, visNetwork, dplyr, utils, methods, BiocBaseUtils Suggests: knitr, testthat, rjsoncons, igraph, BiocStyle License: Artistic-2.0 MD5sum: b1f1d73fbbd0d77985664635c8b78cf1 NeedsCompilation: no Title: Illustration of FHIR ingestion and transformation using R Description: FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations. biocViews: Infrastructure, DataImport, DataRepresentation Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocFHIR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocFHIR/issues git_url: https://git.bioconductor.org/packages/BiocFHIR git_branch: devel git_last_commit: 9b190a5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocFHIR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocFHIR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocFHIR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocFHIR_1.9.0.tgz vignettes: vignettes/BiocFHIR/inst/doc/A_upper.html, vignettes/BiocFHIR/inst/doc/B_handling.html, vignettes/BiocFHIR/inst/doc/BiocFHIR.html, vignettes/BiocFHIR/inst/doc/C_tables.html, vignettes/BiocFHIR/inst/doc/D_linking.html vignetteTitles: Upper level FHIR concepts, Handling FHIR documents with BiocFHIR, BiocFHIR -- infrastructure for parsing and analyzing FHIR data, Transforming FHIR documents to tables with BiocFHIR, Linking information between FHIR resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFHIR/inst/doc/A_upper.R, vignettes/BiocFHIR/inst/doc/B_handling.R, vignettes/BiocFHIR/inst/doc/BiocFHIR.R, vignettes/BiocFHIR/inst/doc/C_tables.R, vignettes/BiocFHIR/inst/doc/D_linking.R dependencyCount: 66 Package: BiocFileCache Version: 2.15.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: 2acb42ad120f0ad99b8ddff455e7f958 NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: devel git_last_commit: 2aefe72 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocFileCache_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocFileCache_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocFileCache_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocFileCache_2.15.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.html, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.html, vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: 3. BiocFileCache Troubleshooting, 2. BiocFileCache: Use Cases, 1. BiocFileCache Overview: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.R, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.R, vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, easylift, ExperimentHub, RcwlPipelines, JASPAR2022, JASPAR2024, scATAC.Explorer, TMExplorer, csawBook, OSCA.intro, OSCA.workflows importsMe: AlphaMissenseR, AMARETTO, atSNP, autonomics, BayesSpace, BiocCheck, BiocHail, BiocPkgTools, biodb, biomaRt, brendaDb, bugsigdbr, cbaf, cBioPortalData, CBNplot, CellBench, CTDquerier, customCMPdb, CytoPipeline, easyRNASeq, enhancerHomologSearch, EnMCB, EnrichmentBrowser, EpiTxDb, fenr, fgga, GeDi, GenomicScores, GenomicSuperSignature, ggkegg, GSEABenchmarkeR, gwascat, hca, iSEEindex, MBQN, MIRit, motifbreakR, MotifPeeker, MsBackendMetaboLights, OmicsMLRepoR, ontoProc, ORFik, Organism.dplyr, PhIPData, psichomics, rBLAST, recount3, recountmethylation, regutools, ReUseData, RiboDiPA, rpx, scviR, sesame, signeR, SpatialExperiment, SpatialOmicsOverlay, SpliceWiz, tenXplore, terraTCGAdata, TFutils, tomoseqr, tximeta, UMI4Cats, UniProt.ws, waddR, xenLite, geneplast.data, HPO.db, MPO.db, org.Mxanthus.db, PANTHER.db, BioPlex, bugphyzz, depmap, DNAZooData, fourDNData, HiContactsData, MicrobiomeBenchmarkData, NxtIRFdata, orthosData, SFEData, SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations, convertid suggestsMe: AnnotationForge, bambu, BiocSet, ChIPpeakAnno, CoGAPS, dominoSignal, EpiCompare, fastreeR, FLAMES, GRaNIE, HicAggR, HiCDCPlus, HiCExperiment, HiCool, MethReg, MetMashR, Nebulosa, nipalsMCIA, progeny, qsvaR, seqsetvis, spatialHeatmap, structToolbox, TREG, visiumStitched, zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData, msigdb, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, scCustomize dependencyCount: 45 Package: BiocGenerics Version: 0.53.3 Depends: R (>= 4.0.0), methods, utils, graphics, stats, generics Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, S4Arrays, SparseArray, DelayedArray, HDF5Array, GenomicRanges, pwalign, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 3d35210a258114d23c74084c4401b47c NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines many S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: devel git_last_commit: 68e6aa4 git_last_commit_date: 2024-11-14 Date/Publication: 2024-11-14 source.ver: src/contrib/BiocGenerics_0.53.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocGenerics_0.53.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocGenerics_0.53.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocGenerics_0.53.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, BSgenomeForge, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, clusterExperiment, codelink, consensusDE, consensusSeekeR, CoreGx, CRISPRseek, cummeRbund, DelayedArray, ensembldb, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, ggbio, girafe, graph, GSEABase, GUIDEseq, HelloRanges, interactiveDisplay, interactiveDisplayBase, IRanges, ISLET, lute, MBASED, MGnifyR, MineICA, minfi, MLInterfaces, MotifDb, mpra, MSnbase, 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ChemmineR, chipenrich, ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, chromVAR, cicero, CircSeqAlignTk, CleanUpRNAseq, clusterSeq, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, cola, compEpiTools, CompoundDb, concordexR, crisprBase, crisprBowtie, crisprBwa, crisprDesign, crisprScore, crisprShiny, crisprViz, crlmm, csaw, CTexploreR, cummeRbund, CuratedAtlasQueryR, cydar, dada2, dagLogo, DAMEfinder, ddCt, decompTumor2Sig, deconvR, DegCre, DEGreport, DelayedDataFrame, demuxSNP, derfinder, DEScan2, DESeq2, DESpace, destiny, DEWSeq, DFplyr, diffcoexp, diffHic, dinoR, DirichletMultinomial, DiscoRhythm, DNAfusion, dreamlet, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisaR, enhancerHomologSearch, EnrichDO, epialleleR, EpiCompare, epigenomix, epimutacions, epistack, EpiTxDb, epivizrChart, epivizrStandalone, erma, esATAC, factR, FamAgg, fastseg, ffpe, FindIT2, FLAMES, flowBin, flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats, flowWorkspace, fmcsR, FRASER, 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KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, raerdata, scRNAseq, spatialLIBD, systemPipeRdata, TENxBUSData, VariantToolsData, ExpHunterSuite, GeoMxWorkflows, crispRdesignR, DCLEAR, decompDL, EEMDlstm, geno2proteo, kmeRs, locuszoomr, MicroSEC, MOCHA, oncoPredict, pathwayTMB, RNAseqQC, RobLoxBioC, SCRIP, Signac, spectralAnalysis, TaxaNorm, toxpiR, treediff, TSdeeplearning suggestsMe: acde, adverSCarial, aggregateBioVar, AIMS, AlphaMissenseR, ASSET, ASURAT, BaalChIP, baySeq, bigmelon, BiocParallel, BiocStyle, biocViews, biosigner, BiRewire, BLMA, BloodGen3Module, bnem, borealis, BUScorrect, BUSseq, CAFE, CAMERA, CausalR, ccrepe, CDI, cellmigRation, CexoR, chihaya, ChIPanalyser, ChIPXpress, CHRONOS, CINdex, cleanUpdTSeq, clipper, ClustAll, clustComp, CNORfeeder, CNORfuzzy, consensus, cosmiq, COSNet, cpvSNP, cypress, DEsubs, DExMA, DMRcaller, DMRcate, EnhancedVolcano, ENmix, EpiMix, epiNEM, EventPointer, fCCAC, fcScan, fgga, 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rgsepd, riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sarks, SCArray, scDataviz, scmeth, scp, screenCounter, scry, segmentSeq, SeqArray, seqPattern, seqTools, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, singleCellTK, SingleR, slingshot, SNPRelate, SparseSignatures, spatialHeatmap, specL, STATegRa, STRINGdb, SUITOR, systemPipeTools, TCC, TFEA.ChIP, tidytof, TIN, transcriptogramer, traseR, TreeAndLeaf, tripr, tRNAdbImport, TRONCO, Uniquorn, variancePartition, VERSO, xcore, zenith, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, healthyControlsPresenceChecker, microRNAome, RegParallel, scMultiome, sesameData, xcoredata, adjclust, aroma.affymetrix, asteRisk, gkmSVM, GSEMA, MarZIC, NutrienTrackeR, openSkies, pagoda2, Platypus, polyRAD, Rediscover, Seurat dependencyCount: 5 Package: biocGraph Version: 1.69.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: a574b9d9496b25fbe3b7e824762b3cb0 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: devel git_last_commit: f12102d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biocGraph_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocGraph_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocGraph_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocGraph_1.69.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: EnrichmentBrowser dependencyCount: 55 Package: BiocHail Version: 1.7.1 Depends: R (>= 4.3.0), graphics, stats, utils Imports: reticulate, basilisk, BiocFileCache, methods, dplyr, BiocGenerics Suggests: knitr, testthat, BiocStyle, ggplot2, DT License: Artistic-2.0 MD5sum: e87088a34de49f92f991cd95c85d7b23 NeedsCompilation: no Title: basilisk and hail Description: Use hail via basilisk when appropriate, or via reticulate. This package can be used in terra.bio to interact with UK Biobank resources processed by hail.is. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocHail VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocHail/issues git_url: https://git.bioconductor.org/packages/BiocHail git_branch: devel git_last_commit: 3bfea67 git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-20 source.ver: src/contrib/BiocHail_1.7.1.tar.gz vignettes: vignettes/BiocHail/inst/doc/gwas_tut.html, vignettes/BiocHail/inst/doc/large_t2t.html, vignettes/BiocHail/inst/doc/ukbb.html vignetteTitles: 01 BiocHail -- GWAS tutorial, 02 Working with larger VCF: T2T by chromosome, 03 Working with UK Biobank summary statistics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocHail/inst/doc/gwas_tut.R, vignettes/BiocHail/inst/doc/large_t2t.R, vignettes/BiocHail/inst/doc/ukbb.R dependencyCount: 61 Package: BiocHubsShiny Version: 1.7.2 Depends: R (>= 4.3.0), shiny Imports: AnnotationHub, ExperimentHub, DT, htmlwidgets, rclipboard, S4Vectors, shinyAce, shinyjs, shinythemes, shinytoastr, utils Suggests: BiocManager, BiocStyle, knitr, rmarkdown, sessioninfo, shinytest2 License: Artistic-2.0 MD5sum: c1d1b99300afb350c08a0c01b41310a8 NeedsCompilation: no Title: View AnnotationHub and ExperimentHub Resources Interactively Description: A package that allows interactive exploration of AnnotationHub and ExperimentHub resources. It uses DT / DataTable to display resources for multiple organisms. It provides template code for reproducibility and for downloading resources via the indicated Hub package. biocViews: Software, ShinyApps Author: Marcel Ramos [aut, cre] (ORCID: ), Vincent Carey [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocHubsShiny VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocHubsShiny/issues git_url: https://git.bioconductor.org/packages/BiocHubsShiny git_branch: devel git_last_commit: ac10951 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-19 source.ver: src/contrib/BiocHubsShiny_1.7.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocHubsShiny_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocHubsShiny_1.7.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocHubsShiny_1.7.2.tgz vignettes: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.html vignetteTitles: BiocHubsShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.R dependencyCount: 98 Package: BiocIO Version: 1.17.1 Depends: R (>= 4.3.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: a25edfe5502c5521120d50323558fbe3 NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: The `BiocIO` package contains high-level abstract classes and generics used by developers to build IO funcionality within the Bioconductor suite of packages. Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Marcel Ramos [cre] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: devel git_last_commit: a2a0962 git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-21 source.ver: src/contrib/BiocIO_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocIO_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocIO_1.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocIO_1.17.1.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: BSgenome, HelloRanges, LoomExperiment importsMe: BiocSet, BSgenomeForge, extraChIPs, HiCExperiment, HiContacts, HiCool, rtracklayer, TENxIO, tidyCoverage, txdbmaker, VisiumIO dependencyCount: 9 Package: BiocNeighbors Version: 2.1.2 Imports: Rcpp, methods LinkingTo: Rcpp, assorthead Suggests: BiocParallel, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 41a7487d405d98330c59e13fd21c1f25 NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: devel git_last_commit: 4407310 git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-09 source.ver: src/contrib/BiocNeighbors_2.1.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocNeighbors_2.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocNeighbors_2.1.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocNeighbors_2.1.2.tgz vignettes: vignettes/BiocNeighbors/inst/doc/userguide.html vignetteTitles: Finding neighbors in high-dimensional space hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/userguide.R dependsOnMe: OSCA.workflows, SingleRBook importsMe: batchelor, bluster, CellMixS, concordexR, cydar, GeDi, imcRtools, lemur, miloR, mumosa, scater, scDblFinder, scMerge, scrapper, SingleR, SpatialFeatureExperiment, SpotSweeper, StabMap, UCell suggestsMe: TrajectoryUtils, TSCAN linksToMe: scrapper, SingleR dependencyCount: 4 Package: BioCor Version: 1.31.0 Depends: R (>= 3.4.0) Imports: BiocParallel, GSEABase, Matrix, methods Suggests: airway, BiocStyle, boot, DESeq2, ggplot2 (>= 3.4.1), GOSemSim, Hmisc, knitr (>= 1.35), org.Hs.eg.db, reactome.db, rmarkdown, spelling, targetscan.Hs.eg.db, testthat (>= 3.0.0), WGCNA License: MIT + file LICENSE MD5sum: c993714b686a5c2ecca3bf1eb7654573 NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (ORCID: ), Pau Sancho-Bru [ths] (ORCID: ), Juan José Salvatella Lozano [ths] (ORCID: ) Maintainer: Lluís Revilla Sancho URL: https://bioconductor.org/packages/BioCor, https://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: devel git_last_commit: 59fbed3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioCor_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioCor_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioCor_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioCor_1.31.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 63 Package: BiocParallel Version: 1.41.0 Depends: methods, R (>= 3.5.0) Imports: stats, utils, futile.logger, parallel, snow, codetools LinkingTo: BH, cpp11 Suggests: BiocGenerics, tools, foreach, BBmisc, doParallel, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table Enhances: Rmpi License: GPL-2 | GPL-3 Archs: x64 MD5sum: 9242165148fc3b13775ac18becde63bd NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Martin Morgan [aut, cre], Jiefei Wang [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb], Madelyn Carlson [ctb] (Translated 'Random Numbers' vignette from Sweave to RMarkdown / HTML.), Phylis Atieno [ctb] (Translated 'Introduction to BiocParallel' vignette from Sweave to Rmarkdown / HTML.), Sergio Oller [ctb] (Improved bpmapply() efficiency., ORCID: ) Maintainer: Martin Morgan URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: devel git_last_commit: 0f688b6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocParallel_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocParallel_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocParallel_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocParallel_1.41.0.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.html, vignettes/BiocParallel/inst/doc/Random_Numbers.html vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel, 4. Random Numbers in BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R, vignettes/BiocParallel/inst/doc/Random_Numbers.R dependsOnMe: bacon, BEclear, Cardinal, CardinalIO, ChIPQC, ClassifyR, clusterSeq, consensusSeekeR, DEWSeq, DMCFB, DMCHMM, doppelgangR, DSS, extraChIPs, FEAST, FRASER, GenomicFiles, hiReadsProcessor, INSPEcT, iPath, ISLET, matter, MBASED, metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, RedisParam, Rqc, ShortRead, Spectra, sva, variancePartition, xcms, sequencing, OSCA.workflows, SingleRBook importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR, amplican, ASICS, ATACseqQC, atena, atSNP, bambu, BANDITS, bandle, BASiCS, batchelor, BayesSpace, bayNorm, beer, BERT, BioCor, BiocSingular, BioNERO, biotmle, biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, ccImpute, CDI, cellbaseR, CellBench, CelliD, CellMixS, censcyt, Cepo, ChIPexoQual, ChromSCape, chromVAR, ClusterFoldSimilarity, CNVMetrics, CNVRanger, CoGAPS, comapr, coMethDMR, CompoundDb, concordexR, condiments, consensusDE, consensusOV, consICA, CoreGx, coseq, cpvSNP, CrispRVariants, csaw, CTSV, cydar, cypress, CytoGLMM, cytoKernel, cytomapper, CytoMDS, CytoPipeline, dcGSA, debCAM, decoupleR, DeepTarget, DegCre, DepInfeR, derfinder, DEScan2, DESeq2, DEsingle, DESpace, DiffBind, Dino, dmrseq, DOSE, dreamlet, DRIMSeq, DropletUtils, Dune, easier, easyRNASeq, EMDomics, enhancerHomologSearch, epimutacions, epiregulon, epistasisGA, erma, ERSSA, escape, EWCE, factR, faers, fgsea, findIPs, FindIT2, FLAMES, flowcatchR, flowSpecs, GDCRNATools, gDNAx, gDRcore, gDRutils, GeDi, GENESIS, GenomAutomorphism, GenomicAlignments, gINTomics, GloScope, gmapR, gscreend, GSEABenchmarkeR, GSVA, h5vc, HicAggR, HiCBricks, HiCcompare, HiCDOC, HiCExperiment, HiContacts, HTSFilter, HybridExpress, iasva, icetea, ideal, IMAS, imcRtools, IONiseR, IPO, katdetectr, KinSwingR, LimROTS, lisaClust, loci2path, LRcell, Macarron, magpie, magrene, mariner, mbkmeans, MCbiclust, MetaboAnnotation, MetaboCoreUtils, metabomxtr, metaseqR2, MethylAid, methylGSA, methyLImp2, methylInheritance, methylscaper, MetNet, mia, miaViz, microSTASIS, MICSQTL, miloR, minfi, MIRit, mixOmics, MOGAMUN, MoleculeExperiment, monaLisa, motifbreakR, MotifPeeker, MPAC, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MSnbase, msqrob2, MsQuality, multiHiCcompare, mumosa, muscat, NBAMSeq, nnSVG, NPARC, ORFik, orthos, OVESEG, PAIRADISE, PCAtools, PDATK, pengls, PharmacoGx, pipeComp, pram, proActiv, profileplyr, ProteoDisco, PSMatch, qpgraph, qsea, QuasR, RadioGx, raer, rawDiag, Rcwl, recount, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, RNAseqCovarImpute, Rsamtools, RUVcorr, satuRn, scanMiR, scanMiRApp, SCArray, SCArray.sat, scater, scBubbletree, scClassify, scDblFinder, scDD, scDDboost, scde, scDesign3, SCFA, scFeatures, scHOT, scMerge, scMultiSim, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, screenCounter, scruff, scShapes, scTHI, scuttle, sesame, SEtools, sigFeature, signatureSearch, simpleSeg, SingleCellAlleleExperiment, singleCellTK, SingleR, singscore, SNPhood, soGGi, sparrow, SpatialFeatureExperiment, SpectralTAD, spicyR, splatter, SpliceWiz, SplicingGraphs, spoon, SpotSweeper, StabMap, Statial, SUITOR, syntenet, TAPseq, TBSignatureProfiler, ternarynet, TFBSTools, tidyCoverage, TMixClust, ToxicoGx, TPP2D, tpSVG, tradeSeq, TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell, VariantFiltering, VariantTools, VDJdive, velociraptor, Voyager, waddR, weitrix, zinbwave, CytoMethIC, IHWpaper, JohnsonKinaseData, ExpHunterSuite, seqpac, DCLEAR, DTSEA, DysPIA, enviGCMS, GSEMA, Holomics, LDM, minSNPs, oosse, robin, scGate suggestsMe: alabaster.mae, beachmat, BiocNeighbors, cliqueMS, DelayedArray, EpiCompare, GenomicDataCommons, ggsc, glmGamPoi, GRaNIE, HDF5Array, ISAnalytics, MungeSumstats, netSmooth, omicsPrint, plyinteractions, PureCN, randRotation, RcisTarget, rebook, rhdf5, S4Arrays, scGPS, SeqArray, spatialHeatmap, survClust, TFutils, TileDBArray, TrajectoryUtils, TSCAN, universalmotif, xcore, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, clustermq, conos, pagoda2, phase1RMD, RaMS, SpatialDDLS, survBootOutliers, wrTopDownFrag dependencyCount: 12 Package: BiocPkgTools Version: 1.25.2 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, magrittr, methods, rlang, stringr, stats, rvest, dplyr, xml2, readr, httr, htmltools, DT, tools, utils, igraph, jsonlite, gh, RBGL, graph, rorcid Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, lubridate, networkD3, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 06f890a7e4dd021a66012b7604a69af8 NeedsCompilation: no Title: Collection of simple tools for learning about Bioconductor Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [aut, ctb] (ORCID: ), Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: devel git_last_commit: 19ff678 git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-03 source.ver: src/contrib/BiocPkgTools_1.25.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocPkgTools_1.25.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocPkgTools_1.25.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocPkgTools_1.25.2.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R suggestsMe: rworkflows dependencyCount: 109 Package: biocroxytest Version: 1.3.0 Depends: R (>= 4.4.0) Imports: cli, glue, roxygen2, stringr Suggests: BiocStyle, here, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) Archs: x64 MD5sum: 7ffcf9a9655ceec87ad93009348ffbed NeedsCompilation: no Title: Handle Long Tests in Bioconductor Packages Description: This package provides a roclet for roxygen2 that identifies and processes code blocks in your documentation marked with `@longtests`. These blocks should contain tests that take a long time to run and thus cannot be included in the regular test suite of the package. When you run `roxygen2::roxygenise` with the `longtests_roclet`, it will extract these long tests from your documentation and save them in a separate directory. This allows you to run these long tests separately from the rest of your tests, for example, on a continuous integration server that is set up to run long tests. biocViews: Software, Infrastructure Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/xec-cm/biocroxytest VignetteBuilder: knitr BugReports: https://github.com/xec-cm/biocroxytest/issues git_url: https://git.bioconductor.org/packages/biocroxytest git_branch: devel git_last_commit: d07605f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biocroxytest_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocroxytest_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocroxytest_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocroxytest_1.3.0.tgz vignettes: vignettes/biocroxytest/inst/doc/biocroxytest.html vignetteTitles: Introduction to biocroxytest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocroxytest/inst/doc/biocroxytest.R dependencyCount: 35 Package: BiocSet Version: 1.21.0 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 684c60b9923e24193278df574ae10c8d NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet. biocViews: GeneExpression, GO, KEGG, Software Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin Rue-Albrecht [ctb], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSet git_branch: devel git_last_commit: 263c319 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocSet_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocSet_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocSet_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocSet_1.21.0.tgz vignettes: vignettes/BiocSet/inst/doc/BiocSet.html vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R dependsOnMe: RegEnrich importsMe: sparrow suggestsMe: dearseq dependencyCount: 62 Package: BiocSingular Version: 1.23.0 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat (>= 2.21.1) LinkingTo: Rcpp, beachmat, assorthead Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix License: GPL-3 MD5sum: 73c5f4c0bb232ab7d2de94bff775cde3 NeedsCompilation: yes Title: Singular Value Decomposition for Bioconductor Packages Description: Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: devel git_last_commit: bd97d96 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocSingular_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocSingular_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocSingular_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocSingular_1.23.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R dependsOnMe: OSCA.workflows importsMe: batchelor, BayesSpace, clusterExperiment, COTAN, DelayedTensor, Dino, GSVA, miloR, MPAC, mumosa, NanoMethViz, NewWave, PCAtools, ReactomeGSA, SCArray, SCArray.sat, scater, scDblFinder, scMerge, scran, scry, StabMap, velociraptor suggestsMe: alabaster.matrix, chihaya, ResidualMatrix, ScaledMatrix, spatialHeatmap, splatter, Voyager, HCAData dependencyCount: 39 Package: BiocSklearn Version: 1.29.0 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment Imports: basilisk Suggests: testthat, HDF5Array, BiocStyle, rmarkdown, knitr License: Artistic-2.0 MD5sum: 9f3331d740d8444917b8d4068b164d24 NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey [cre, aut] Maintainer: Vince Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: devel git_last_commit: 8c69d92 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocSklearn_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocSklearn_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocSklearn_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocSklearn_1.29.0.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 51 Package: BiocStyle Version: 2.35.0 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 Archs: x64 MD5sum: 397df8a5405591881755daa79d907f7c NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś [aut] (ORCID: ), Mike Smith [ctb] (ORCID: ), Martin Morgan [ctb], Wolfgang Huber [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: devel git_last_commit: 40286b4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocStyle_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocStyle_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocStyle_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocStyle_2.35.0.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 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emtdata, eoPredData, epimutacionsData, ewceData, fourDNData, furrowSeg, gDNAinRNAseqData, gDRtestData, GenomicDistributionsData, GeuvadisTranscriptExpr, GSE103322, GSE13015, GSE159526, GSE62944, HarmanData, HCAData, HCATonsilData, HD2013SGI, HDCytoData, healthyControlsPresenceChecker, HelloRangesData, HiCDataHumanIMR90, HiContactsData, HighlyReplicatedRNASeq, HMP16SData, HumanAffyData, humanHippocampus2024, IHWpaper, imcdatasets, JohnsonKinaseData, LegATo, LRcellTypeMarkers, mCSEAdata, mcsurvdata, MerfishData, MetaGxOvarian, MetaGxPancreas, MethylAidData, methylclockData, MethylSeqData, MicrobiomeBenchmarkData, microbiomeDataSets, minionSummaryData, MOFAdata, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, MSMB, msqc1, multiWGCNAdata, muscData, nanotubes, NestLink, NetActivityData, OnassisJavaLibs, optimalFlowData, orthosData, parathyroidSE, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, preciseTADhub, ProteinGymR, ptairData, raerdata, rcellminerData, RforProteomics, RGMQLlib, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, scaeData, scanMiRData, scATAC.Explorer, SCLCBam, scMultiome, scpdata, scRNAseq, seventyGeneData, SFEData, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, smokingMouse, SpatialDatasets, spatialDmelxsim, spatialLIBD, STexampleData, SubcellularSpatialData, systemPipeRdata, TabulaMurisData, TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data, TCGAWorkflowData, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, TENxXeniumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, TransOmicsData, tuberculosis, tweeDEseqCountData, VariantToolsData, VectraPolarisData, WeberDivechaLCdata, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, recountWorkflow, RNAseq123, seqpac, sequencing, SingscoreAMLMutations, spicyWorkflow, variants, aIc, asteRisk, BiocManager, corrmeta, cyjShiny, DEHOGT, EHRtemporalVariability, genetic.algo.optimizeR, ggBubbles, GSEMA, ipsRdbs, magmaR, MarZIC, multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, rjsoncons, rworkflows, SCIntRuler, StepReg, TFactSR dependencyCount: 33 Package: biocthis Version: 1.17.0 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: 490cd416f48c19a4b23b8d720c65e015 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: devel git_last_commit: 1baef19 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biocthis_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocthis_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocthis_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocthis_1.17.0.tgz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R importsMe: HubPub suggestsMe: tripr dependencyCount: 44 Package: BiocVersion Version: 3.21.1 Depends: R (>= 4.5.0) License: Artistic-2.0 MD5sum: 58ff0a6132b77db8a38aad3668ea3e11 NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: devel git_last_commit: 1e5e5af git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocVersion_3.21.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocVersion_3.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocVersion_3.21.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocVersion_3.21.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.75.0 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, knitr, commonmark, BiocStyle License: Artistic-2.0 MD5sum: 79a4d8013922b825f35d532d8f4be6df NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/biocViews VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/biocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: devel git_last_commit: 8aa6daf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biocViews_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biocViews_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biocViews_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biocViews_1.75.0.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.html, vignettes/biocViews/inst/doc/HOWTO-BCV.html vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, BioGA, monocle, sigFeature, RforProteomics, genetic.algo.optimizeR suggestsMe: packFinder, plasmut, rworkflows dependencyCount: 17 Package: BiocWorkflowTools Version: 1.33.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: c33e3dda8d6e76bc6ae282057ccecd70 NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: devel git_last_commit: f751dad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiocWorkflowTools_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiocWorkflowTools_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiocWorkflowTools_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiocWorkflowTools_1.33.0.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow, SingscoreAMLMutations dependencyCount: 61 Package: biodb Version: 1.15.0 Depends: R (>= 4.1.0) Imports: BiocFileCache, R6, RCurl, RSQLite, Rcpp, XML, chk, git2r, jsonlite, lgr, lifecycle, methods, openssl, plyr, progress, rappdirs, stats, stringr, tools, withr, yaml LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, xml2 License: AGPL-3 MD5sum: 0bf7bc73882fd9b4126ef77971ac373c NeedsCompilation: yes Title: biodb, a library and a development framework for connecting to chemical and biological databases Description: The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages. biocViews: Software, Infrastructure, DataImport, KEGG Author: Pierrick Roger [aut, cre] (ORCID: ), Alexis Delabrière [ctb] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodb/issues git_url: https://git.bioconductor.org/packages/biodb git_branch: devel git_last_commit: 9aceaac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodb_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodb_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodb_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodb_1.15.0.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html, vignettes/biodb/inst/doc/new_connector.html, vignettes/biodb/inst/doc/new_entry_field.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects, Creating a new connector class for accessing a database., Creating a new field for entries. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R, vignettes/biodb/inst/doc/new_connector.R, vignettes/biodb/inst/doc/new_entry_field.R importsMe: biodbChebi, biodbHmdb, biodbNcbi, biodbNci, biodbUniprot, phenomis dependencyCount: 75 Package: biodbChebi Version: 1.13.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.5) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr License: AGPL-3 MD5sum: 7f9c2e3e15310e90c10fd5579c3e20c2 NeedsCompilation: no Title: biodbChebi, a library for connecting to the ChEBI Database Description: The biodbChebi library provides access to the ChEBI Database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name, mass or other fields. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbChebi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbChebi/issues git_url: https://git.bioconductor.org/packages/biodbChebi git_branch: devel git_last_commit: 686a27c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodbChebi_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbChebi_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbChebi_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbChebi_1.13.0.tgz vignettes: vignettes/biodbChebi/inst/doc/biodbChebi.html vignetteTitles: Introduction to the biodbChebi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbChebi/inst/doc/biodbChebi.R importsMe: phenomis dependencyCount: 76 Package: biodbHmdb Version: 1.13.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.3.2), Rcpp, zip LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: 441674ec510115b14cecd2707f115882 NeedsCompilation: yes Title: biodbHmdb, a library for connecting to the HMDB Database Description: The biodbHmdb library is an extension of the biodb framework package that provides access to the HMDB Metabolites database. It allows to download the whole HMDB Metabolites database locally, access entries and search for entries by name or description. A future version of this package will also include a search by mass and mass spectra annotation. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbHmdb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbHmdb/issues git_url: https://git.bioconductor.org/packages/biodbHmdb git_branch: devel git_last_commit: d2b9830 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodbHmdb_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbHmdb_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbHmdb_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbHmdb_1.13.0.tgz vignettes: vignettes/biodbHmdb/inst/doc/biodbHmdb.html vignetteTitles: Introduction to the biodbHmdb package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbHmdb/inst/doc/biodbHmdb.R dependencyCount: 77 Package: biodbNcbi Version: 1.11.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), R6, XML, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 8980bc87be701b854c81965cc860bc01 NeedsCompilation: no Title: biodbNcbi, a library for connecting to NCBI Databases. Description: The biodbNcbi library provides access to the NCBI databases CCDS, Gene, Pubchem Comp and Pubchem Subst, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or mass. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbNcbi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbNCbi/issues git_url: https://git.bioconductor.org/packages/biodbNcbi git_branch: devel git_last_commit: 4a3f8f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodbNcbi_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbNcbi_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbNcbi_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbNcbi_1.11.0.tgz vignettes: vignettes/biodbNcbi/inst/doc/biodbNcbi.html vignetteTitles: Introduction to the biodbNcbi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNcbi/inst/doc/biodbNcbi.R dependencyCount: 76 Package: biodbNci Version: 1.11.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, Rcpp, chk LinkingTo: Rcpp, testthat Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: 32ed12e5d8513dbb30c72d9e3902a66a NeedsCompilation: yes Title: biodbNci, a library for connecting to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database Description: The biodbNci library is an extension of the biodb framework package. It provides access to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database. It allows to retrieve entries by their accession number, and run specific web services. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbNci git_branch: devel git_last_commit: 10e1a05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodbNci_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbNci_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbNci_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbNci_1.11.0.tgz vignettes: vignettes/biodbNci/inst/doc/biodbNci.html vignetteTitles: Introduction to the biodbNci package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNci/inst/doc/biodbNci.R dependencyCount: 76 Package: biodbUniprot Version: 1.13.0 Depends: R (>= 4.1.0) Imports: R6, biodb (>= 1.4.2) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr, covr License: AGPL-3 MD5sum: 3315e1a472bd14a76ead171e22cf04cb NeedsCompilation: no Title: biodbUniprot, a library for connecting to the Uniprot Database Description: The biodbUniprot library is an extension of the biodb framework package. It provides access to the UniProt database. It allows to retrieve entries by their accession number, and run web service queries for searching for entries. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] (ORCID: ) Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbUniprot VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbUniprot/issues git_url: https://git.bioconductor.org/packages/biodbUniprot git_branch: devel git_last_commit: 098a02a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biodbUniprot_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biodbUniprot_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biodbUniprot_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biodbUniprot_1.13.0.tgz vignettes: vignettes/biodbUniprot/inst/doc/biodbUniprot.html vignetteTitles: Introduction to the biodbUniprot package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbUniprot/inst/doc/biodbUniprot.R dependencyCount: 76 Package: bioDist Version: 1.79.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 Archs: x64 MD5sum: e822979703d707013640accf415f0a00 NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: devel git_last_commit: a634128 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bioDist_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bioDist_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bioDist_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bioDist_1.79.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile dependencyCount: 8 Package: BioGA Version: 1.1.0 Depends: R (>= 4.4) Imports: ggplot2, graphics, Rcpp, SummarizedExperiment, animation, rlang, biocViews, sessioninfo, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 0f6b1f9143073e3cae00d4ed8fc9ca29 NeedsCompilation: yes Title: Bioinformatics Genetic Algorithm (BioGA) Description: Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R. biocViews: ExperimentalDesign, Technology Author: Dany Mukesha [aut, cre] (ORCID: ) Maintainer: Dany Mukesha URL: https://danymukesha.github.io/BioGA/ VignetteBuilder: knitr BugReports: https://github.com/danymukesha/BioGA/issues git_url: https://git.bioconductor.org/packages/BioGA git_branch: devel git_last_commit: 7656c41 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioGA_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioGA_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioGA_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioGA_1.1.0.tgz vignettes: vignettes/BioGA/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioGA/inst/doc/Introduction.R dependencyCount: 97 Package: biomaRt Version: 2.63.0 Depends: methods Imports: utils, AnnotationDbi, progress, stringr, httr2, digest, BiocFileCache, rappdirs, xml2, curl Suggests: BiocStyle, knitr, mockery, rmarkdown, testthat, httptest2 License: Artistic-2.0 MD5sum: 4e8ac02e5544fdffa47103f5919aa34c NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] (ORCID: ) Maintainer: Mike Smith URL: https://github.com/grimbough/biomaRt VignetteBuilder: knitr BugReports: https://github.com/grimbough/biomaRt/issues git_url: https://git.bioconductor.org/packages/biomaRt git_branch: devel git_last_commit: d5a275c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biomaRt_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomaRt_2.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomaRt_2.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomaRt_2.63.0.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: chromPlot, customProDB, DrugVsDisease, genefu, GenomicOZone, MineICA, NetSAM, PPInfer, RepViz, VegaMC, annotation importsMe: BadRegionFinder, BgeeCall, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, CoSIA, dagLogo, DMRcate, DominoEffect, dominoSignal, easyRNASeq, EDASeq, ELMER, EpiMix, epimutacions, FRASER, GDCRNATools, GenVisR, gINTomics, glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes, InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, motifbreakR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM, R453Plus1Toolbox, ramwas, recoup, rgsepd, scPipe, seq2pathway, SeqGSEA, sitadela, SPONGE, surfaltr, SWATH2stats, TCGAbiolinks, TEKRABber, TFEA.ChIP, transcriptogramer, txdbmaker, ViSEAGO, yarn, ExpHunterSuite, TCGAWorkflow, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, GRIN2, ProFAST, scGOclust, seeker, snplinkage, snplist suggestsMe: AnnotationForge, bioassayR, celda, ClusterJudge, crisprDesign, cTRAP, Damsel, DELocal, epistack, fedup, FELLA, GeDi, h5vc, martini, massiR, MethReg, MineICA, MiRaGE, MIRit, MutationalPatterns, netSmooth, oligo, OrganismDbi, pathlinkR, piano, Pigengene, progeny, R3CPET, RnBeads, rTRM, scater, ShortRead, SIM, sincell, tidysbml, trackViewer, wiggleplotr, zinbwave, BioMartGOGeneSets, BloodCancerMultiOmics2017, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, DGEobj, DGEobj.utils, grandR, kangar00, MoBPS, Patterns, Platypus, scDiffCom, SNPassoc dependencyCount: 68 Package: biomformat Version: 1.35.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 Archs: x64 MD5sum: a95d2efd7d733cbccc96aec0e17b7aa4 NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: devel git_last_commit: a3d34c3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biomformat_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomformat_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomformat_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomformat_1.35.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: microbiomeExplorer suggestsMe: animalcules, iSEEtree, metagenomeSeq, MGnifyR, mia, MicrobiotaProcess dependencyCount: 14 Package: BioMVCClass Version: 1.75.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: d118422b5114c999e4b0728ec566e4ec NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: devel git_last_commit: 61ff376 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioMVCClass_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioMVCClass_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioMVCClass_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioMVCClass_1.75.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.47.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: eae8b3e80a6262c3b6a80a9707a6fc86 NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: devel git_last_commit: de5ca15 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biomvRCNS_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biomvRCNS_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biomvRCNS_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biomvRCNS_1.47.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 158 Package: BioNAR Version: 1.9.1 Depends: R (>= 3.5.0), igraph (>= 2.0.1.1), poweRlaw, latex2exp, RSpectra, Rdpack Imports: stringr, viridis, fgsea, grid, methods, AnnotationDbi, dplyr, GO.db, org.Hs.eg.db (>= 3.19.1), rSpectral, WGCNA, ggplot2, ggrepel, minpack.lm, cowplot, data.table, scales, stats, Matrix Suggests: knitr, BiocStyle, magick, rmarkdown, igraphdata, testthat (>= 3.0.0), vdiffr, devtools, pander, plotly, randomcoloR License: Artistic-2.0 MD5sum: 1a3b67e4a7231cd2ebaee604e20c6b68 NeedsCompilation: no Title: Biological Network Analysis in R Description: the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms. biocViews: Software, GraphAndNetwork, Network Author: Colin Mclean [aut], Anatoly Sorokin [aut, cre], Oksana Sorokina [aut], J. Douglas Armstrong [aut, fnd], T. Ian Simpson [ctb, fnd] Maintainer: Anatoly Sorokin VignetteBuilder: knitr BugReports: https://github.com/lptolik/BioNAR/issues/ git_url: https://git.bioconductor.org/packages/BioNAR git_branch: devel git_last_commit: e9d4e8b git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-11 source.ver: src/contrib/BioNAR_1.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNAR_1.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNAR_1.9.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNAR_1.9.1.tgz vignettes: vignettes/BioNAR/inst/doc/BioNAR_overview.html vignetteTitles: BioNAR: Biological Network Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNAR/inst/doc/BioNAR_overview.R dependencyCount: 141 Package: BioNERO Version: 1.15.0 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, ggdendro, matrixStats, sva, RColorBrewer, ComplexHeatmap, ggplot2, rlang, ggrepel, patchwork, reshape2, igraph, ggnetwork, intergraph, NetRep, stats, grDevices, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2, networkD3, covr License: GPL-3 Archs: x64 MD5sum: 87e1f16d89087129db755176edd6d2aa NeedsCompilation: no Title: Biological Network Reconstruction Omnibus Description: BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks. biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology, GraphAndNetwork, Preprocessing, Network, NetworkInference Author: Fabricio Almeida-Silva [cre, aut] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/BioNERO VignetteBuilder: knitr BugReports: https://github.com/almeidasilvaf/BioNERO/issues git_url: https://git.bioconductor.org/packages/BioNERO git_branch: devel git_last_commit: 20a3539 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioNERO_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNERO_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNERO_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNERO_1.15.0.tgz vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html vignetteTitles: Gene coexpression network inference, Gene regulatory network inference with BioNERO, Network comparison: consensus modules and module preservation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R importsMe: cageminer dependencyCount: 165 Package: BioNet Version: 1.67.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 24ed1488c9424d4359f29c4c4dd21a40 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: devel git_last_commit: b06522b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioNet_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioNet_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioNet_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioNet_1.67.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: gatom, SMITE suggestsMe: SANTA, mwcsr dependencyCount: 54 Package: BioQC Version: 1.35.0 Depends: R (>= 3.5.0), Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer License: GPL (>=3) + file LICENSE MD5sum: c5b8fb40c96c3dfce66cf5a3e582c629 NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] Maintainer: Jitao David Zhang URL: https://accio.github.io/BioQC VignetteBuilder: knitr BugReports: https://accio.github.io/BioQC/issues git_url: https://git.bioconductor.org/packages/BioQC git_branch: devel git_last_commit: 33cb26a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioQC_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioQC_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioQC_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BioQC_1.35.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-introduction.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc-simulation.html, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html, vignettes/BioQC/inst/doc/BioQC.html vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test, BioQC: Detect tissue heterogeneity in gene expression data, Using BioQC with signed genesets, BioQC-benchmark: Testing Efficiency,, Sensitivity and Specificity of BioQC on simulated and real-world data, Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests, BioQC-kidney: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-introduction.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc-simulation.R, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R, vignettes/BioQC/inst/doc/BioQC.R dependencyCount: 15 Package: biosigner Version: 1.35.0 Imports: Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 45ea92ddcfb689837fd6d9bde14a9e88 NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.3389/fmolb.2016.00026 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: devel git_last_commit: 8a970ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biosigner_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biosigner_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biosigner_1.35.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: biosigner-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R suggestsMe: phenomis dependencyCount: 110 Package: Biostrings Version: 2.75.3 Depends: R (>= 4.0.0), BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), GenomeInfoDb Imports: methods, utils, grDevices, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: graphics, pwalign, BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr, testthat (>= 3.0.0), covr License: Artistic-2.0 Archs: x64 MD5sum: bf976df0351d6f6d1b4d5f186657f649 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Robert Gentleman [aut], Saikat DebRoy [aut], Vince Carey [ctb], Nicolas Delhomme [ctb], Felix Ernst [ctb], Wolfgang Huber [ctb] ('matchprobes' vignette), Beryl Kanali [ctb] (Converted 'MultipleAlignments' vignette from Sweave to RMarkdown), Haleema Khan [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Aidan Lakshman [ctb], Kieran O'Neill [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Albert Vill [ctb], Jen Wokaty [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Erik Wright [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Biostrings VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: devel git_last_commit: 1169409 git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/Biostrings_2.75.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/Biostrings_2.75.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Biostrings_2.75.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Biostrings_2.75.3.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf, vignettes/Biostrings/inst/doc/matchprobes.html, vignettes/Biostrings/inst/doc/MultipleAlignments.html vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Pairwise Sequence Alignments, Handling probe sequence information, Multiple Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R dependsOnMe: alabaster.string, altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, BSgenomeForge, chimeraviz, ChIPanalyser, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, motifTestR, msa, muscle, oligo, ORFhunteR, periodicDNA, 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JASPAR2014, NestLink, generegulation, sequencing, CleanBSequences, STRMPS, SubVis importsMe: AllelicImbalance, AneuFinder, AnnotationHubData, appreci8R, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall, biovizBase, branchpointer, bsseq, BUMHMM, BUSpaRse, CAGEr, CellBarcode, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, CNEr, CNVfilteR, cogeqc, compEpiTools, consensusDE, coRdon, crisprBase, crisprBowtie, crisprDesign, crisprScore, crisprShiny, CrispRVariants, crisprViz, customProDB, dada2, dagLogo, DAMEfinder, Damsel, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, doubletrouble, DuplexDiscovereR, easyRNASeq, EDASeq, enhancerHomologSearch, ensembldb, EpiTxDb, esATAC, eudysbiome, EventPointer, factR, FastqCleaner, FLAMES, G4SNVHunter, GA4GHclient, gcapc, gcrma, gDNAx, GeneRegionScan, genomation, GenomAutomorphism, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicScores, GenVisR, 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cubar, deepredeff, DNAmotif, dowser, EncDNA, ensembleTax, EpiSemble, GB5mcPred, genBaRcode, geneHapR, GenomicSig, hoardeR, ICAMS, iimi, immuneSIM, kibior, kmeRs, kmeRtone, longreadvqs, metaCluster, MicroSEC, MitoHEAR, MixviR, ogrdbstats, OpEnHiMR, PACVr, Platypus, PredCRG, refseqR, revert, SeedMatchR, seqmagick, simMP, SMITIDstruct, vhcub suggestsMe: alabaster.files, annotate, AnnotationForge, AnnotationHub, autonomics, bambu, BANDITS, CSAR, eisaR, GenomicFiles, GenomicRanges, GenomicTuples, ggseqalign, GWASTools, HiContacts, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, nuCpos, plyinteractions, RNAmodR.AlkAnilineSeq, rpx, rTRM, screenCounter, spatzie, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, bbl, bio3d, DDPNA, file2meco, gkmSVM, karyotapR, maGUI, MARVEL, MiscMetabar, msaR, NameNeedle, orthGS, phangorn, polyRAD, protr, seqtrie, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 25 Package: BioTIP Version: 1.21.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 67584ffd07854144fd66d2462f5f2a95 NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Yuxi (Jennifer) Sun , Zhezhen Wang , and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: devel git_last_commit: 3d67b56 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BioTIP_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BioTIP_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BioTIP_1.21.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 82 Package: biotmle Version: 1.31.0 Depends: R (>= 4.0) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: MIT + file LICENSE Archs: x64 MD5sum: d2556e05683a882534894366b6344a18 NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (ORCID: ), Alan Hubbard [aut, ths] (ORCID: ), Mark van der Laan [aut, ths] (ORCID: ), Weixin Cai [ctb] (ORCID: ), Philippe Boileau [ctb] (ORCID: ) Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: devel git_last_commit: 7b0119e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biotmle_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biotmle_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/biotmle_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/biotmle_1.31.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 111 Package: biovizBase Version: 1.55.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 MD5sum: d8873e007c9c6a7066a319acdf723e82 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: devel git_last_commit: 85d7c94 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biovizBase_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biovizBase_1.55.0.zip vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, Rqc suggestsMe: CINdex, Damsel, derfinderPlot, FRASER, NanoStringNCTools, OUTRIDER, R3CPET, shiny.gosling, StructuralVariantAnnotation, Signac dependencyCount: 136 Package: BiRewire Version: 3.39.0 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 MD5sum: 0fd2539da644b312a3ba3b5dddb6275e NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted. biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: devel git_last_commit: 8cf10d9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiRewire_3.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiRewire_3.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BiRewire_3.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BiRewire_3.39.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 20 Package: biscuiteer Version: 1.21.0 Depends: R (>= 4.1.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, IRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rmarkdown, markdown, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10, BiocStyle License: GPL-3 Archs: x64 MD5sum: 1fbd4eaa98633c7ce8008eb447a3e7dc NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut], Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut] Maintainer: Jacob Morrison URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: devel git_last_commit: 7fd1d20 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/biscuiteer_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/biscuiteer_1.21.0.zip vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 181 Package: BiSeq Version: 1.47.0 Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 Archs: x64 MD5sum: e822404b65d3bc4b042a51b7f8f92b0f NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: devel git_last_commit: 225e029 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BiSeq_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BiSeq_1.47.0.zip vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata suggestsMe: updateObject dependencyCount: 92 Package: blacksheepr Version: 1.19.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: 9890098d8395b6a9728307a006c6dff1 NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: devel git_last_commit: 2a4087a git_last_commit_date: 2024-04-30 Date/Publication: 2024-10-25 source.ver: src/contrib/blacksheepr_1.19.0.tar.gz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 81 Package: blima Version: 1.41.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 MD5sum: f855c2e6d71b12431f2dadd6717b556c NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: devel git_last_commit: c59eaff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/blima_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/blima_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/blima_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/blima_1.41.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R suggestsMe: blimaTestingData dependencyCount: 82 Package: BLMA Version: 1.31.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 2ac66c3cc15d2f2cc789ac408584de21 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Van-Dung Pham git_url: https://git.bioconductor.org/packages/BLMA git_branch: devel git_last_commit: 3ad7545 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BLMA_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BLMA_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BLMA_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BLMA_1.31.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R dependencyCount: 78 Package: BloodGen3Module Version: 1.15.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics, stats, grDevices, circlize, testthat, ComplexHeatmap(>= 1.99.8), ggplot2, matrixStats, gtools, reshape2, preprocessCore, randomcoloR, V8, limma Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 664258ed1f8ec1dd4f826257d04bce9e NeedsCompilation: no Title: This R package for performing module repertoire analyses and generating fingerprint representations Description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows. biocViews: Software, Visualization, GeneExpression Author: Darawan Rinchai [aut, cre] (ORCID: ) Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: devel git_last_commit: 1a37edb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BloodGen3Module_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BloodGen3Module_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BloodGen3Module_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BloodGen3Module_1.15.0.tgz vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R dependencyCount: 130 Package: bluster Version: 1.17.0 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp, assorthead Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster, DirichletMultinomial, vegan, fastcluster License: GPL-3 MD5sum: 76202e3d1ed5b0e0b51b5db7570cd3e3 NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb], Basil Courbayre [ctb], Tuomas Borman [ctb], Leo Lahti [ctb] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: devel git_last_commit: 510e7a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bluster_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bluster_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bluster_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bluster_1.17.0.tgz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R dependsOnMe: OSCA.intro, OSCA.workflows, SingleRBook importsMe: concordexR, epiregulon, mia, MPAC, scDblFinder, scDiagnostics, scran, Voyager, Canek suggestsMe: batchelor, ChromSCape, dittoSeq, mbkmeans, miaViz, MOSim, mumosa, SpatialDDLS, SuperCell dependencyCount: 34 Package: bnbc Version: 1.29.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils, HiCBricks LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: d9537db99ad482734e1d8caf242a61f5 NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: devel git_last_commit: c54d071 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bnbc_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bnbc_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bnbc_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bnbc_1.29.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 143 Package: bnem Version: 1.15.0 Depends: R (>= 4.1) Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster, flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods, utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown Suggests: knitr, BiocGenerics, MatrixGenerics, BiocStyle, RUnit License: GPL-3 Archs: x64 MD5sum: da0e2f4a7c24868dae9b147bf4f83f72 NeedsCompilation: no Title: Training of logical models from indirect measurements of perturbation experiments Description: bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate). biocViews: Pathways, SystemsBiology, NetworkInference, Network, GeneExpression, GeneRegulation, Preprocessing Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/MartinFXP/bnem/ VignetteBuilder: knitr BugReports: https://github.com/MartinFXP/bnem/issues git_url: https://git.bioconductor.org/packages/bnem git_branch: devel git_last_commit: ea50de2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bnem_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bnem_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bnem_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bnem_1.15.0.tgz vignettes: vignettes/bnem/inst/doc/bnem.html vignetteTitles: bnem.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnem/inst/doc/bnem.R dependencyCount: 180 Package: BOBaFIT Version: 1.11.0 Depends: R (>= 2.10) Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr, GenomicRanges, ggforce, stringr, plyranges, methods, utils, magrittr Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0), utils, testthat License: GPL (>= 3) Archs: x64 MD5sum: d48a94d844122e83472cc50ccbffe68f NeedsCompilation: no Title: Refitting diploid region profiles using a clustering procedure Description: This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster. biocViews: CopyNumberVariation, Clustering, Visualization, Normalization, Software Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza Solli [aut] Maintainer: Gaia Mazzocchetti URL: https://github.com/andrea-poletti-unibo/BOBaFIT VignetteBuilder: knitr BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues git_url: https://git.bioconductor.org/packages/BOBaFIT git_branch: devel git_last_commit: eb24815 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BOBaFIT_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BOBaFIT_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BOBaFIT_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BOBaFIT_1.11.0.tgz vignettes: vignettes/BOBaFIT/inst/doc/BOBaFIT.html, vignettes/BOBaFIT/inst/doc/Data-Preparation.html vignetteTitles: BOBaFIT.Rmd, Data preparation using TCGA-BRCA database.Rmd hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BOBaFIT/inst/doc/BOBaFIT.R, vignettes/BOBaFIT/inst/doc/Data-Preparation.R dependencyCount: 170 Package: borealis Version: 1.11.0 Depends: R (>= 4.2.0), Biobase Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist, bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot, dplyr, rlang, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL-3 MD5sum: 4525256e8268c65c9f29050ba3140831 NeedsCompilation: no Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution Description: Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses. biocViews: Sequencing, Coverage, DNAMethylation, DifferentialMethylation Author: Garrett Jenkinson [aut, cre] (ORCID: ) Maintainer: Garrett Jenkinson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/borealis git_branch: devel git_last_commit: 9ec3fee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/borealis_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/borealis_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/borealis_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/borealis_1.11.0.tgz vignettes: vignettes/borealis/inst/doc/borealis.html vignetteTitles: Borealis outlier methylation detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/borealis/inst/doc/borealis.R dependencyCount: 118 Package: BPRMeth Version: 1.33.0 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 84c0b179902afccd9acf30d4d52d9b28 NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: devel git_last_commit: 5371378 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BPRMeth_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BPRMeth_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BPRMeth_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BPRMeth_1.33.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R dependsOnMe: Melissa dependencyCount: 98 Package: BRAIN Version: 1.53.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 0972cf7fdcdf11caff0f71640ea4bd2e NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: devel git_last_commit: e298262 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BRAIN_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BRAIN_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BRAIN_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BRAIN_1.53.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter, RforProteomics dependencyCount: 30 Package: branchpointer Version: 1.33.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 68736754ab3004999df0990ba4acb7fa NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: devel git_last_commit: 7492c70 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/branchpointer_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/branchpointer_1.33.0.zip vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 156 Package: breakpointR Version: 1.25.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: 45d40475f8b3c711590207c96854c974 NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: devel git_last_commit: da582c9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/breakpointR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/breakpointR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/breakpointR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/breakpointR_1.25.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 84 Package: brendaDb Version: 1.21.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: c5a5fed736ff4c22dd5990fb1173a7c7 NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] (ORCID: ) Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: devel git_last_commit: 513401a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/brendaDb_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/brendaDb_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/brendaDb_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/brendaDb_1.21.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 58 Package: BREW3R.r Version: 1.3.0 Imports: GenomicRanges, methods, rlang, S4Vectors, utils Suggests: testthat (>= 3.0.0), IRanges, knitr, rmarkdown, BiocStyle, rtracklayer License: GPL-3 MD5sum: 0984a60c2e7f466025e5ccfc45623f02 NeedsCompilation: no Title: R package associated to BREW3R Description: This R package provide functions that are used in the BREW3R workflow. This mainly contains a function that extend a gtf as GRanges using information from another gtf (also as GRanges). The process allows to extend gene annotation without increasing the overlap between gene ids. biocViews: GenomeAnnotation Author: Lucille Lopez-Delisle [aut, cre] (ORCID: ) Maintainer: Lucille Lopez-Delisle URL: https://github.com/lldelisle/BREW3R.r VignetteBuilder: knitr BugReports: https://github.com/lldelisle/BREW3R.r/issues/ git_url: https://git.bioconductor.org/packages/BREW3R.r git_branch: devel git_last_commit: 1dca2c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BREW3R.r_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BREW3R.r_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BREW3R.r_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BREW3R.r_1.3.0.tgz vignettes: vignettes/BREW3R.r/inst/doc/BREW3R.r.html vignetteTitles: BREW3R.r hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BREW3R.r/inst/doc/BREW3R.r.R dependencyCount: 25 Package: BridgeDbR Version: 2.17.1 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 Archs: x64 MD5sum: fde2bac38f709d8274f6b0e962d10a7e NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Denise Slenter, Anwesha Bohler , Lars Eijssen , Tooba Abbassi-Daloii Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: devel git_last_commit: ec6e592 git_last_commit_date: 2024-11-06 Date/Publication: 2024-11-06 source.ver: src/contrib/BridgeDbR_2.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BridgeDbR_2.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BridgeDbR_2.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BridgeDbR_2.17.1.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 3 Package: broadSeq Version: 1.1.0 Depends: dplyr, ggpubr, SummarizedExperiment Imports: BiocStyle, DELocal, EBSeq (>= 1.38.0), DESeq2 (>= 1.38.2), NOISeq, forcats (>= 1.0.0), genefilter, ggplot2, ggplotify, plyr, clusterProfiler (>= 4.8.2), pheatmap, sechm (>= 1.6.0), stringr, purrr (>= 0.3.5), edgeR (>= 3.40.1) Suggests: knitr, limma (>= 3.54.0), rmarkdown, stats (>= 4.2.2), samr License: MIT + file LICENSE MD5sum: b5f1ff8623c91377dccf3d99847ee9c7 NeedsCompilation: no Title: broadSeq : for streamlined exploration of RNA-seq data Description: This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics, Sequencing, Coverage, GeneSetEnrichment, GO Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/broadSeq VignetteBuilder: knitr BugReports: https://github.com/dasroy/broadSeq/issues git_url: https://git.bioconductor.org/packages/broadSeq git_branch: devel git_last_commit: 78af4d8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/broadSeq_1.1.0.tar.gz vignettes: vignettes/broadSeq/inst/doc/broadSeq.html vignetteTitles: Using broadSeq to analyze RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/broadSeq/inst/doc/broadSeq.R dependencyCount: 232 Package: BrowserViz Version: 2.29.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 8dbfedeed23f178c6468fb2a622f46fa NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: devel git_last_commit: 0acee92 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BrowserViz_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BrowserViz_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BrowserViz_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BrowserViz_2.29.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 15 Package: BSgenome Version: 1.75.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), BiocIO, rtracklayer Imports: utils, stats, matrixStats, XVector, Rsamtools Suggests: BiocManager, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit, BSgenomeForge License: Artistic-2.0 MD5sum: 279fa9c8685d5609cf3a56b9166a6554 NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: devel git_last_commit: c53bfc0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BSgenome_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BSgenome_1.75.0.zip vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, BSgenomeForge, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, rGADEM, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, crisprViz, diffHic, enhancerHomologSearch, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, katdetectr, m6Aboost, MADSEQ, methrix, MethylSeekR, MMDiff2, monaLisa, Motif2Site, motifbreakR, motifmatchr, MotifPeeker, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, raer, RAIDS, RareVariantVis, RCAS, regioneR, REMP, Repitools, RESOLVE, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, signeR, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spatzie, spiky, SpliceWiz, TAPseq, TFBSTools, transmogR, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, XNAString, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ICAMS, MOCHA, revert, simMP suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, DegCre, DiffBind, easyRNASeq, eisaR, factR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, sitadela, gkmSVM, MARVEL, sigminer, Signac dependencyCount: 59 Package: BSgenomeForge Version: 1.7.0 Depends: R (>= 4.3.0), methods, BiocGenerics, IRanges, GenomeInfoDb (>= 1.33.17), Biostrings, BSgenome Imports: utils, stats, Biobase, S4Vectors, GenomicRanges, BiocIO, rtracklayer Suggests: GenomicFeatures, Rsamtools, testthat, knitr, rmarkdown, BiocStyle, devtools, BSgenome.Celegans.UCSC.ce2 License: Artistic-2.0 MD5sum: e93b7e14d76e1849508c026992096e89 NeedsCompilation: no Title: Forge your own BSgenome data package Description: A set of tools to forge BSgenome data packages. Supersedes the old seed-based tools from the BSgenome software package. This package allows the user to create a BSgenome data package in one function call, simplifying the old seed-based process. biocViews: Infrastructure, DataRepresentation, GenomeAssembly, Annotation, GenomeAnnotation, Sequencing, Alignment, DataImport, SequenceMatching Author: Hervé Pagès [aut, cre], Atuhurira Kirabo Kakopo [aut], Emmanuel Chigozie Elendu [ctb], Prisca Chidimma Maduka [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenomeForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BSgenomeForge/issues git_url: https://git.bioconductor.org/packages/BSgenomeForge git_branch: devel git_last_commit: b850d52 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BSgenomeForge_1.7.0.tar.gz vignettes: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.pdf, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.html vignetteTitles: Advanced BSgenomeForge usage, A quick introduction to the BSgenomeForge package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.R, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.R suggestsMe: BSgenome dependencyCount: 60 Package: bsseq Version: 1.43.1 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, parallel, tools, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5, beachmat (>= 2.23.2) LinkingTo: Rcpp, beachmat, assorthead (>= 1.1.4) Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, batchtools License: Artistic-2.0 MD5sum: fe099c142e417c5335b6aa13ec9c25fe NeedsCompilation: yes Title: Analyze, manage and store whole-genome methylation data Description: A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. This includes whole-genome bisulfite sequencing and Oxford nanopore data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre] (ORCID: ), Peter Hickey [aut] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: devel git_last_commit: 1cce9a0 git_last_commit_date: 2024-12-15 Date/Publication: 2024-12-16 source.ver: src/contrib/bsseq_1.43.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/bsseq_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bsseq_1.43.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bsseq_1.43.1.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData importsMe: borealis, DMRcate, methylCC, methylSig, MIRA, NanoMethViz, scmeth suggestsMe: methrix, tissueTreg dependencyCount: 89 Package: BubbleTree Version: 2.37.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: 297d6761d3befaafb608706b95ba8f9e NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: devel git_last_commit: 19789a5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BubbleTree_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BubbleTree_2.37.0.zip vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R dependencyCount: 151 Package: BufferedMatrix Version: 1.71.1 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: x64 MD5sum: 0e70e72d5a77338d779dde96183a9e6b NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: devel git_last_commit: 824836d git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/BufferedMatrix_1.71.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BufferedMatrix_1.71.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrix_1.71.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrix_1.71.1.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.71.1 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) MD5sum: 38b948ecb73b4809cdf201fb371ec37d NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: devel git_last_commit: 15eb988 git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/BufferedMatrixMethods_1.71.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/BufferedMatrixMethods_1.71.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrixMethods_1.71.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrixMethods_1.71.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.13.0 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: f1c7ebf9dff72656fb538c7191c28b6e NeedsCompilation: no Title: R-side access to published microbial signatures from BugSigDB Description: The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT. biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/bugsigdbr VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bugsigdbr/issues git_url: https://git.bioconductor.org/packages/bugsigdbr git_branch: devel git_last_commit: 3d187dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bugsigdbr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bugsigdbr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bugsigdbr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bugsigdbr_1.13.0.tgz vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html vignetteTitles: R-side access to BugSigDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R dependencyCount: 52 Package: BUMHMM Version: 1.31.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 Archs: x64 MD5sum: 89a9a3c50b1209fa3b04d9f30af0cd22 NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: devel git_last_commit: 61b2956 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BUMHMM_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUMHMM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUMHMM_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUMHMM_1.31.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 125 Package: bumphunter Version: 1.49.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, txdbmaker, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 2b7e58aba276896434435ee20d929eac NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb], Tamilselvi Guharaj [cre] Maintainer: Tamilselvi Guharaj URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: devel git_last_commit: 28669ca git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/bumphunter_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/bumphunter_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bumphunter_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bumphunter_1.49.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions, epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData dependencyCount: 86 Package: BumpyMatrix Version: 1.15.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: c67043ff427cc53eecf4388465a7539b NeedsCompilation: no Title: Bumpy Matrix of Non-Scalar Objects Description: Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. biocViews: Software, Infrastructure, DataRepresentation Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/BumpyMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/BumpyMatrix/issues git_url: https://git.bioconductor.org/packages/BumpyMatrix git_branch: devel git_last_commit: e7f4260 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BumpyMatrix_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BumpyMatrix_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BumpyMatrix_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BumpyMatrix_1.15.0.tgz vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html vignetteTitles: The BumpyMatrix class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R dependsOnMe: alabaster.bumpy importsMe: CoreGx, gDRcore, gDRimport, gDRutils, MerfishData, MouseGastrulationData, TENxXeniumData suggestsMe: escheR, gDR, ggspavis, SpatialExperiment, tpSVG, STexampleData dependencyCount: 13 Package: BUS Version: 1.63.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 MD5sum: 4dbbe1d0a01fcaa01290b052bbea0c97 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: devel git_last_commit: 17fe722 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BUS_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUS_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUS_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUS_1.63.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.25.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: c4e6e6b1243c792c85ba6800cc5fc57c NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: devel git_last_commit: 0194875 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BUScorrect_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUScorrect_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUScorrect_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUScorrect_1.25.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 42 Package: BUSpaRse Version: 1.21.0 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, txdbmaker, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, txdbmaker, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: b1f4571234413c981dd2e10c8200cc70 NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: devel git_last_commit: 199b6ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BUSpaRse_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUSpaRse_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUSpaRse_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUSpaRse_1.21.0.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 125 Package: BUSseq Version: 1.13.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: ac230a447b9df708273967590baad515 NeedsCompilation: yes Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript. biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing Author: Fangda Song [aut, cre] (ORCID: ), Ga Ming Chan [aut], Yingying Wei [aut] (ORCID: ) Maintainer: Fangda Song URL: https://github.com/songfd2018/BUSseq VignetteBuilder: knitr BugReports: https://github.com/songfd2018/BUSseq/issues git_url: https://git.bioconductor.org/packages/BUSseq git_branch: devel git_last_commit: 4ec2844 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/BUSseq_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/BUSseq_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/BUSseq_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/BUSseq_1.13.0.tgz vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R dependencyCount: 43 Package: CaDrA Version: 1.5.0 Depends: R (>= 4.4.0) Imports: doParallel, ggplot2, gplots, graphics, grid, gtable, knnmi, MASS, methods, misc3d, plyr, ppcor, R.cache, reshape2, stats, SummarizedExperiment Suggests: BiocManager, devtools, knitr, pheatmap, rmarkdown, testthat (>= 3.1.6) License: GPL-3 + file LICENSE Archs: x64 MD5sum: ece8ce52575aeaa3f6ade6490b47de32 NeedsCompilation: yes Title: Candidate Driver Analysis Description: Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events. biocViews: Microarray, RNASeq, GeneExpression, Software, FeatureExtraction Author: Reina Chau [aut, cre] (ORCID: ), Katia Bulekova [aut] (ORCID: ), Vinay Kartha [aut], Stefano Monti [aut] (ORCID: ) Maintainer: Reina Chau URL: https://github.com/montilab/CaDrA/ VignetteBuilder: knitr BugReports: https://github.com/montilab/CaDrA/issues git_url: https://git.bioconductor.org/packages/CaDrA git_branch: devel git_last_commit: 3f6af6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CaDrA_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CaDrA_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CaDrA_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CaDrA_1.5.0.tgz vignettes: vignettes/CaDrA/inst/doc/docker.html, vignettes/CaDrA/inst/doc/permutation_based_testing.html, vignettes/CaDrA/inst/doc/scoring_functions.html vignetteTitles: How to run CaDrA within a Docker Environment, Permutation-Based Testing, Scoring Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaDrA/inst/doc/permutation_based_testing.R, vignettes/CaDrA/inst/doc/scoring_functions.R dependencyCount: 87 Package: CAEN Version: 1.15.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown,BiocManager,SummarizedExperiment,BiocStyle License: GPL-2 Archs: x64 MD5sum: 1efc21f5728b88ef1b31a4c684b1394b NeedsCompilation: no Title: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq Description: With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples. biocViews: DifferentialExpression, Sequencing, Classification, RNASeq, ATACSeq, SingleCell, GeneExpression, RIPSeq Author: Zhou Yan [aut, cre] Maintainer: Zhou Yan <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAEN git_branch: devel git_last_commit: ad0515f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CAEN_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAEN_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CAEN_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CAEN_1.15.0.tgz vignettes: vignettes/CAEN/inst/doc/CAEN.html vignetteTitles: CAEN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAEN/inst/doc/CAEN.R dependencyCount: 38 Package: CAFE Version: 1.43.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: cc62358039c41de986c0bd63b6e20404 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: devel git_last_commit: da805c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CAFE_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAFE_1.43.0.zip vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 169 Package: CAGEfightR Version: 1.27.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 970a7e0c9e0820b2285805b26083eae6 NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: devel git_last_commit: 5e8f35b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CAGEfightR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAGEfightR_1.27.0.zip vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow importsMe: CAGEr suggestsMe: nanotubes dependencyCount: 164 Package: cageminer Version: 1.13.0 Depends: R (>= 4.1) Imports: ggplot2, rlang, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: 42fd5350c01354a81a2439cc1f528f87 NeedsCompilation: no Title: Candidate Gene Miner Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments. biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation, GeneExpression, NetworkEnrichment, VariantAnnotation, FunctionalGenomics, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cageminer VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cageminer git_url: https://git.bioconductor.org/packages/cageminer git_branch: devel git_last_commit: ce80090 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cageminer_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cageminer_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cageminer_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cageminer_1.13.0.tgz vignettes: vignettes/cageminer/inst/doc/cageminer.html vignetteTitles: Mining high-confidence candidate genes with cageminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cageminer/inst/doc/cageminer.R dependencyCount: 208 Package: CAGEr Version: 2.13.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, CAGEfightR, data.table, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, rlang, Rsamtools, reshape2, rtracklayer, S4Vectors (>= 0.27.5), scales, som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9, DESeq2, FANTOM3and4CAGE, ggseqlogo, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 023128492841632e777bbb7223cfc272 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: The _CAGEr_ package identifies transcription start sites (TSS) and their usage frequency from CAGE (Cap Analysis Gene Expression) sequencing data. It normalises raw CAGE tag count, clusters TSSs into tag clusters (TC) and aggregates them across multiple CAGE experiments to construct consensus clusters (CC) representing the promoterome. CAGEr provides functions to profile expression levels of these clusters by cumulative expression and rarefaction analysis, and outputs the plots in ggplot2 format for further facetting and customisation. After clustering, CAGEr performs analyses of promoter width and detects differential usage of TSSs (promoter shifting) between samples. CAGEr also exports its data as genome browser tracks, and as R objects for downsteam expression analysis by other Bioconductor packages such as DESeq2, CAGEfightR, or seqArchR. biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic [ctb], Sarvesh Nikumbh [ctb] Maintainer: Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: devel git_last_commit: cdfe2b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CAGEr_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAGEr_2.13.0.zip vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 178 Package: calm Version: 1.21.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: f087b619ff82097bdf60afb609ed6efe NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: devel git_last_commit: 2e49d35 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/calm_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/calm_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/calm_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/calm_1.21.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.63.0 Depends: R (>= 3.5.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics, multtest Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: e65dc944e1fd00a1801fcf24a5ea96c3 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: devel git_last_commit: 1d980b7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CAMERA_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CAMERA_1.63.0.zip vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 160 Package: CaMutQC Version: 1.3.0 Depends: R (>= 4.0.0) Imports: ggplot2, dplyr, org.Hs.eg.db, vcfR, clusterProfiler, stringr, DT, MesKit, maftools, data.table, utils, stats, methods, tidyr Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 31bc0a2ace40b7f805c9e91516306752 NeedsCompilation: no Title: An R Package for Comprehensive Filtration and Selection of Cancer Somatic Mutations Description: CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags. And a detailed and vivid filter report will be offered after completing a whole filtration or selection section. Also, CaMutQC integrates serveral methods and gene panels for Tumor Mutational Burden (TMB) estimation. biocViews: Software, QualityControl, GeneTarget Author: Xin Wang [aut, cre] (ORCID: ) Maintainer: Xin Wang URL: https://github.com/likelet/CaMutQC VignetteBuilder: knitr BugReports: https://github.com/likelet/CaMutQC/issues git_url: https://git.bioconductor.org/packages/CaMutQC git_branch: devel git_last_commit: c466d6a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CaMutQC_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CaMutQC_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CaMutQC_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CaMutQC_1.3.0.tgz vignettes: vignettes/CaMutQC/inst/doc/CaMutQC-manual.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaMutQC/inst/doc/CaMutQC-manual.R dependencyCount: 173 Package: canceR Version: 1.41.0 Depends: R (>= 4.3), tcltk, cBioPortalData Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, tidyr, dplyr, graphics, stats, utils, grDevices, R.oo, R.methodsS3 Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 Archs: x64 MD5sum: 7194bb476457a01b444c7ba9370b6733 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud SystemRequirements: Tktable, BWidget VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/canceR/issues git_url: https://git.bioconductor.org/packages/canceR git_branch: devel git_last_commit: 89371a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/canceR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/canceR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/canceR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/canceR_1.41.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.html vignetteTitles: canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/canceR/inst/doc/canceR.R dependencyCount: 214 Package: cancerclass Version: 1.51.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 MD5sum: 4bc0b2a19abfa70beb2c5802db1e0abd NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: devel git_last_commit: 7222a6b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cancerclass_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cancerclass_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cancerclass_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cancerclass_1.51.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 8 Package: cardelino Version: 1.9.0 Depends: R (>= 4.2), stats Imports: combinat, GenomeInfoDb, GenomicRanges, ggplot2, ggtree, Matrix, matrixStats, methods, pheatmap, snpStats, S4Vectors, utils, VariantAnnotation, vcfR Suggests: BiocStyle, foreach, knitr, pcaMethods, rmarkdown, testthat, VGAM Enhances: doMC License: GPL-3 MD5sum: 8185c2a2a54c7337598cb46df5af2560 NeedsCompilation: yes Title: Clone Identification from Single Cell Data Description: Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used. biocViews: SingleCell, RNASeq, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, ExomeSeq Author: Jeffrey Pullin [aut], Yuanhua Huang [aut], Davis McCarthy [aut, cre] Maintainer: Davis McCarthy URL: https://github.com/single-cell-genetics/cardelino VignetteBuilder: knitr BugReports: https://github.com/single-cell-genetics/cardelino/issues git_url: https://git.bioconductor.org/packages/cardelino git_branch: devel git_last_commit: a8b665e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cardelino_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cardelino_1.9.0.zip vignettes: vignettes/cardelino/inst/doc/vignette-cloneid.html vignetteTitles: Clone ID with cardelino hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cardelino/inst/doc/vignette-cloneid.R dependencyCount: 130 Package: Cardinal Version: 3.9.0 Depends: R (>= 4.4), BiocParallel, BiocGenerics, ProtGenerics, S4Vectors, methods, stats, stats4 Imports: CardinalIO, Biobase, EBImage, graphics, grDevices, irlba, Matrix, matter (>= 2.7.10), nlme, parallel, utils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: b81231f3abf7fed1186511c77fb6e0af NeedsCompilation: no Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/Cardinal/issues git_url: https://git.bioconductor.org/packages/Cardinal git_branch: devel git_last_commit: 8b2a58c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Cardinal_3.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cardinal_3.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cardinal_3.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cardinal_3.9.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal3-guide.html, vignettes/Cardinal/inst/doc/Cardinal3-stats.html vignetteTitles: 1. Cardinal 3: User guide for mass spectrometry imaging analysis, 2. Cardinal 3: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cardinal/inst/doc/Cardinal3-guide.R, vignettes/Cardinal/inst/doc/Cardinal3-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 66 Package: CardinalIO Version: 1.5.0 Depends: R (>= 4.4), BiocParallel, matter, ontologyIndex Imports: methods, S4Vectors, stats, utils, tools Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 24d27af8711f95c79bb491a4d0c1c463 NeedsCompilation: yes Title: Read and write mass spectrometry imaging files Description: Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging. biocViews: Software, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/CardinalIO/issues git_url: https://git.bioconductor.org/packages/CardinalIO git_branch: devel git_last_commit: 60442d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CardinalIO_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CardinalIO_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CardinalIO_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CardinalIO_1.5.0.tgz vignettes: vignettes/CardinalIO/inst/doc/CardinalIO-guide.html vignetteTitles: Parsing and writing imzML files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CardinalIO/inst/doc/CardinalIO-guide.R importsMe: Cardinal dependencyCount: 28 Package: CARNIVAL Version: 2.17.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson, rmarkdown Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0), sessioninfo License: GPL-3 MD5sum: 2ec647013758017a8d0417676a8cf481 NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga [aut] (ORCID: ), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: devel git_last_commit: 153557f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CARNIVAL_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CARNIVAL_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CARNIVAL_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CARNIVAL_2.17.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: Contextualizing large scale signalling networks from expression footprints with CARNIVAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR suggestsMe: dce dependencyCount: 64 Package: casper Version: 2.41.0 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: x64 MD5sum: ea9ea0ebb467878ab14d59cf70152ba4 NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: devel git_last_commit: d9348d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/casper_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/casper_2.41.0.zip vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 94 Package: CATALYST Version: 1.31.2 Depends: R (>= 4.4), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, Matrix, matrixStats, methods, nnls, purrr, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: 593f65337dc9985eb2cf90cac8f6ae0c NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. biocViews: Clustering, DataImport, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry,Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Vito R.T. Zanotelli [aut] (ORCID: ), Stéphane Chevrier [aut, dtc] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ), Bernd Bodenmiller [fnd] (ORCID: ) Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: devel git_last_commit: ada7c3c git_last_commit_date: 2024-11-28 Date/Publication: 2024-11-29 source.ver: src/contrib/CATALYST_1.31.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/CATALYST_1.31.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CATALYST_1.31.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CATALYST_1.31.2.tgz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: spillR, cytofWorkflow importsMe: cytofQC suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 179 Package: Category Version: 2.73.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 Archs: x64 MD5sum: 0c16729015a364a2bc797c3fded93722 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: devel git_last_commit: 1ae31f4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Category_2.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Category_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Category_2.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Category_2.73.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats importsMe: categoryCompare, GmicR, interactiveDisplay, meshr, miRLAB, phenoTest, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 61 Package: categoryCompare Version: 1.51.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter, rmarkdown License: GPL-2 MD5sum: 72c8c2eddca7f53c761eef32aa62692a NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: devel git_last_commit: 570b4b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/categoryCompare_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/categoryCompare_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/categoryCompare_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/categoryCompare_1.51.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 92 Package: CatsCradle Version: 1.1.0 Depends: R (>= 4.4.0) Imports: Seurat (>= 5.0.1), ggplot2, networkD3, stringr, pracma, reshape2, rdist, igraph, geometry, Rfast, data.table, abind, pheatmap, EBImage, S4Vectors, SeuratObject, SingleCellExperiment, SpatialExperiment, Matrix, methods, SummarizedExperiment, msigdbr Suggests: fossil, interp, knitr, BiocStyle, tictoc License: MIT + file LICENSE MD5sum: 664491efc8de03a84822e90cf43b45a8 NeedsCompilation: no Title: This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters Description: This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell, Transcriptomics, Spatial Author: Anna Laddach [aut] (ORCID: ), Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro URL: https://github.com/AnnaLaddach/CatsCradle VignetteBuilder: knitr BugReports: https://github.com/AnnaLaddach/CatsCradle/issues git_url: https://git.bioconductor.org/packages/CatsCradle git_branch: devel git_last_commit: 33deb7f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CatsCradle_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CatsCradle_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CatsCradle_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CatsCradle_1.1.0.tgz vignettes: vignettes/CatsCradle/inst/doc/CatsCradle.html, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.html, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.html vignetteTitles: CatsCradle, CatsCradle Example Data, CatsCradle Quick Start, CatsCradle SingleCellExperiment Quick Start, CatsCradle Spatial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CatsCradle/inst/doc/CatsCradle.R, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.R, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.R dependencyCount: 204 Package: CausalR Version: 1.39.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: bf244fa8787cc9dececec2abb351c8ba NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: devel git_last_commit: f417628 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CausalR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CausalR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CausalR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CausalR_1.39.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 17 Package: cbaf Version: 1.29.0 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx, zip Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1d5fd634d496cc654aed208ec315f350 NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, Microarray,ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: devel git_last_commit: 8845104 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cbaf_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cbaf_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cbaf_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cbaf_1.29.0.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 155 Package: cBioPortalData Version: 2.19.4 Depends: R (>= 4.2.0), AnVIL (>= 1.7.1), MultiAssayExperiment Imports: BiocBaseUtils, BiocFileCache (>= 1.5.3), digest, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 MD5sum: 5f3effab51e507b7b2f9ce22c5f02d92 NeedsCompilation: no Title: Exposes and Makes Available Data from the cBioPortal Web Resources Description: The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] (ORCID: ), Karim Mezhoud [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: devel git_last_commit: 2002dbf git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-10 source.ver: src/contrib/cBioPortalData_2.19.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/cBioPortalData_2.19.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cBioPortalData_2.19.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cBioPortalData_2.19.4.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html, vignettes/cBioPortalData/inst/doc/cgdsrMigration.html vignetteTitles: cBioPortalData User Guide, cBioPortal Data Build Errors, cBioPortal Developer Guide, cgdsr to cBioPortalData Migration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R, vignettes/cBioPortalData/inst/doc/cgdsrMigration.R dependsOnMe: bioCancer, canceR importsMe: cbaf, GNOSIS suggestsMe: OmicsMLRepoR dependencyCount: 143 Package: CBNplot Version: 1.7.0 Depends: R (>= 4.3.0) Imports: ggplot2, magrittr, graphite, ggraph, igraph, bnlearn (>= 4.7), patchwork, org.Hs.eg.db, clusterProfiler, utils, enrichplot, reshape2, ggforce, dplyr, tidyr, stringr, depmap, ExperimentHub, Rmpfr, graphlayouts, BiocFileCache, ggdist, purrr, pvclust, stats, rlang Suggests: knitr, arules, concaveman, ReactomePA, bnviewer, rmarkdown, withr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 32889a2dd375ebbefbbb69323024fa5e NeedsCompilation: no Title: plot bayesian network inferred from gene expression data based on enrichment analysis results Description: This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized. biocViews: Visualization, Bayesian, GeneExpression, NetworkInference, Pathways, Reactome, Network, NetworkEnrichment, GeneSetEnrichment Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/CBNplot VignetteBuilder: knitr BugReports: https://github.com/noriakis/CBNplot/issues git_url: https://git.bioconductor.org/packages/CBNplot git_branch: devel git_last_commit: 5dcddfc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CBNplot_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CBNplot_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CBNplot_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CBNplot_1.7.0.tgz vignettes: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.html vignetteTitles: CBNplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.R dependencyCount: 152 Package: cbpManager Version: 1.15.0 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, rlang, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 6580ed7b3e0bfe90a16f4ae66b2e49f4 NeedsCompilation: no Title: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics Description: This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients. biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI, ThirdPartyClient, Preprocessing, Visualization Author: Arsenij Ustjanzew [aut, cre, cph] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Arsenij Ustjanzew URL: https://arsenij-ust.github.io/cbpManager/index.html VignetteBuilder: knitr BugReports: https://github.com/arsenij-ust/cbpManager/issues git_url: https://git.bioconductor.org/packages/cbpManager git_branch: devel git_last_commit: fd4e30c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cbpManager_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cbpManager_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cbpManager_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cbpManager_1.15.0.tgz vignettes: vignettes/cbpManager/inst/doc/intro.html vignetteTitles: intro.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cbpManager/inst/doc/intro.R dependencyCount: 89 Package: ccfindR Version: 1.27.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 976fbc36a6f78b10a00296780d68b2f0 NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: devel git_last_commit: 8f11dda git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ccfindR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccfindR_1.27.0.zip vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 51 Package: ccImpute Version: 1.9.0 Imports: Rcpp, sparseMatrixStats, stats, BiocParallel, irlba, SingleCellExperiment, Matrix, SummarizedExperiment LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, mclust, testthat (>= 3.0.0), splatter License: GPL-3 MD5sum: e9dbeeb9bab1f5880eba31680b89b879 NeedsCompilation: yes Title: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8) Description: Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. biocViews: SingleCell, Sequencing, PrincipalComponent, DimensionReduction, Clustering, RNASeq, Transcriptomics Author: Marcin Malec [cre, aut] (ORCID: ), Parichit Sharma [aut] (ORCID: ), Hasan Kurban [aut] (ORCID: ), Mehmet Dalkilic [aut] Maintainer: Marcin Malec URL: https://github.com/khazum/ccImpute/ VignetteBuilder: knitr BugReports: https://github.com/khazum/ccImpute/issues git_url: https://git.bioconductor.org/packages/ccImpute git_branch: devel git_last_commit: 11f4990 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ccImpute_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccImpute_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ccImpute_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ccImpute_1.9.0.tgz vignettes: vignettes/ccImpute/inst/doc/ccImpute.html vignetteTitles: ccImpute package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccImpute/inst/doc/ccImpute.R dependencyCount: 52 Package: ccmap Version: 1.33.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE Archs: x64 MD5sum: 282fad9f0e9ea248e78b7a64c78d4ca1 NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: devel git_last_commit: 5eec3ab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ccmap_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccmap_1.33.0.zip vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R dependencyCount: 60 Package: CCPlotR Version: 1.5.0 Imports: plyr, tidyr, dplyr, ggplot2, forcats, ggraph, igraph, scatterpie, circlize, ComplexHeatmap, tibble, grid, ggbump, stringr, ggtext, ggh4x, patchwork, RColorBrewer, scales, viridis, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: c60aa852df6f1e4bec5b2ef92f860da4 NeedsCompilation: no Title: Plots For Visualising Cell-Cell Interactions Description: CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc. biocViews: SingleCell, Network, Visualization, CellBiology, SystemsBiology Author: Sarah Ennis [aut, cre] (ORCID: ), Pilib Ó Broin [aut], Eva Szegezdi [aut] Maintainer: Sarah Ennis URL: https://github.com/Sarah145/CCPlotR VignetteBuilder: knitr BugReports: https://github.com/Sarah145/CCPlotR/issues git_url: https://git.bioconductor.org/packages/CCPlotR git_branch: devel git_last_commit: c2f2f50 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CCPlotR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CCPlotR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CCPlotR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CCPlotR_1.5.0.tgz vignettes: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.html vignetteTitles: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.R dependencyCount: 99 Package: CCPROMISE Version: 1.33.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 5648a2e22e45edc3ecc8dee5d7a94f54 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: devel git_last_commit: 941008a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CCPROMISE_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CCPROMISE_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CCPROMISE_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CCPROMISE_1.33.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 53 Package: ccrepe Version: 1.43.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat, RUnit License: MIT + file LICENSE MD5sum: d0b6bd3c7bc4bde3a9c4283e0e0da0d9 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: devel git_last_commit: defe8c1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ccrepe_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ccrepe_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ccrepe_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ccrepe_1.43.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: CDI Version: 1.5.0 Depends: R(>= 3.6) Imports: matrixStats, Seurat, SeuratObject, stats, BiocParallel, ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment, SummarizedExperiment, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, magick, BiocStyle License: GPL-3 + file LICENSE MD5sum: 74a2766784769e03adbc1a581b0bbd7a NeedsCompilation: no Title: Clustering Deviation Index (CDI) Description: Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells. biocViews: SingleCell, Software, Clustering, Visualization, Sequencing, RNASeq, CellBasedAssays Author: Jiyuan Fang [cre, aut] (ORCID: ), Jichun Xie [ctb], Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb], Diyuan Qin [ctb], Qi-Jing Li [ctb], Jichun Xie [ctb] Maintainer: Jiyuan Fang URL: https://github.com/jichunxie/CDI VignetteBuilder: knitr BugReports: https://github.com/jichunxie/CDI/issues git_url: https://git.bioconductor.org/packages/CDI git_branch: devel git_last_commit: 82b7486 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CDI_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CDI_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CDI_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CDI_1.5.0.tgz vignettes: vignettes/CDI/inst/doc/CDI.html vignetteTitles: Clustering Deviation Index (CDI) Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CDI/inst/doc/CDI.R dependencyCount: 179 Package: celaref Version: 1.25.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: 694bb2a78f54ff36d76fd76f7ff7c627 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: devel git_last_commit: 34ca877 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/celaref_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/celaref_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/celaref_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/celaref_1.25.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 84 Package: celda Version: 1.21.0 Depends: R (>= 4.0), SingleCellExperiment, Matrix Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, scater (>= 1.14.4), scran, dbscan, DelayedArray, stringr, ComplexHeatmap, gridExtra, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE MD5sum: 251f04b5857b7945db8e5d6c8b4b6a70 NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian, ImmunoOncology, DataImport Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut], Sean Corbett [aut], Yusuke Koga [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: devel git_last_commit: 705c668 git_last_commit_date: 2024-04-30 Date/Publication: 2024-10-25 source.ver: src/contrib/celda_1.21.0.tar.gz vignettes: vignettes/celda/inst/doc/celda.html, vignettes/celda/inst/doc/decontX.html vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: decontX, singleCellTK dependencyCount: 137 Package: CellBarcode Version: 1.13.1 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr, Rsamtools LinkingTo: Rcpp, BH Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: Artistic-2.0 MD5sum: 375b86dbc3e3db27b533899fc6cfced6 NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre, aut] (ORCID: ), Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun URL: https://wenjie1991.github.io/CellBarcode/ VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/CellBarcode/issues git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: devel git_last_commit: f32bb33 git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-16 source.ver: src/contrib/CellBarcode_1.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellBarcode_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellBarcode_1.13.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellBarcode_1.13.1.tgz vignettes: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.html, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: 10X_Barcode, UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.R, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 103 Package: cellbaseR Version: 1.31.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: f815692fd7071cffe16bdd4e672e1a89 NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: devel git_last_commit: 76824da git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cellbaseR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellbaseR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellbaseR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellbaseR_1.31.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 66 Package: CellBench Version: 1.23.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: assertthat, BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 Archs: x64 MD5sum: f307b90e08b6a67eece0e6acbcf2b3b2 NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure, SingleCell Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: devel git_last_commit: 65e4041 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellBench_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellBench_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellBench_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellBench_1.23.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/TidyversePatterns.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Introduction, Tidyverse Patterns, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral, speckle dependencyCount: 82 Package: CelliD Version: 1.15.0 Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater, stringr, irlba, data.table, glue, pbapply, umap, Rtsne, reticulate, fastmatch, matrixStats, ggplot2, BiocParallel, SummarizedExperiment, fgsea LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr, destiny, ggrepel License: GPL-3 + file LICENSE MD5sum: 8f30993883fc40954f998e189f5e31a2 NeedsCompilation: yes Title: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis Description: CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data. biocViews: RNASeq, SingleCell, DimensionReduction, Clustering, GeneSetEnrichment, GeneExpression, ATACSeq Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb] Maintainer: Akira Cortal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CelliD git_branch: devel git_last_commit: af4d3e3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CelliD_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CelliD_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CelliD_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CelliD_1.15.0.tgz vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html vignetteTitles: CelliD Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R dependencyCount: 201 Package: cellity Version: 1.35.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: bef43724dd0edf1636ca6ad6f1cea0b6 NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: devel git_last_commit: 614b66c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cellity_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellity_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellity_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellity_1.35.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 83 Package: CellMapper Version: 1.33.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 Archs: x64 MD5sum: fe5dab5461e3585f50d53bbb6470a492 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: devel git_last_commit: 5a2c66b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellMapper_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellMapper_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellMapper_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellMapper_1.33.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 8 Package: cellmigRation Version: 1.15.0 Depends: R (>= 4.1), methods, foreach Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel, grDevices, matrixStats, FME, SpatialTools, sp, vioplot, FactoMineR, Hmisc Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics, BiocManager, kableExtra, rgl License: GPL-2 MD5sum: 871eaa3d8450f20a1901eb12c9a7f6b5 NeedsCompilation: no Title: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics Description: Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions. biocViews: CellBiology, DataRepresentation, DataImport Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir Leoncio [cre, aut], Øystein Sørensen [aut] Maintainer: Waldir Leoncio URL: https://github.com/ocbe-uio/cellmigRation/ VignetteBuilder: knitr BugReports: https://github.com/ocbe-uio/cellmigRation/issues git_url: https://git.bioconductor.org/packages/cellmigRation git_branch: devel git_last_commit: 16757c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cellmigRation_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellmigRation_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellmigRation_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellmigRation_1.15.0.tgz vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html vignetteTitles: cellmigRation hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R dependencyCount: 138 Package: CellMixS Version: 1.23.0 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: 6afeca36732eaefa9ff7dc97c945651a NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: devel git_last_commit: 0d878da git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellMixS_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellMixS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellMixS_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellMixS_1.23.0.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 117 Package: CellNOptR Version: 1.53.0 Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML, ggplot2, rmarkdown Imports: igraph, stringi, stringr Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics, Enhances: doParallel, foreach License: GPL-3 MD5sum: 6b1089bb60cb261cf36c16d4bc5d8f6c NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara [aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: devel git_last_commit: edc045a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellNOptR_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellNOptR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellNOptR_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellNOptR_1.53.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html vignetteTitles: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode importsMe: bnem suggestsMe: MEIGOR dependencyCount: 71 Package: cellscape Version: 1.31.0 Depends: R (>= 3.3) Imports: dplyr (>= 0.4.3), gtools (>= 3.5.0), htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: e51470c789844af8f45a1ea0d155a384 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Shixiang Wang [aut, cre] (ORCID: ), Maia Smith [aut] Maintainer: Shixiang Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: devel git_last_commit: 1a8351b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cellscape_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellscape_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellscape_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellscape_1.31.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 50 Package: CellScore Version: 1.27.0 Depends: R (>= 4.3.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0), SummarizedExperiment Suggests: hgu133plus2CellScore, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 4464713764559ce74fe042e27133ba3a NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah [aut, cre], Katerina Taskova [aut], Justin Marsh [aut] Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: devel git_last_commit: 9e8a1d2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellScore_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellScore_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellScore_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellScore_1.27.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R suggestsMe: homosapienDEE2CellScore dependencyCount: 46 Package: CellTrails Version: 1.25.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 4779e915b71aef01628b8d5e579ad7d7 NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: devel git_last_commit: b064f50 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CellTrails_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CellTrails_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CellTrails_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CellTrails_1.25.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 85 Package: cellxgenedp Version: 1.11.0 Depends: dplyr Imports: httr, curl, utils, tools, cli, shiny, DT, rjsoncons Suggests: zellkonverter, SingleCellExperiment, HDF5Array, tidyr, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 MD5sum: 1161a0d753031ead9908340f785b93c5 NeedsCompilation: no Title: Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal Description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading. biocViews: SingleCell, DataImport, ThirdPartyClient Author: Martin Morgan [aut, cre] (ORCID: ), Kayla Interdonato [aut] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/cellxgenedp/, https://github.com/mtmorgan/cellxgenedp VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/cellxgenedp/issues git_url: https://git.bioconductor.org/packages/cellxgenedp git_branch: devel git_last_commit: c17b43e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cellxgenedp_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cellxgenedp_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cellxgenedp_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cellxgenedp_1.11.0.tgz vignettes: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.html, vignettes/cellxgenedp/inst/doc/b_case_studies.html vignetteTitles: Discovery and retrieval, Case studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.R, vignettes/cellxgenedp/inst/doc/b_case_studies.R dependencyCount: 63 Package: CEMiTool Version: 1.31.0 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: 44ee1f61bab5626fc0f0a6983218540f NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: devel git_last_commit: 3dd9735 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/CEMiTool_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CEMiTool_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CEMiTool_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CEMiTool_1.31.0.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 193 Package: censcyt Version: 1.15.0 Depends: R (>= 4.0), diffcyt Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR, fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp, purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment, survival, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2 License: MIT + file LICENSE MD5sum: e5461b6d2f9a4598ea9bde932891c747 NeedsCompilation: no Title: Differential abundance analysis with a right censored covariate in high-dimensional cytometry Description: Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software, Survival Author: Reto Gerber [aut, cre] (ORCID: ) Maintainer: Reto Gerber URL: https://github.com/retogerber/censcyt VignetteBuilder: knitr BugReports: https://github.com/retogerber/censcyt/issues git_url: https://git.bioconductor.org/packages/censcyt git_branch: devel git_last_commit: 22cee1d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/censcyt_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/censcyt_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/censcyt_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/censcyt_1.15.0.tgz vignettes: vignettes/censcyt/inst/doc/censored_covariate.html vignetteTitles: Censored covariate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R dependencyCount: 179 Package: Cepo Version: 1.13.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr, purrr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap License: MIT + file LICENSE MD5sum: 9d149c1c4b3d3d15fe020c4c6f9f350a NeedsCompilation: no Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (ORCID: ), Kevin Wang [aut] (ORCID: ) Maintainer: Hani Jieun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cepo git_branch: devel git_last_commit: 5cb935f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Cepo_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cepo_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cepo_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cepo_1.13.0.tgz vignettes: vignettes/Cepo/inst/doc/cepo.html vignetteTitles: Cepo method for differential stability analysis of scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cepo/inst/doc/cepo.R importsMe: scClassify dependencyCount: 107 Package: ceRNAnetsim Version: 1.19.0 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) MD5sum: 56b8a4b953f86d03c4cc8abf8b99d37e NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (ORCID: ), Alper Yilmaz [aut] (ORCID: ) Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: devel git_last_commit: 1046dbf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ceRNAnetsim_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ceRNAnetsim_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ceRNAnetsim_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ceRNAnetsim_1.19.0.tgz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 69 Package: CeTF Version: 1.19.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils, methods LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 MD5sum: 51c52b25f8d3799f875c97d8490a5eea NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev, gfortran, build-essential, libz-dev, zlib1g-dev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: devel git_last_commit: 759b9e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CeTF_1.19.0.tar.gz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 205 Package: CexoR Version: 1.45.0 Depends: R (>= 4.2.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 00e2267743777706e78422dc6b12ab36 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed. biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq, PeakDetection Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: devel git_last_commit: bc1c514 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CexoR_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CexoR_1.45.0.zip vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 109 Package: CFAssay Version: 1.41.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 2563526d2bc93f18eadda8a99991a572 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: devel git_last_commit: c9b6501 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CFAssay_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CFAssay_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CFAssay_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CFAssay_1.41.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfdnakit Version: 1.5.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, GenomicRanges, GenomeInfoDb, ggplot2, IRanges, magrittr, PSCBS, QDNAseq, Rsamtools, utils, S4Vectors, stats, rlang Suggests: rmarkdown, knitr, roxygen2, BiocStyle License: GPL-3 MD5sum: 4c99e84a52eb8c2e6c4fa21882f2d009 NeedsCompilation: no Title: Fragmen-length analysis package from high-throughput sequencing of cell-free DNA (cfDNA) Description: This package provides basic functions for analyzing shallow whole-genome sequencing (~0.3X or more) of cell-free DNA (cfDNA). The package basically extracts the length of cfDNA fragments and aids the vistualization of fragment-length information. The package also extract fragment-length information per non-overlapping fixed-sized bins and used it for calculating ctDNA estimation score (CES). biocViews: CopyNumberVariation, Sequencing, WholeGenome Author: Pitithat Puranachot [aut, cre] (ORCID: ) Maintainer: Pitithat Puranachot VignetteBuilder: knitr BugReports: https://github.com/Pitithat-pu/cfdnakit/issues git_url: https://git.bioconductor.org/packages/cfdnakit git_branch: devel git_last_commit: 889853b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cfdnakit_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cfdnakit_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cfdnakit_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cfdnakit_1.5.0.tgz vignettes: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.html vignetteTitles: cfdnakit vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.R dependencyCount: 94 Package: cfDNAPro Version: 1.13.0 Depends: R (>= 4.1.0), magrittr (>= 1.5.0) Imports: tibble, GenomicAlignments, IRanges, plyranges, GenomeInfoDb, GenomicRanges, BiocGenerics, stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0), BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.NCBI.GRCh38 Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: 91fabf11b956cfedf9eae31c9ad8e209 NeedsCompilation: no Title: cfDNAPro extracts and Visualises biological features from whole genome sequencing data of cell-free DNA Description: cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Elkie Chan [ctb], Christopher Smith [ctb], Tomer Kaplan [ctb], Florian Markowetz [ctb], Nitzan Rosenfeld [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: devel git_last_commit: 384ebef git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cfDNAPro_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cfDNAPro_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cfDNAPro_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cfDNAPro_1.13.0.tgz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 98 Package: cfTools Version: 1.7.0 Imports: Rcpp, utils, GenomicRanges, basilisk, R.utils, stats, cfToolsData LinkingTo: Rcpp, BH Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: file LICENSE MD5sum: 6070453de266f0287ee98d983ef5e7fb NeedsCompilation: yes Title: Informatics Tools for Cell-Free DNA Study Description: The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring. biocViews: Software, BiomedicalInformatics, Epigenetics, Sequencing, MethylSeq, DNAMethylation, DifferentialMethylation Author: Ran Hu [aut, cre] (ORCID: ), Mary Louisa Stackpole [aut] (ORCID: ), Shuo Li [aut] (ORCID: ), Xianghong Jasmine Zhou [aut] (ORCID: ), Wenyuan Li [aut] (ORCID: ) Maintainer: Ran Hu URL: https://github.com/jasminezhoulab/cfTools VignetteBuilder: knitr BugReports: https://github.com/jasminezhoulab/cfTools/issues git_url: https://git.bioconductor.org/packages/cfTools git_branch: devel git_last_commit: f90509a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cfTools_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cfTools_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cfTools_1.7.0.tgz vignettes: vignettes/cfTools/inst/doc/cfTools-vignette.html vignetteTitles: cfTools-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cfTools/inst/doc/cfTools-vignette.R dependencyCount: 85 Package: CGEN Version: 3.43.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE MD5sum: 1f63591c22936fbfbdbf7e798d17e6b1 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population. biocViews: SNP, MultipleComparison, Clustering Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut], Summer Han [aut], Minsun Song [aut], William Wheeler [aut], Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin Lee [cre] Maintainer: Justin Lee git_url: https://git.bioconductor.org/packages/CGEN git_branch: devel git_last_commit: a8ad56a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CGEN_3.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGEN_3.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGEN_3.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGEN_3.43.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.67.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: b2643d0f0cf99876157e7d37430d055f NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: devel git_last_commit: 2905ae7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CGHbase_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHbase_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHbase_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHbase_1.67.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 11 Package: CGHcall Version: 2.69.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) Archs: x64 MD5sum: e5d716b07e22c726e0e1f9347358daf6 NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: devel git_last_commit: 3089e44 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CGHcall_2.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHcall_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHcall_2.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHcall_2.69.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 16 Package: cghMCR Version: 1.65.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 1f74e0e05ef75f8825996c329c3d3e51 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: devel git_last_commit: 7280ca7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cghMCR_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cghMCR_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cghMCR_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cghMCR_1.65.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 61 Package: CGHnormaliter Version: 1.61.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: de5c2c8249ada1c4efc18e6e7640369f NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: devel git_last_commit: bb90e82 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CGHnormaliter_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHnormaliter_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHnormaliter_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHnormaliter_1.61.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 17 Package: CGHregions Version: 1.65.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 4e2a33ec6e572cf9f80f4a38008a52e1 NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: devel git_last_commit: 39db6ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CGHregions_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CGHregions_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CGHregions_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CGHregions_1.65.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 12 Package: ChAMP Version: 2.37.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: c8f8d3363cccfce3befdbc7fc7e3ae9f NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: devel git_last_commit: bebfd7d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChAMP_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChAMP_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChAMP_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChAMP_2.37.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R suggestsMe: GeoTcgaData dependencyCount: 264 Package: ChemmineOB Version: 1.45.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp, zlibbioc Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown,RUnit,codetools Enhances: ChemmineR (>= 2.13.0) License: Artistic-2.0 MD5sum: e97b4cd1d3b03fdeef965200bf5b1671 NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: devel git_last_commit: e269adf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChemmineOB_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChemmineOB_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineOB_1.45.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R suggestsMe: MetMashR dependencyCount: 9 Package: ChemmineR Version: 3.59.0 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg,jsonlite,stringi LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager,bibtex,codetools Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 3133086ed179214c122bd7385810c172 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: devel git_last_commit: ee3bf9e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChemmineR_3.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChemmineR_3.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineR_3.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChemmineR_3.59.0.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID, RMassBank, chemodiv, DrugSim2DR, DRviaSPCN suggestsMe: ChemmineOB, xnet dependencyCount: 76 Package: CHETAH Version: 1.23.0 Depends: R (>= 4.2), ggplot2, SingleCellExperiment Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE Archs: x64 MD5sum: f3db529da2e143cb8ea256bccca9345b NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering, GeneExpression, ImmunoOncology Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: devel git_last_commit: 2fa43fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CHETAH_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CHETAH_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CHETAH_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CHETAH_1.23.0.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R suggestsMe: adverSCarial dependencyCount: 114 Package: Chicago Version: 1.35.0 Depends: R (>= 3.3.1), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 Archs: x64 MD5sum: bfdeab4d702443640822233982b39ede NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: devel git_last_commit: eb0001c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Chicago_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Chicago_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Chicago_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Chicago_1.35.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 76 Package: chihaya Version: 1.7.0 Depends: DelayedArray Imports: methods, Matrix, rhdf5, Rcpp, HDF5Array LinkingTo: Rcpp, Rhdf5lib Suggests: BiocGenerics, S4Vectors, BiocSingular, ResidualMatrix, BiocStyle, testthat, rmarkdown, knitr License: GPL-3 MD5sum: 159875e93617c25b9e318d0f057074f9 NeedsCompilation: yes Title: Save Delayed Operations to a HDF5 File Description: Saves the delayed operations of a DelayedArray to a HDF5 file. This enables efficient recovery of the DelayedArray's contents in other languages and analysis frameworks. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/chihaya-R SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/chihaya-R/issues git_url: https://git.bioconductor.org/packages/chihaya git_branch: devel git_last_commit: e02b134 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chihaya_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chihaya_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chihaya_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chihaya_1.7.0.tgz vignettes: vignettes/chihaya/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chihaya/inst/doc/userguide.R suggestsMe: alabaster.matrix dependencyCount: 28 Package: chimeraviz Version: 1.33.0 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, checkmate, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 30764fe009171508fd159bddae61af08 NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: devel git_last_commit: 3819c30 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chimeraviz_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chimeraviz_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chimeraviz_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chimeraviz_1.33.0.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 173 Package: ChIPanalyser Version: 1.29.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb,RColorBrewer Suggests: BSgenome.Dmelanogaster.UCSC.dm6,knitr, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: e61b2fa82d18772668b0749e87e56fd2 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: devel git_last_commit: 0c32095 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPanalyser_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPanalyser_1.29.0.zip vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide, ChIPanalyser User's Guide for Genetic Algorithms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.R dependencyCount: 69 Package: ChIPComp Version: 1.37.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL MD5sum: 793992737316c9e39138fab2afe340da NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: devel git_last_commit: 3fbdb06 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPComp_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPComp_1.37.0.zip vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 64 Package: chipenrich Version: 2.31.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: f794ecb39f9255bd06d657f5fb6750d1 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Kai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: devel git_last_commit: ae52d55 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chipenrich_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chipenrich_2.31.0.zip vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 160 Package: ChIPexoQual Version: 1.31.0 Depends: R (>= 3.5.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) Archs: x64 MD5sum: 9c367d7079918d30c0005d89167c99a8 NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: devel git_last_commit: 7b88583 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPexoQual_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPexoQual_1.31.0.zip vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 143 Package: ChIPpeakAnno Version: 3.41.0 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), pwalign, DBI, dplyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils, universalmotif, stringr, tibble, tidyr, data.table, scales, ensembldb Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, reshape2, testthat, trackViewer, motifStack, OrganismDbi, BiocFileCache License: GPL (>= 2) MD5sum: e640b27b88ae424410e1785bc7dcfd8c NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data Description: The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Junhui Li, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe, Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu , Kai Hu , Junhui Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: devel git_last_commit: 0f4b7da git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPpeakAnno_3.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPpeakAnno_3.41.0.zip vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html vignetteTitles: ChIPpeakAnno: annotate,, visualize,, and compare peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R dependsOnMe: REDseq, csawBook importsMe: ATACseqQC, DEScan2, GUIDEseq suggestsMe: hicVennDiagram, R3CPET, seqsetvis, chipseqDB dependencyCount: 132 Package: ChIPQC Version: 1.43.0 Depends: R (>= 3.5.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19), BiocParallel Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: e4b789f16f8ef5bc1eab4a88f48ed0ea NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: devel git_last_commit: a673e8a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPQC_1.43.0.tar.gz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R dependencyCount: 168 Package: ChIPseeker Version: 1.43.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, aplot, BiocGenerics, boot, dplyr, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, magrittr, methods, plotrix, parallel, RColorBrewer, rlang, rtracklayer, S4Vectors, scales, stats, tibble, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, yulab.utils (>= 0.1.5) Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram, knitr, org.Hs.eg.db, prettydoc, ReactomePA, rmarkdown, testthat License: Artistic-2.0 MD5sum: f097ad1561aeb255ae9c69c8d723337e NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (ORCID: ), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: devel git_last_commit: 9e54d40 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPseeker_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPseeker_1.43.0.zip vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: EpiCompare, esATAC, segmenter, TCGAWorkflow, cinaR suggestsMe: GRaNIE, curatedAdipoChIP dependencyCount: 149 Package: chipseq Version: 1.57.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm9, BiocStyle, knitr License: Artistic-2.0 MD5sum: 402e5d5b322250f32af792ee6ee59610 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments. biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar [aut], Robert Gentleman [aut], Michael Lawrence [aut], Zizhen Yao [aut], Oluwabukola Bamigbade [ctb] (Converted vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseq git_branch: devel git_last_commit: 205b179 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chipseq_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chipseq_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chipseq_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chipseq_1.57.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.html vignetteTitles: Some Basic Analysis of ChIP-Seq Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: ChIPQC, soGGi, transcriptR dependencyCount: 64 Package: ChIPseqR Version: 1.61.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) MD5sum: 868c03512c0bb7ec22cba01829e214cb NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: devel git_last_commit: 87a6195 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPseqR_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPseqR_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPseqR_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPseqR_1.61.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 72 Package: ChIPsim Version: 1.61.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) Archs: x64 MD5sum: 0d9c7db033dd65845d01914a8a18eb4d NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: devel git_last_commit: 267ec77 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPsim_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChIPsim_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ChIPsim_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ChIPsim_1.61.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 64 Package: ChIPXpress Version: 1.51.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 8ae3ba698c40243f61d077e750666606 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: devel git_last_commit: 498fd5f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChIPXpress_1.51.0.tar.gz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 109 Package: chopsticks Version: 1.73.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: 82f057035f1f0859ae28234b717822fd NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: devel git_last_commit: a2c9896 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chopsticks_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chopsticks_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chopsticks_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chopsticks_1.73.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R dependencyCount: 10 Package: chromDraw Version: 2.37.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: 5f93c5a09f5cd2abfb7d219e60b90bc9 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: devel git_last_commit: c40d500 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chromDraw_2.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chromDraw_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chromDraw_2.37.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 25 Package: ChromHeatMap Version: 1.61.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: e312d4f15f5d76fe08b57ce786ebb15b NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: devel git_last_commit: 31693e7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChromHeatMap_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChromHeatMap_1.61.0.zip vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 79 Package: chromPlot Version: 1.35.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: x64 MD5sum: f26e33a1db8bda1836f0546bf2c16afa NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: devel git_last_commit: df675e7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chromPlot_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chromPlot_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/chromPlot_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/chromPlot_1.35.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 72 Package: ChromSCape Version: 1.17.0 Depends: R (>= 4.1) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, ggrepel, gggenes, gridExtra, qualV, stringdist, stringr, fs, qs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future, igraph, bluster, httr License: GPL-3 MD5sum: 2109a7c4024b47af14005ee54029d091 NeedsCompilation: yes Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: ShinyApps, Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (ORCID: ), Celine Vallot [aut] (ORCID: ) Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: devel git_last_commit: 237c895 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ChromSCape_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ChromSCape_1.17.0.zip vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 211 Package: chromVAR Version: 1.29.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE MD5sum: 5fd9aa5a2ca3308e476c46ce6f3e8127 NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: devel git_last_commit: 261e3b9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/chromVAR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/chromVAR_1.29.0.zip vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R suggestsMe: MOCHA, Signac dependencyCount: 162 Package: CHRONOS Version: 1.35.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 09dce8670ffea5e682a23cafa15e6b23 NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: devel git_last_commit: 630e48d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CHRONOS_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CHRONOS_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CHRONOS_1.35.0.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 92 Package: cicero Version: 1.25.0 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE Archs: x64 MD5sum: 94a8f62d6981454412a47d08ff7b62de NeedsCompilation: no Title: Predict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: devel git_last_commit: ff77b2c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cicero_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cicero_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cicero_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cicero_1.25.0.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R dependencyCount: 183 Package: CIMICE Version: 1.15.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat, tidygraph, expm, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 Archs: x64 MD5sum: ae5498d2cf9b8186a417e1ba0260f1a9 NeedsCompilation: no Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution Description: CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction. biocViews: Software, BiologicalQuestion, NetworkInference, ResearchField, Phylogenetics, StatisticalMethod, GraphAndNetwork, Technology, SingleCell Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and Bioinformatics, Department of Mathematics, Computer Science and Physics, University of Udine, ORCID: ) Maintainer: Nicolò Rossi URL: https://github.com/redsnic/CIMICE VignetteBuilder: knitr BugReports: https://github.com/redsnic/CIMICE/issues git_url: https://git.bioconductor.org/packages/CIMICE git_branch: devel git_last_commit: 93f6739 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CIMICE_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CIMICE_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CIMICE_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CIMICE_1.15.0.tgz vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html, vignettes/CIMICE/inst/doc/CIMICER.html vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer Cancer Evolution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R, vignettes/CIMICE/inst/doc/CIMICER.R dependencyCount: 94 Package: CINdex Version: 1.35.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens, R.utils License: GPL (>= 2) MD5sum: e5325bc0639413880ef4de1bbae3ee7a NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Krithika Bhuvaneshwar [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yue Wang [aut, ths] (Virginia Polytechnic Institute and State University), Yuanjian Feng [aut] (Virginia Polytechnic Institute and State University), Ie-Ming Shih [aut] (Johns Hopkins University School of Medicine), Subha Madhavan [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yuriy Gusev [aut, cre] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center) Maintainer: Yuriy Gusev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: devel git_last_commit: 5f3d72a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CINdex_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CINdex_1.35.0.zip vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, How to obtain Cytoband and Stain Information, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.R, vignettes/CINdex/inst/doc/PrepareInputData.R dependencyCount: 51 Package: circRNAprofiler Version: 1.21.0 Depends: R(>= 4.3.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: 9492d376fa03e7d5f20d7063592ddf81 NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: devel git_last_commit: 1018c6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/circRNAprofiler_1.21.0.tar.gz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 143 Package: CircSeqAlignTk Version: 1.9.0 Depends: R (>= 4.2) Imports: stats, tools, utils, R.utils, methods, S4Vectors, rlang, magrittr, dplyr, tidyr, ggplot2, BiocGenerics, Biostrings, IRanges, ShortRead, Rsamtools, Rbowtie2, Rhisat2, shiny, shinyFiles, shinyjs, plotly, parallel, htmltools Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 4ff40c96a2e5525f1a7df9e4435d8133 NeedsCompilation: no Title: A toolkit for end-to-end analysis of RNA-seq data for circular genomes Description: CircSeqAlignTk is designed for end-to-end RNA-Seq data analysis of circular genome sequences, from alignment to visualization. It mainly targets viroids which are composed of 246-401 nt circular RNAs. In addition, CircSeqAlignTk implements a tidy interface to generate synthetic sequencing data that mimic real RNA-Seq data, allowing developers to evaluate the performance of alignment tools and workflows. biocViews: Sequencing, SmallRNA, Alignment, Software Author: Jianqiang Sun [cre, aut] (ORCID: ), Xi Fu [ctb], Wei Cao [ctb] Maintainer: Jianqiang Sun URL: https://github.com/jsun/CircSeqAlignTk VignetteBuilder: knitr BugReports: https://github.com/jsun/CircSeqAlignTk/issues git_url: https://git.bioconductor.org/packages/CircSeqAlignTk git_branch: devel git_last_commit: c5921ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CircSeqAlignTk_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CircSeqAlignTk_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CircSeqAlignTk_1.9.0.tgz vignettes: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.html vignetteTitles: CircSeqAlignTk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.R dependencyCount: 160 Package: cisPath Version: 1.47.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) MD5sum: b629b7b1e2616a5f711e5e8f9432a791 NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: devel git_last_commit: 7d721c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cisPath_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cisPath_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cisPath_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cisPath_1.47.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R dependencyCount: 2 Package: CiteFuse Version: 1.19.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang, Rcpp, compositions LinkingTo: Rcpp Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 5b22f8d54bdca84382fa5ed9c93e3ebc NeedsCompilation: yes Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: devel git_last_commit: 699f98c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CiteFuse_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CiteFuse_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CiteFuse_1.19.0.tgz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R suggestsMe: MuData dependencyCount: 163 Package: ClassifyR Version: 3.11.4 Depends: R (>= 4.1.0), generics, methods, S4Vectors, MultiAssayExperiment, BiocParallel, survival Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger, ggplot2 (>= 3.0.0), ggpubr, reshape2, ggupset Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC, MatrixModels, xgboost, data.tree, ggnewscale License: GPL-3 MD5sum: 749a1d9f793c9977bf3bfcf2efda759e NeedsCompilation: yes Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac [aut, cre], Ellis Patrick [aut], Sourish Iyengar [aut], Harry Robertson [aut], Andy Tran [aut], John Ormerod [aut], Graham Mann [aut], Jean Yang [aut] Maintainer: Dario Strbenac URL: https://sydneybiox.github.io/ClassifyR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: devel git_last_commit: c1585ac git_last_commit_date: 2024-12-06 Date/Publication: 2024-12-06 source.ver: src/contrib/ClassifyR_3.11.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClassifyR_3.11.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClassifyR_3.11.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClassifyR_3.11.4.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/DevelopersGuide.html vignetteTitles: An Introduction to the ClassifyR Package, Developer's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/DevelopersGuide.R importsMe: spicyR, TOP suggestsMe: scFeatures, Statial, spicyWorkflow dependencyCount: 133 Package: cleanUpdTSeq Version: 1.45.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, GenomeInfoDb, IRanges, utils, stringr, stats, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 Archs: x64 MD5sum: ae596e9b89532acd56c89b8307a33ede NeedsCompilation: no Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data Description: This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods. biocViews: Sequencing, 3' end sequencing, polyadenylation site, internal priming Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: devel git_last_commit: 0239c1b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cleanUpdTSeq_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cleanUpdTSeq_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cleanUpdTSeq_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cleanUpdTSeq_1.45.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R dependencyCount: 81 Package: CleanUpRNAseq Version: 1.1.1 Depends: R (>= 4.4.0) Imports: AnnotationFilter, BiocGenerics, Biostrings, BSgenome, DESeq2, edgeR, ensembldb, GenomeInfoDb, GenomicRanges, ggplot2, ggrepel, graphics, grDevices, KernSmooth, limma, methods, pheatmap, qsmooth, R6, RColorBrewer, Rsamtools, Rsubread, reshape2, SummarizedExperiment, stats, tximport, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86, ggplotify, knitr, patchwork, R.utils, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: bb6113a1c741d48f2a089a1cf18b0fec NeedsCompilation: no Title: Detect and Correct Genomic DNA Contamination in RNA-seq Data Description: RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes. biocViews: QualityControl, Sequencing, GeneExpression Author: Haibo Liu [aut, cre] (ORCID: ), Kevin O'Connor [ctb], Michelle Kelliher [ctb], Lihua Julie Zhu [aut], Kai Hu [aut] Maintainer: Haibo Liu VignetteBuilder: knitr BugReports: https://github.com/haibol2016/CleanUpRNAseq/issues git_url: https://git.bioconductor.org/packages/CleanUpRNAseq git_branch: devel git_last_commit: dac4dc8 git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-19 source.ver: src/contrib/CleanUpRNAseq_1.1.1.tar.gz vignettes: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.html vignetteTitles: CleanUpRNAseq: detecting and correcting for DNA contamination\nin RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.R dependencyCount: 154 Package: cleaver Version: 1.45.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.36.5) License: GPL (>= 3) Archs: x64 MD5sum: 86f1c339416015f82933260af9ea8cd7 NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] (ORCID: ) Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: devel git_last_commit: 739ea76 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cleaver_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cleaver_1.45.0.zip vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: ProteoDisco, synapter suggestsMe: RforProteomics dependencyCount: 26 Package: clevRvis Version: 1.7.0 Imports: shiny, ggraph, igraph, ggiraph, cowplot, htmlwidgets, readxl, dplyr, readr, purrr, tibble, patchwork, R.utils, shinyWidgets, colorspace, shinyhelper, shinycssloaders, ggnewscale, shinydashboard, DT, colourpicker, grDevices, methods, utils, stats, ggplot2, magrittr, tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL-3 Archs: x64 MD5sum: e53a540cbd6fff8f9c3cadf60d0d9de8 NeedsCompilation: no Title: Visualization Techniques for Clonal Evolution Description: clevRvis provides a set of visualization techniques for clonal evolution. These include shark plots, dolphin plots and plaice plots. Algorithms for time point interpolation as well as therapy effect estimation are provided. Phylogeny-aware color coding is implemented. A shiny-app for generating plots interactively is additionally provided. biocViews: Software, ShinyApps, Visualization Author: Sarah Sandmann [aut, cre] (ORCID: ) Maintainer: Sarah Sandmann URL: https://github.com/sandmanns/clevRvis VignetteBuilder: knitr BugReports: https://github.com/sandmanns/clevRvis/issues git_url: https://git.bioconductor.org/packages/clevRvis git_branch: devel git_last_commit: 6e1b698 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clevRvis_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clevRvis_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clevRvis_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clevRvis_1.7.0.tgz vignettes: vignettes/clevRvis/inst/doc/clevRvis.html vignetteTitles: ClEvR Viz vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clevRvis/inst/doc/clevRvis.R dependencyCount: 119 Package: clippda Version: 1.57.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 15f983522219f6ebe2ac78f8e3c9050c NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: devel git_last_commit: d7fcfbb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clippda_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clippda_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clippda_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clippda_1.57.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 43 Package: clipper Version: 1.47.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: f07dfbf2bc13bf336e50520244f65faa NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: devel git_last_commit: 623d9a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clipper_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clipper_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clipper_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clipper_1.47.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R dependencyCount: 93 Package: cliProfiler Version: 1.13.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: aa7249aefa547b15680a24574a8c16eb NeedsCompilation: no Title: A package for the CLIP data visualization Description: An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results. biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/Codezy99/cliProfiler VignetteBuilder: knitr BugReports: https://github.com/Codezy99/cliProfiler/issues git_url: https://git.bioconductor.org/packages/cliProfiler git_branch: devel git_last_commit: bbef029 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cliProfiler_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cliProfiler_1.13.0.zip vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html vignetteTitles: cliProfiler Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R dependencyCount: 88 Package: cliqueMS Version: 1.21.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, coop, slam, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: BiocParallel, knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) MD5sum: e9f8a1bad3fc05ff19669d3edcdf075b NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: devel git_last_commit: 6b0f4ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cliqueMS_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cliqueMS_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cliqueMS_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cliqueMS_1.21.0.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 150 Package: Clomial Version: 1.43.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: cdf58efe17351d749ff14391293cf914 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: devel git_last_commit: ed32b0f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Clomial_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Clomial_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Clomial_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Clomial_1.43.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: clst Version: 1.55.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 365b3194463e2845e6930fc6b47e4cb5 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: devel git_last_commit: b7497fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clst_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clst_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clst_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clst_1.55.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 14 Package: clstutils Version: 1.55.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 Archs: x64 MD5sum: 6cc7be1c9c0ac6bcad01cf5f1d240afc NeedsCompilation: no Title: Tools for performing taxonomic assignment Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: devel git_last_commit: 467cc63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clstutils_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clstutils_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clstutils_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clstutils_1.55.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 37 Package: CluMSID Version: 1.23.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE MD5sum: ccb8461d20794b8776d4fe25aa58cdbf NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: devel git_last_commit: e5261c4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CluMSID_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CluMSID_1.23.0.zip vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 155 Package: ClustAll Version: 1.3.0 Depends: R (>= 4.2.0) Imports: FactoMineR, bigstatsr, clValid, doSNOW, parallel, foreach, dplyr, fpc, mice, modeest, flock, networkD3, methods, ComplexHeatmap, cluster, RColorBrewer, circlize, grDevices, ggplot2, grid, stats, utils, pbapply Suggests: RUnit, knitr, BiocGenerics, rmarkdown, BiocStyle, roxygen2 License: GPL-2 MD5sum: 6e017319dae1668f1e65b807e1d54d49 NeedsCompilation: no Title: ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases Description: Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types). biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: Asier Ortega-Legarreta [aut, cre] (ORCID: ), Sara Palomino-Echeverria [aut] Maintainer: Asier Ortega-Legarreta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClustAll git_branch: devel git_last_commit: 3058c84 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ClustAll_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClustAll_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClustAll_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClustAll_1.3.0.tgz vignettes: vignettes/ClustAll/inst/doc/Vignette_Clustall.html vignetteTitles: ClustALL User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClustAll/inst/doc/Vignette_Clustall.R dependencyCount: 182 Package: clustComp Version: 1.35.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: fbb81ed42b70cb0f4bd1fed7f2f0d11f NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: devel git_last_commit: b968259 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clustComp_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clustComp_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clustComp_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clustComp_1.35.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.27.0 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, locfdr, matrixStats, graphics, parallel, BiocSingular, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph, rmarkdown License: Artistic-2.0 MD5sum: d462c13a3acc5ea801a70ed8f72943d5 NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: devel git_last_commit: 3bb82fb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clusterExperiment_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterExperiment_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterExperiment_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterExperiment_2.27.0.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html, vignettes/clusterExperiment/inst/doc/largeDataSets.html vignetteTitles: clusterExperiment Vignette, Working with Large Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R, vignettes/clusterExperiment/inst/doc/largeDataSets.R dependsOnMe: netSmooth suggestsMe: slingshot, tradeSeq dependencyCount: 149 Package: ClusterFoldSimilarity Version: 1.3.0 Imports: methods, igraph, ggplot2, scales, BiocParallel, graphics, stats, utils, Matrix, cowplot, dplyr, reshape2, Seurat, SeuratObject, SingleCellExperiment, ggdendro Suggests: knitr, rmarkdown, kableExtra, scRNAseq, BiocStyle License: Artistic-2.0 MD5sum: 8c6d5e4cbca500be9db5b3f4d34f9673 NeedsCompilation: no Title: Calculate similarity of clusters from different single cell samples using foldchanges Description: This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m biocViews: SingleCell, Clustering, FeatureExtraction, GraphAndNetwork, GeneTarget, RNASeq Author: Oscar Gonzalez-Velasco [cre, aut] (ORCID: ) Maintainer: Oscar Gonzalez-Velasco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterFoldSimilarity git_branch: devel git_last_commit: 60167ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ClusterFoldSimilarity_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterFoldSimilarity_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterFoldSimilarity_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterFoldSimilarity_1.3.0.tgz vignettes: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.html vignetteTitles: ClusterFoldSimilarity: hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.R dependencyCount: 179 Package: ClusterJudge Version: 1.29.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: a063a662d59224ead16bb61597699466 NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: devel git_last_commit: e8c9f1d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ClusterJudge_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterJudge_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterJudge_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterJudge_1.29.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 26 Package: clusterProfiler Version: 4.15.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, DOSE (>= 3.23.2), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim (>= 2.27.2), gson (>= 0.0.7), httr, igraph, magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils (>= 0.1.6) Suggests: AnnotationHub, knitr, jsonlite, readr, rmarkdown, org.Hs.eg.db, prettydoc, BiocManager, testthat License: Artistic-2.0 Archs: x64 MD5sum: 201bcb9bd63b9b5280cb806eb98e89ca NeedsCompilation: no Title: A universal enrichment tool for interpreting omics data Description: This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Li-Gen Wang [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: devel git_last_commit: 1aedd90 git_last_commit_date: 2024-11-29 Date/Publication: 2024-11-29 source.ver: src/contrib/clusterProfiler_4.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterProfiler_4.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterProfiler_4.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterProfiler_4.15.1.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R dependsOnMe: maEndToEnd importsMe: bioCancer, broadSeq, CaMutQC, CBNplot, CEMiTool, CeTF, debrowser, EasyCellType, EnrichDO, epiregulon.extra, esATAC, famat, GDCRNATools, gINTomics, goSorensen, MetaPhOR, methylGSA, MicrobiomeProfiler, miRSM, miRspongeR, Moonlight2R, MoonlightR, mosdef, PanomiR, pathlinkR, Pigengene, signatureSearch, ExpHunterSuite, recountWorkflow, TCGAWorkflow, DRviaSPCN, genekitr, Grouphmap, immcp, pathwayTMB, PMAPscore, RVA, ssdGSA, tinyarray suggestsMe: ChIPseeker, cola, DAPAR, DOSE, enrichplot, EpiCompare, EpiMix, GeDi, GeneTonic, GenomicSuperSignature, GeoTcgaData, ggkegg, GOSemSim, GRaNIE, GSEAmining, mastR, MesKit, ReactomePA, rrvgo, scFeatures, scGPS, TCGAbiolinks, tidybulk, vsclust, org.Mxanthus.db, aPEAR, GeneSelectR, grandR, MARVEL, OlinkAnalyze, ReporterScore, SCpubr dependencyCount: 123 Package: clusterSeq Version: 1.31.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 9606fb8e65f7f4295efa130ad7991888 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle [aut], Irene Papatheodorou [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/clusterSeq BugReports: https://github.com/samgg/clusterSeq/issues git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: devel git_last_commit: 72a65eb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clusterSeq_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterSeq_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterSeq_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterSeq_1.31.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 43 Package: ClusterSignificance Version: 1.35.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: 180828de5b8e7302fa5d0f303328413a NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: devel git_last_commit: 4f0c989 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ClusterSignificance_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClusterSignificance_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClusterSignificance_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClusterSignificance_1.35.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.79.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 19392e3300dff3428f5a78e0430bd8ae NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: devel git_last_commit: a43a209 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clusterStab_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clusterStab_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clusterStab_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clusterStab_1.79.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 7 Package: clustifyr Version: 1.19.0 Depends: R (>= 2.10) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, SeuratObject, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr, data.table, R.utils License: MIT + file LICENSE MD5sum: cefad5e60f6637a481c2c436c97dab93 NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray, GeneExpression Author: Rui Fu [cre, aut], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph] Maintainer: Rui Fu URL: https://github.com/rnabioco/clustifyr, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: devel git_last_commit: 01ae82e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/clustifyr_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/clustifyr_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/clustifyr_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/clustifyr_1.19.0.tgz vignettes: vignettes/clustifyr/inst/doc/clustifyr.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyr.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 99 Package: ClustIRR Version: 1.5.19 Depends: R (>= 4.4.0) Imports: blaster, future, future.apply, grDevices, igraph, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, stringdist, utils, posterior, visNetwork LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, testthat, ggplot2, ggrepel, patchwork License: GPL-3 + file LICENSE MD5sum: 1c94eabb5d3b3aad01aa116095dfeae2 NeedsCompilation: yes Title: Clustering of immune receptor repertoires Description: ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment. biocViews: Clustering, ImmunoOncology, SingleCell, Software, Classification Author: Simo Kitanovski [aut, cre] (ORCID: ), Kai Wollek [aut] (ORCID: ) Maintainer: Simo Kitanovski URL: https://github.com/snaketron/ClustIRR SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/ClustIRR/issues git_url: https://git.bioconductor.org/packages/ClustIRR git_branch: devel git_last_commit: 967c34e git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/ClustIRR_1.5.19.tar.gz win.binary.ver: bin/windows/contrib/4.5/ClustIRR_1.5.19.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ClustIRR_1.5.19.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ClustIRR_1.5.19.tgz vignettes: vignettes/ClustIRR/inst/doc/User_manual.html vignetteTitles: Analysis of T and B cell receptor repertoires with ClustIRR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClustIRR/inst/doc/User_manual.R dependencyCount: 100 Package: CMA Version: 1.65.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 8d31d064263c9630760760b18040c72a NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: devel git_last_commit: c6f5e4f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CMA_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CMA_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CMA_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CMA_1.65.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 7 Package: cmapR Version: 1.19.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: 6b2fae7ea3217436c52a6a3df64d44c0 NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] (ORCID: ) Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: devel git_last_commit: 53a0f4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/cmapR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cmapR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cmapR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cmapR_1.19.0.tgz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 47 Package: cn.farms Version: 1.55.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: x64 MD5sum: c79ca82f6b70afecf231da58a86f756b NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: devel git_last_commit: 8600141 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cn.farms_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cn.farms_1.55.0.zip vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 67 Package: cn.mops Version: 1.53.0 Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors Suggests: DNAcopy License: LGPL (>= 2.0) MD5sum: 4ec460a7d476d3d92a3f7cbe3b052c2a NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer [aut], Gundula Povysil [cre] Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: devel git_last_commit: 3abd26d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cn.mops_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cn.mops_1.53.0.zip vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots dependencyCount: 41 Package: CNAnorm Version: 1.53.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 MD5sum: 2e8655cb54573cbb88670861a47a189e NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: devel git_last_commit: 65ad348 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNAnorm_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNAnorm_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNAnorm_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNAnorm_1.53.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.43.0 Depends: R (>= 3.5.0) Imports: Biostrings (>= 2.33.4), pwalign, DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: x64 MD5sum: 44968c337893c92471616e4c4995a154 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: devel git_last_commit: 7b0cff1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNEr_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNEr_1.43.0.zip vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools dependencyCount: 119 Package: CNORdt Version: 1.49.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: x64 MD5sum: 19a2f6f9df4eef59c617526dbe1c32f5 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: devel git_last_commit: 7924ad2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNORdt_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORdt_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORdt_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORdt_1.49.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 73 Package: CNORfeeder Version: 1.47.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 Archs: x64 MD5sum: 77260819823417dd703c6727444bb0bf NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: Federica Eduati [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: devel git_last_commit: cc61372 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNORfeeder_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORfeeder_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORfeeder_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORfeeder_1.47.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 72 Package: CNORfuzzy Version: 1.49.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 115431a9454047b57691ec9a305efc56 NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: devel git_last_commit: 48255bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNORfuzzy_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORfuzzy_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORfuzzy_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORfuzzy_1.49.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 73 Package: CNORode Version: 1.49.0 Depends: CellNOptR, genalg Suggests: knitr, rmarkdown Enhances: doParallel, foreach License: GPL-2 Archs: x64 MD5sum: ec1d475cbe3074334b7791cc310ed5f8 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: Logic based ordinary differential equation (ODE) add-on to CellNOptR. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Attila Gabor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: devel git_last_commit: 851f051 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNORode_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNORode_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNORode_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNORode_1.49.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 73 Package: CNTools Version: 1.63.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL MD5sum: 9d71b8f05f6cbf1b627df4fb71662b6a NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: devel git_last_commit: 05a59fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNTools_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNTools_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNTools_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNTools_1.63.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 57 Package: CNVfilteR Version: 1.21.0 Depends: R (>= 4.3) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: c407ce29093241682735c7830c19a392 NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (ORCID: ), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: devel git_last_commit: b987028 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNVfilteR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVfilteR_1.21.0.zip vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 149 Package: cnvGSA Version: 1.51.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL Archs: x64 MD5sum: 70216baf9b83c6db8cda98873d63d44c NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: devel git_last_commit: 66054b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cnvGSA_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cnvGSA_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cnvGSA_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cnvGSA_1.51.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 33 Package: CNViz Version: 1.15.0 Depends: R (>= 4.0), shiny (>= 1.5.0) Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR, CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics Suggests: rmarkdown, knitr License: Artistic-2.0 Archs: x64 MD5sum: d4b0bb354932a774411e4e08511c388f NeedsCompilation: no Title: Copy Number Visualization Description: CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data. biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq Author: Rebecca Greenblatt [aut, cre] Maintainer: Rebecca Greenblatt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNViz git_branch: devel git_last_commit: 5bf989f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNViz_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNViz_1.15.0.zip vignettes: vignettes/CNViz/inst/doc/CNViz.html vignetteTitles: CNViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNViz/inst/doc/CNViz.R dependencyCount: 161 Package: CNVMetrics Version: 1.11.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods, magrittr, stats, pheatmap, gridExtra, grDevices, rBeta2009 Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 9b0c28070b9f28f92b89005a252cc4eb NeedsCompilation: no Title: Copy Number Variant Metrics Description: The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics. biocViews: BiologicalQuestion, Software, CopyNumberVariation Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Astrid Deschênes URL: https://github.com/krasnitzlab/CNVMetrics, https://krasnitzlab.github.io/CNVMetrics/ VignetteBuilder: knitr BugReports: https://github.com/krasnitzlab/CNVMetrics/issues git_url: https://git.bioconductor.org/packages/CNVMetrics git_branch: devel git_last_commit: 961ad8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNVMetrics_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVMetrics_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVMetrics_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVMetrics_1.11.0.tgz vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html vignetteTitles: Copy number variant metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R dependencyCount: 52 Package: CNVPanelizer Version: 1.39.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: 5e071195694f74b7fa1db1a0291f7a33 NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: devel git_last_commit: 4aac16f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNVPanelizer_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVPanelizer_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVPanelizer_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVPanelizer_1.39.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 118 Package: CNVRanger Version: 1.23.0 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, TxDb.Hsapiens.UCSC.hg19.knownGene, curatedTCGAData, ensembldb, grid, knitr, org.Hs.eg.db, regioneR, rmarkdown, statmod License: Artistic-2.0 MD5sum: 8d9acb74bfd7e5c6a717057c99257f87 NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva [aut], Marcel Ramos [ctb], Levi Waldron [ctb] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: devel git_last_commit: 7cbb1a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNVRanger_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVRanger_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CNVRanger_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CNVRanger_1.23.0.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 74 Package: CNVrd2 Version: 1.45.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 46b5295579d7ba61ac80ed920a86bc8d NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: devel git_last_commit: 55815ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CNVrd2_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CNVrd2_1.45.0.zip vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 104 Package: CoCiteStats Version: 1.79.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 5e7abfa5f40b15b26d821f87a97d12da NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: devel git_last_commit: 51ec881 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CoCiteStats_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoCiteStats_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoCiteStats_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoCiteStats_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 47 Package: COCOA Version: 2.21.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 0b6499cfd3252e7669def3df1dfcf63c NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: devel git_last_commit: 16ecf4b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/COCOA_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COCOA_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COCOA_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COCOA_2.21.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 127 Package: codelink Version: 1.75.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 91bf478103badb5038f9dc258b7bb76e NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: devel git_last_commit: d620585 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/codelink_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/codelink_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/codelink_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/codelink_1.75.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 51 Package: CODEX Version: 1.39.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: e0bde123b3e651d0cf5f3dbfe8651ae1 NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: devel git_last_commit: a803161 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CODEX_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CODEX_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CODEX_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CODEX_1.39.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 61 Package: CoGAPS Version: 3.27.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5, dplyr, fgsea, forcats, ggplot2 LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject, BiocFileCache License: BSD_3_clause + file LICENSE MD5sum: 5b2558d10f8cfd0f1e22116aacf42aa6 NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Jeanette Johnson, Ashley Tsang, Jacob Mitchell, Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Jeanette Johnson , Dmitrijs Lvovs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: devel git_last_commit: ec59da0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CoGAPS_3.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoGAPS_3.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoGAPS_3.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoGAPS_3.27.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R suggestsMe: projectR, SpaceMarkers dependencyCount: 91 Package: cogena Version: 1.41.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 MD5sum: 138e5acd3f1e63c2efff7257f301a554 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: devel git_last_commit: 06f4d8f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cogena_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cogena_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cogena_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cogena_1.41.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 146 Package: cogeqc Version: 1.11.0 Depends: R (>= 4.2.0) Imports: utils, graphics, stats, methods, reshape2, ggplot2, scales, ggtree, patchwork, igraph, rlang, ggbeeswarm, jsonlite, Biostrings Suggests: testthat (>= 3.0.0), sessioninfo, knitr, BiocStyle, rmarkdown, covr License: GPL-3 MD5sum: d3a9c65194ccb0a197e3249ff4c253bd NeedsCompilation: no Title: Systematic quality checks on comparative genomics analyses Description: cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics. biocViews: Software, GenomeAssembly, ComparativeGenomics, FunctionalGenomics, Phylogenetics, QualityControl, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: devel git_last_commit: 8c60dba git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cogeqc_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cogeqc_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cogeqc_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cogeqc_1.11.0.tgz vignettes: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html vignetteTitles: Assessing genome assembly and annotation quality, Assessing orthogroup inference, Assessing synteny identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R dependencyCount: 83 Package: Cogito Version: 1.13.0 Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy Imports: BiocManager, rmarkdown, GenomeInfoDb, S4Vectors, AnnotationDbi, graphics, stats, utils, methods, magrittr, ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0) License: LGPL-3 Archs: x64 MD5sum: 0a7e1de1df4a48cbd571be91e663bb39 NeedsCompilation: no Title: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets Description: Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis. biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing Author: Annika Bürger [cre, aut] Maintainer: Annika Bürger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: devel git_last_commit: 55b1c5a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Cogito_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cogito_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cogito_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cogito_1.13.0.tgz vignettes: vignettes/Cogito/inst/doc/Cogito.html vignetteTitles: Cogito: Compare annotated genomic intervals tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cogito/inst/doc/Cogito.R dependencyCount: 116 Package: coGPS Version: 1.51.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 4e3629d613c37c64db8589b97fd59b14 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: devel git_last_commit: 2f7fc38 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/coGPS_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coGPS_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coGPS_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coGPS_1.51.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: cola Version: 2.13.0 Depends: R (>= 4.0.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats, GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr (>= 1.4.0), markdown (>= 1.6), digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, doRNG, irlba LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot, flexclust, randomForest, e1071 License: MIT + file LICENSE MD5sum: 77e1c0e391db425af682aac505fa9ec6 NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: devel git_last_commit: c1c8dc8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cola_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cola_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cola_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cola_2.13.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 66 Package: comapr Version: 1.11.0 Depends: R (>= 4.1.0) Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly, circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel, GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils, Matrix, grid, stats, SummarizedExperiment, plyr, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod License: MIT + file LICENSE MD5sum: 58203ae3e0017da6db666b74258db6a6 NeedsCompilation: no Title: Crossover analysis and genetic map construction Description: comapr detects crossover intervals for single gametes from their haplotype states sequences and stores the crossovers in GRanges object. The genetic distances can then be calculated via the mapping functions using estimated crossover rates for maker intervals. Visualisation functions for plotting interval-based genetic map or cumulative genetic distances are implemented, which help reveal the variation of crossovers landscapes across the genome and across individuals. biocViews: Software, SingleCell, Visualization, Genetics Author: Ruqian Lyu [aut, cre] (ORCID: ) Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: devel git_last_commit: 8257419 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/comapr_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/comapr_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/comapr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/comapr_1.11.0.tgz vignettes: vignettes/comapr/inst/doc/getStarted.html, vignettes/comapr/inst/doc/single-sperm-co-analysis.html vignetteTitles: Get-Started-With-comapr, single-sperm-co-analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/comapr/inst/doc/getStarted.R, vignettes/comapr/inst/doc/single-sperm-co-analysis.R dependencyCount: 168 Package: coMethDMR Version: 1.11.0 Depends: R (>= 4.1) Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub, GenomicRanges, IRanges, lmerTest, methods, stats, utils Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: GPL-3 MD5sum: 347d785916496ecade0da58251f7e995 NeedsCompilation: no Title: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies Description: coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously. biocViews: DNAMethylation, Epigenetics, MethylationArray, DifferentialMethylation, GenomeWideAssociation Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut] Maintainer: Fernanda Veitzman URL: https://github.com/TransBioInfoLab/coMethDMR VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues git_url: https://git.bioconductor.org/packages/coMethDMR git_branch: devel git_last_commit: 7275ade git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/coMethDMR_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coMethDMR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coMethDMR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coMethDMR_1.11.0.tgz vignettes: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel Computing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R dependencyCount: 129 Package: COMPASS Version: 1.45.0 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, progress License: Artistic-2.0 Archs: x64 MD5sum: 059d957ef76dca48274d0435f90dbaf9 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: devel git_last_commit: 2b16d61 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/COMPASS_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COMPASS_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COMPASS_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COMPASS_1.45.0.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 72 Package: compcodeR Version: 1.43.0 Depends: R (>= 4.0), sm Imports: knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, stats, utils, ape, phylolm, matrixStats, grDevices, graphics, rmarkdown, shiny, shinydashboard Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), phytools, phangorn, testthat, ggtree, tidytree, statmod, covr, sva, tcltk Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 17ef7033e2ea2d39d63316951483bc53 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] (ORCID: ), Paul Bastide [aut] (ORCID: ), Mélina Gallopin [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: devel git_last_commit: a538f70 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/compcodeR_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compcodeR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compcodeR_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compcodeR_1.43.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html, vignettes/compcodeR/inst/doc/phylocompcodeR.html vignetteTitles: compcodeR, phylocompcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R, vignettes/compcodeR/inst/doc/phylocompcodeR.R dependencyCount: 106 Package: compEpiTools Version: 1.41.0 Depends: R (>= 3.5.0), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: a99e47d82a953db9f01925fc3abe1ebe NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: devel git_last_commit: 87788a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/compEpiTools_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compEpiTools_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compEpiTools_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compEpiTools_1.41.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 169 Package: ComplexHeatmap Version: 2.23.0 Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel, codetools Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), gridGraphics, gplots, rmarkdown, Cairo, magick License: MIT + file LICENSE MD5sum: 0446da886e3ab7b3d190ab7022c5833e NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: devel git_last_commit: 29bc185 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ComplexHeatmap_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ComplexHeatmap_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ComplexHeatmap_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ComplexHeatmap_2.23.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap, multistateQTL, recoup, sechm, countToFPKM importsMe: airpart, ASURAT, bettr, BindingSiteFinder, BioNERO, blacksheepr, BloodGen3Module, CATALYST, CCPlotR, celda, CeTF, ClustAll, COCOA, cola, COTAN, CRISPRball, CTexploreR, cytoKernel, Damsel, DEGreport, DEP, diffcyt, dinoR, dominoSignal, ELMER, epiregulon.extra, fCCAC, FLAMES, gCrisprTools, GeDi, GeneTonic, GenomicPlot, GenomicSuperSignature, gINTomics, gmoviz, GRaNIE, hermes, hoodscanR, HybridExpress, InterCellar, iSEE, MAPFX, MatrixQCvis, MesKit, MOMA, monaLisa, Moonlight2R, MOSClip, MPAC, MultiRNAflow, muscat, musicatk, MWASTools, nipalsMCIA, pathlinkR, PeacoQC, pipeComp, POMA, profileplyr, PRONE, scRNAseqApp, segmenter, simona, simplifyEnrichment, singleCellTK, sparrow, SPONGE, TBSignatureProfiler, TMSig, Xeva, YAPSA, ProteinGymR, spatialLIBD, TCGAWorkflow, bulkAnalyseR, coda4microbiome, conos, DiscreteGapStatistic, GRIN2, GSSTDA, karyotapR, mineSweepR, missoNet, MitoHEAR, MKomics, ogrdbstats, Path.Analysis, PCAPAM50, pkgndep, rKOMICS, rliger, RNAseqQC, RVA, scITD, sigQC, spiralize, tidyHeatmap, TransProR, visxhclust, wilson suggestsMe: artMS, bambu, clustifyr, CNVRanger, demuxSNP, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, mastR, miaViz, msImpute, plotgardener, projectR, QFeatures, raer, scDblFinder, SPIAT, TCGAbiolinks, TCGAutils, weitrix, curatedPCaData, LegATo, NanoporeRNASeq, BeeBDC, CIARA, circlize, ConsensusOPLS, eclust, ggsector, grandR, inferCSN, IOHanalyzer, metasnf, multipanelfigure, plotthis, scCustomize, SCpubr, sfcurve, singleCellHaystack, SpatialDDLS, tinyarray dependencyCount: 29 Package: CompoundDb Version: 1.11.1 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics (>= 1.35.3), xml2, IRanges, Spectra (>= 1.15.10), MsCoreUtils, MetaboCoreUtils, BiocParallel, stringi Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19), MsBackendMgf License: Artistic-2.0 Archs: x64 MD5sum: 88e2cdb6b7c7cdf27762c9353e7ff3a4 NeedsCompilation: no Title: Creating and Using (Chemical) Compound Annotation Databases Description: CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. biocViews: MassSpectrometry, Metabolomics, Annotation Author: Jan Stanstrup [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Josep M. Badia [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Prateek Arora [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/CompoundDb VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues git_url: https://git.bioconductor.org/packages/CompoundDb git_branch: devel git_last_commit: c7e6b86 git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-18 source.ver: src/contrib/CompoundDb_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/CompoundDb_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CompoundDb_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CompoundDb_1.11.1.tgz vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html, vignettes/CompoundDb/inst/doc/create-compounddb.html vignetteTitles: Usage of Annotation Resources with the CompoundDb Package, Creating CompoundDb annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R, vignettes/CompoundDb/inst/doc/create-compounddb.R importsMe: MetaboAnnotation suggestsMe: AHMassBank, AnnotationHub, MetMashR dependencyCount: 120 Package: ComPrAn Version: 1.15.0 Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr, rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio, scales, shinydashboard, shinyjs, stats, tibble, grid Suggests: testthat (>= 2.1.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4e214249c74bbe4fd223f79acfee271c NeedsCompilation: no Title: Complexome Profiling Analysis package Description: This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. biocViews: MassSpectrometry, Proteomics, Visualization Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] (ORCID: ) Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: devel git_last_commit: 30c8d3c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ComPrAn_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ComPrAn_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ComPrAn_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ComPrAn_1.15.0.tgz vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html, vignettes/ComPrAn/inst/doc/proteinWorkflow.html, vignettes/ComPrAn/inst/doc/SILACcomplexomics.html vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R, vignettes/ComPrAn/inst/doc/proteinWorkflow.R, vignettes/ComPrAn/inst/doc/SILACcomplexomics.R dependencyCount: 107 Package: compSPOT Version: 1.5.0 Depends: R (>= 4.3.0) Imports: stats, base, ggplot2, plotly, magrittr, ggpubr, gridExtra, utils, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3bc6e3c265e9eb3e9019237f0aec72e1 NeedsCompilation: no Title: compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots Description: Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome, Classification, SingleCell, Survival, MultipleComparison Author: Sydney Grant [aut, cre] (ORCID: ), Ella Sampson [aut], Rhea Rodrigues [aut] (ORCID: ), Gyorgy Paragh [aut] (ORCID: ) Maintainer: Sydney Grant URL: https://github.com/sydney-grant/compSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/compSPOT/issues git_url: https://git.bioconductor.org/packages/compSPOT git_branch: devel git_last_commit: 05639f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/compSPOT_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/compSPOT_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/compSPOT_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/compSPOT_1.5.0.tgz vignettes: vignettes/compSPOT/inst/doc/compSPOT-vignette.html vignetteTitles: compSPOT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compSPOT/inst/doc/compSPOT-vignette.R dependencyCount: 111 Package: concordexR Version: 1.7.0 Depends: R (>= 4.4.0) Imports: BiocGenerics, BiocNeighbors, BiocParallel, bluster, cli, DelayedArray, Matrix, methods, purrr, rlang, SingleCellExperiment, sparseMatrixStats, SpatialExperiment, SummarizedExperiment Suggests: BiocManager, BiocStyle, ggplot2, glue, knitr, mbkmeans, patchwork, rmarkdown, scater, SFEData, SpatialFeatureExperiment, TENxPBMCData, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 61548ad693c67c7b5531346bcfac6359 NeedsCompilation: no Title: Identify Spatial Homogeneous Regions with concordex Description: Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs. biocViews: SingleCell, Clustering, Spatial, Transcriptomics Author: Kayla Jackson [aut, cre] (ORCID: ), A. Sina Booeshaghi [aut] (ORCID: ), Angel Galvez-Merchan [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Alexandra Kim [ctb], Laura Luebbert [ctb] (ORCID: ), Lior Pachter [aut, rev, ths] (ORCID: ) Maintainer: Kayla Jackson URL: https://github.com/pachterlab/concordexR, https://pachterlab.github.io/concordexR/ VignetteBuilder: knitr BugReports: https://github.com/pachterlab/concordexR/issues git_url: https://git.bioconductor.org/packages/concordexR git_branch: devel git_last_commit: 716ddc9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/concordexR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/concordexR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/concordexR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/concordexR_1.7.0.tgz vignettes: vignettes/concordexR/inst/doc/concordex-nonspatial.html, vignettes/concordexR/inst/doc/overview.html vignetteTitles: concordex-nonspatial, overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/concordexR/inst/doc/concordex-nonspatial.R, vignettes/concordexR/inst/doc/overview.R dependencyCount: 88 Package: condiments Version: 1.15.1 Depends: R (>= 4.0) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph, distinct Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN, DelayedMatrixStats License: MIT + file LICENSE MD5sum: eb27382855a55ca4771830d71d1c3080 NeedsCompilation: no Title: Differential Topology, Progression and Differentiation Description: This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format. biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux URL: https://hectorrdb.github.io/condiments/index.html VignetteBuilder: knitr BugReports: https://github.com/HectorRDB/condiments/issues git_url: https://git.bioconductor.org/packages/condiments git_branch: devel git_last_commit: e681d88 git_last_commit_date: 2024-12-03 Date/Publication: 2024-12-04 source.ver: src/contrib/condiments_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/condiments_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/condiments_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/condiments_1.15.1.tgz vignettes: vignettes/condiments/inst/doc/condiments.html, vignettes/condiments/inst/doc/controls.html, vignettes/condiments/inst/doc/examples.html vignetteTitles: The condiments workflow, Using condiments, Generating more examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/condiments/inst/doc/condiments.R, vignettes/condiments/inst/doc/controls.R, vignettes/condiments/inst/doc/examples.R dependencyCount: 168 Package: CONFESS Version: 1.35.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: af15b2223a131cd695ba408dc642890d NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: devel git_last_commit: be5739f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CONFESS_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CONFESS_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CONFESS_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CONFESS_1.35.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 153 Package: consensus Version: 1.25.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: 6dfc91f751969a46ad60ee049bb9ec07 NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: devel git_last_commit: c612c54 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/consensus_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensus_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensus_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensus_1.25.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.71.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 3349265e3f12d49f470447109b61f2fa NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: devel git_last_commit: 21e0b53 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ConsensusClusterPlus_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ConsensusClusterPlus_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ConsensusClusterPlus_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ConsensusClusterPlus_1.71.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, ccml, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks, tidytof dependencyCount: 10 Package: consensusDE Version: 1.25.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 4d001adcc9619a5e927e8626b73fbc51 NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: devel git_last_commit: 44f18e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/consensusDE_1.25.0.tar.gz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 151 Package: consensusOV Version: 1.29.0 Depends: R (>= 3.6) Imports: Biobase, GSVA (>= 1.50.0), gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods, BiocParallel Suggests: BiocStyle, ggplot2, knitr, rmarkdown, magick License: Artistic-2.0 MD5sum: 3c7c4fdea6203168cc01aebe49a67194 NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen [aut], Lavanya Kannan [aut], Ludwig Geistlinger [aut], Victor Kofia [aut], Levi Waldron [aut], Christopher Eeles [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: devel git_last_commit: fcae035 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-27 source.ver: src/contrib/consensusOV_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensusOV_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consensusOV_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consensusOV_1.29.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R dependencyCount: 154 Package: consensusSeekeR Version: 1.35.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 4b9f5c30f5d79e4c2cf23cb85e8cb896 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschênes [cre, aut] (ORCID: ), Fabien Claude Lamaze [ctb], Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/adeschen/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: devel git_last_commit: 937d3ad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/consensusSeekeR_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consensusSeekeR_1.35.0.zip vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes suggestsMe: EpiCompare dependencyCount: 67 Package: consICA Version: 2.5.0 Depends: R (>= 4.2.0) Imports: fastICA (>= 1.2.1), sm, org.Hs.eg.db, GO.db, stats, SummarizedExperiment, BiocParallel, graph, ggplot2, methods, Rfast, pheatmap, survival, topGO, graphics, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat, Seurat License: MIT + file LICENSE MD5sum: a71d6b912e70f0fec166c8fc11a12c32 NeedsCompilation: no Title: consensus Independent Component Analysis Description: consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Transcriptomics, Classification, FeatureExtraction Author: Petr V. Nazarov [aut, cre] (ORCID: ), Tony Kaoma [aut] (ORCID: ), Maryna Chepeleva [aut] (ORCID: ) Maintainer: Petr V. Nazarov VignetteBuilder: knitr BugReports: https://github.com/biomod-lih/consICA/issues git_url: https://git.bioconductor.org/packages/consICA git_branch: devel git_last_commit: 7b9bffe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/consICA_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/consICA_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/consICA_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/consICA_2.5.0.tgz vignettes: vignettes/consICA/inst/doc/ConsICA.html vignetteTitles: The consICA package: User’s manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consICA/inst/doc/ConsICA.R dependencyCount: 101 Package: CONSTANd Version: 1.15.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: 1b4c52e21b37fdde59cb00d2f1532063 NeedsCompilation: no Title: Data normalization by matrix raking Description: Normalizes a data matrix `data` by raking (using the RAS method by Bacharach, see references) the Nrows by Ncols matrix such that the row means and column means equal 1. The result is a normalized data matrix `K=RAS`, a product of row mulipliers `R` and column multipliers `S` with the original matrix `A`. Missing information needs to be presented as `NA` values and not as zero values, because CONSTANd is able to ignore missing values when calculating the mean. Using CONSTANd normalization allows for the direct comparison of values between samples within the same and even across different CONSTANd-normalized data matrices. biocViews: MassSpectrometry, Cheminformatics, Normalization, Preprocessing, DifferentialExpression, Genetics, Transcriptomics, Proteomics Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk Valkenborg [aut, cre] Maintainer: Dirk Valkenborg URL: qcquan.net/constand VignetteBuilder: knitr BugReports: https://github.com/PDiracDelta/CONSTANd/issues git_url: https://git.bioconductor.org/packages/CONSTANd git_branch: devel git_last_commit: 7ad2dec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CONSTANd_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CONSTANd_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CONSTANd_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CONSTANd_1.15.0.tgz vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html vignetteTitles: CONSTANd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R dependencyCount: 0 Package: conumee Version: 1.41.0 Depends: R (>= 3.5.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: d249d781f88b23ada3ec6eab15e253bf NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: devel git_last_commit: f47752d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/conumee_1.41.0.tar.gz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 150 Package: convert Version: 1.83.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: b81e19c605a1a673a520cbaa50e86a5f NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: devel git_last_commit: 9922d10 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/convert_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/convert_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/convert_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/convert_1.83.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 11 Package: copa Version: 1.75.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 MD5sum: 1308cacda28659cc583741175183a98d NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: devel git_last_commit: 64ecbf8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/copa_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/copa_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/copa_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/copa_1.75.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 7 Package: CopyNumberPlots Version: 1.23.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: 7dbf406abcb5a55d5b2d680ceb17a128 NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: devel git_last_commit: f101583 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CopyNumberPlots_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CopyNumberPlots_1.23.0.zip vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR, CNViz dependencyCount: 146 Package: coRdon Version: 1.25.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: c01b921d3973e8025df053bce61301f9 NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: devel git_last_commit: c2f3cd0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/coRdon_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/coRdon_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/coRdon_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/coRdon_1.25.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 62 Package: CoreGx Version: 2.11.0 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang, bench Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL (>= 3) MD5sum: 0e1b81bb4ba70d3a86e7bececd9fbcec NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Jermiah Joseph [aut], Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: devel git_last_commit: 2e3e61b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CoreGx_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoreGx_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoreGx_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoreGx_2.11.0.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.html vignetteTitles: CoreGx: Class and Function Abstractions, The TreatmentResponseExperiment Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: gDRimport, PDATK dependencyCount: 140 Package: Cormotif Version: 1.53.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 Archs: x64 MD5sum: b1747b4b565bb72daec61db971b5889c NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: devel git_last_commit: 1746b95 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Cormotif_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Cormotif_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Cormotif_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Cormotif_1.53.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 15 Package: corral Version: 1.17.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, reshape2, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, scater, testthat License: GPL-2 MD5sum: 8232ca0692519399f3c70c01cd221139 NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, GeneExpression, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: devel git_last_commit: 284ef14 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/corral_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/corral_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/corral_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/corral_1.17.0.tgz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependencyCount: 86 Package: coseq Version: 1.31.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2, scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 9d60f3fe50b515002cab7c22e9837eb7 NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: devel git_last_commit: 7cb63f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/coseq_1.31.0.tar.gz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependencyCount: 96 Package: CoSIA Version: 1.7.0 Depends: R (>= 4.3.0), methods (>= 4.3.0), ExperimentHub (>= 2.7.0) Imports: dplyr (>= 1.0.7), magrittr (>= 2.0.1), RColorBrewer (>= 1.1-2), tidyr (>= 1.2.0), plotly (>= 4.10.0), stringr (>= 1.4.0), ggplot2 (>= 3.3.5), tibble (>= 3.1.7), org.Hs.eg.db (>= 3.12.0), org.Mm.eg.db (>= 3.12.0), org.Dr.eg.db (>= 3.12.0), org.Ce.eg.db (>= 3.12.0), org.Dm.eg.db (>= 3.12.0), org.Rn.eg.db (>= 3.12.0), AnnotationDbi (>= 1.52.0), biomaRt (>= 2.46.3), homologene (>= 1.4.68.19), annotationTools (>= 1.64.0), readr (>= 2.1.1), tidyselect (>= 1.1.2), stats (>= 4.1.2) Suggests: BiocStyle (>= 2.22.0), tidyverse (>= 1.3.1), knitr (>= 1.42), rmarkdown (>= 2.20), testthat (>= 3.1.6), qpdf (>= 1.3.0) License: MIT + file LICENSE MD5sum: 11b6fd8f582ee4cf33e699cd18a5186f NeedsCompilation: no Title: An Investigation Across Different Species and Tissues Description: Cross-Species Investigation and Analysis (CoSIA) is a package that provides researchers with an alternative methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee. Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes, tissues, and species. CoSIA uses the Coefficient of Variation and Shannon Entropy and Specificity to calculate transcriptome diversity and variation. CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison. biocViews: Software, BiologicalQuestion, GeneExpression, MultipleComparison, ThirdPartyClient, DataImport, GUI Author: Anisha Haldar [aut] (ORCID: ), Vishal H. Oza [aut] (ORCID: ), Amanda D. Clark [cre, aut] (ORCID: ), Nathaniel S. DeVoss [aut] (ORCID: ), Brittany N. Lasseigne [aut] (ORCID: ) Maintainer: Amanda D. Clark URL: https://www.lasseigne.org/ VignetteBuilder: knitr BugReports: https://github.com/lasseignelab/CoSIA/issues git_url: https://git.bioconductor.org/packages/CoSIA git_branch: devel git_last_commit: 9a6548e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/CoSIA_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CoSIA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CoSIA_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CoSIA_1.7.0.tgz vignettes: vignettes/CoSIA/inst/doc/CoSIA_Intro.html vignetteTitles: CoSIA_Intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoSIA/inst/doc/CoSIA_Intro.R dependencyCount: 127 Package: cosmiq Version: 1.41.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 0878dbb37a7eadf4d11d456460a25c11 NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (ORCID: ), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: devel git_last_commit: acb2474 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/cosmiq_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cosmiq_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cosmiq_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cosmiq_1.41.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 149 Package: cosmosR Version: 1.15.0 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr, rlang, stringr, utils, visNetwork, decoupleR Suggests: testthat, knitr, rmarkdown, piano, ggplot2 License: GPL-3 MD5sum: 5ac3ea54b12cef10b28513963d509af3 NeedsCompilation: no Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut] (ORCID: ), Attila Gabor [cre] (ORCID: ), Katharina Zirngibl [aut] (ORCID: ) Maintainer: Attila Gabor URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues git_url: https://git.bioconductor.org/packages/cosmosR git_branch: devel git_last_commit: 7c6afd6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cosmosR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cosmosR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cosmosR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cosmosR_1.15.0.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 110 Package: COSNet Version: 1.41.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 13b6532c3551c2f7eb93fdfd7b411495 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: devel git_last_commit: 3f65459 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/COSNet_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/COSNet_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COSNet_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COSNet_1.41.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: COTAN Version: 2.7.1 Depends: R (>= 4.3) Imports: stats, plyr, dplyr, methods, grDevices, Matrix, ggplot2, ggrepel, ggthemes, graphics, parallel, parallelly, tibble, tidyr, BiocSingular, PCAtools, parallelDist, ComplexHeatmap, circlize, grid, scales, RColorBrewer, utils, rlang, Rfast, stringr, Seurat, umap, dendextend, zeallot, assertthat, withr, SingleCellExperiment, SummarizedExperiment, S4Vectors Suggests: testthat (>= 3.2.0), proto, spelling, knitr, data.table, gsubfn, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, Rtsne, plotly, BiocStyle, cowplot, qpdf, GEOquery, sf, torch License: GPL-3 MD5sum: a81be7a2cc5860ea8807a5ede75efa08 NeedsCompilation: no Title: COexpression Tables ANalysis Description: Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes. biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell Author: Galfrè Silvia Giulia [aut, cre] (ORCID: ), Morandin Francesco [aut] (ORCID: ), Fantozzi Marco [aut] (ORCID: ), Pietrosanto Marco [aut] (ORCID: ), Puttini Daniel [aut] (ORCID: ), Priami Corrado [aut] (ORCID: ), Cremisi Federico [aut] (ORCID: ), Helmer-Citterich Manuela [aut] (ORCID: ) Maintainer: Galfrè Silvia Giulia URL: https://github.com/seriph78/COTAN VignetteBuilder: knitr BugReports: https://github.com/seriph78/COTAN/issues git_url: https://git.bioconductor.org/packages/COTAN git_branch: devel git_last_commit: 8b64d1c git_last_commit_date: 2024-11-11 Date/Publication: 2024-11-13 source.ver: src/contrib/COTAN_2.7.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/COTAN_2.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/COTAN_2.7.1.tgz vignettes: vignettes/COTAN/inst/doc/Guided_tutorial_v2.html vignetteTitles: Guided tutorial to COTAN V.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COTAN/inst/doc/Guided_tutorial_v2.R dependencyCount: 208 Package: countsimQC Version: 1.25.0 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods, ragg Suggests: knitr, testthat License: GPL (>=2) MD5sum: 376604fc69c241e732589a8e1ce4bac5 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: devel git_last_commit: be98bfe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/countsimQC_1.25.0.tar.gz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 133 Package: covEB Version: 1.33.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: c6fccb6a6b1575a32a15e3c1dba5f14a NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: devel git_last_commit: 9ca408e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/covEB_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/covEB_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/covEB_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/covEB_1.33.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 24 Package: CoverageView Version: 1.45.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 12800f8e35fa17d577890926a16d0470 NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: devel git_last_commit: b74ea21 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CoverageView_1.45.0.tar.gz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 59 Package: covRNA Version: 1.33.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: f1cda399cab05324cb42217eaf26f7ab NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: devel git_last_commit: 84b2985 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/covRNA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/covRNA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/covRNA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/covRNA_1.33.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 64 Package: cpvSNP Version: 1.39.0 Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 288efb00e0a32ab594883f7e6aed7321 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: devel git_last_commit: 644afbb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cpvSNP_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cpvSNP_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cpvSNP_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cpvSNP_1.39.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 105 Package: cqn Version: 1.53.0 Depends: R (>= 2.10.0), mclust Imports: splines, graphics, nor1mix, stats, quantreg Suggests: scales, edgeR License: Artistic-2.0 Archs: x64 MD5sum: edc24e14bb0377e8ea01b2aa167cf4ba NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: devel git_last_commit: 1daa019 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cqn_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cqn_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cqn_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cqn_1.53.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: GeoTcgaData, tweeDEseq dependencyCount: 16 Package: CRImage Version: 1.55.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 9bed7aaaa326b01d053433a61605cf6b NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: devel git_last_commit: 181767b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CRImage_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CRImage_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CRImage_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CRImage_1.55.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 63 Package: CRISPRball Version: 1.3.2 Depends: R (>= 4.4.0), shinyBS Imports: DT, shiny, grid, ComplexHeatmap, InteractiveComplexHeatmap, graphics, stats, ggplot2, plotly, shinyWidgets, shinycssloaders, shinyjqui, dittoSeq, matrixStats, colourpicker, shinyjs, circlize, PCAtools, utils, grDevices, htmlwidgets, methods Suggests: BiocStyle, msigdbr, depmap, pool, RSQLite, mygene, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: 3991eec395fe58c76c2fd2ebb9ab0833 NeedsCompilation: no Title: Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering Description: A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds. biocViews: Software, ShinyApps, CRISPR, QualityControl, Visualization, GUI Author: Jared Andrews [aut, cre] (ORCID: ), Jacob Steele [ctb] (ORCID: ) Maintainer: Jared Andrews URL: https://github.com/j-andrews7/CRISPRball VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/CRISPRball git_branch: devel git_last_commit: 1f91c68 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/CRISPRball_1.3.2.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CRISPRball_1.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CRISPRball_1.3.2.tgz vignettes: vignettes/CRISPRball/inst/doc/CRISPRball.html vignetteTitles: CRISPRball Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CRISPRball/inst/doc/CRISPRball.R dependencyCount: 158 Package: crisprBase Version: 1.11.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges, S4Vectors, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 56280a7a77d5518eda790d6ade5b1fd0 NeedsCompilation: no Title: Base functions and classes for CRISPR gRNA design Description: Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBase VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBase/issues git_url: https://git.bioconductor.org/packages/crisprBase git_branch: devel git_last_commit: d22a715 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprBase_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprBase_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBase_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBase_1.11.0.tgz vignettes: vignettes/crisprBase/inst/doc/crisprBase.html vignetteTitles: Introduction to crisprBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R dependsOnMe: crisprDesign, crisprViz importsMe: crisprBowtie, crisprBwa, crisprShiny, crisprVerse dependencyCount: 35 Package: crisprBowtie Version: 1.11.0 Depends: methods Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, GenomicRanges, IRanges, Rbowtie, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 84ee442017cf27cb568ba2cd8a6b3ce2 NeedsCompilation: no Title: Bowtie-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBowtie VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBowtie/issues git_url: https://git.bioconductor.org/packages/crisprBowtie git_branch: devel git_last_commit: fa0a52f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprBowtie_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprBowtie_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBowtie_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBowtie_1.11.0.tgz vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html vignetteTitles: Introduction to crisprBowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R importsMe: crisprDesign, crisprVerse dependencyCount: 86 Package: crisprBwa Version: 1.11.0 Depends: methods Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, Rbwa, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE OS_type: unix MD5sum: 8700c84502a861f5dc6dc0e54af9b86a NeedsCompilation: no Title: BWA-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bwa. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Currently not supported on Windows machines. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBwa VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBwa/issues git_url: https://git.bioconductor.org/packages/crisprBwa git_branch: devel git_last_commit: 3b1e984 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprBwa_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprBwa_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprBwa_1.11.0.tgz vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html vignetteTitles: Introduction to crisprBwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R suggestsMe: crisprDesign dependencyCount: 86 Package: crisprDesign Version: 1.9.0 Depends: R (>= 4.2.0), crisprBase (>= 1.1.3) Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, crisprBowtie (>= 0.99.8), crisprScore (>= 1.1.6), GenomeInfoDb, GenomicFeatures, GenomicRanges (>= 1.38.0), IRanges, Matrix, MatrixGenerics, methods, rtracklayer, S4Vectors, stats, txdbmaker, utils, VariantAnnotation Suggests: biomaRt, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocStyle, crisprBwa (>= 0.99.7), knitr, rmarkdown, Rbowtie, Rbwa, RCurl, testthat License: MIT + file LICENSE MD5sum: ccb317fdeef48fb0ce281c58cf547910 NeedsCompilation: no Title: Comprehensive design of CRISPR gRNAs for nucleases and base editors Description: Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including DNA- and RNA-target nucleases such as Cas9, Cas12a, and Cas13d. All types of base editors are also supported. gRNA design can be performed on reference genomes, transcriptomes, and custom DNA and RNA sequences. Both unpaired and paired gRNA designs are enabled. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprDesign VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprDesign/issues git_url: https://git.bioconductor.org/packages/crisprDesign git_branch: devel git_last_commit: 61a8bea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprDesign_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprDesign_1.9.0.zip vignettes: vignettes/crisprDesign/inst/doc/intro.html vignetteTitles: Introduction to crisprDesign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprDesign/inst/doc/intro.R dependsOnMe: crisprViz importsMe: crisprShiny, crisprVerse dependencyCount: 126 Package: crisprScore Version: 1.11.0 Depends: R (>= 4.1), crisprScoreData (>= 1.1.3) Imports: basilisk (>= 1.9.2), basilisk.utils (>= 1.9.1), BiocGenerics, Biostrings, IRanges, methods, randomForest, reticulate, stringr, utils, XVector Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 8ad0900366dc4839417cbb4e31c1113b NeedsCompilation: no Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs Description: Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines. biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprScore/issues VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprScore git_url: https://git.bioconductor.org/packages/crisprScore git_branch: devel git_last_commit: d409224 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprScore_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprScore_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprScore_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprScore_1.11.0.tgz vignettes: vignettes/crisprScore/inst/doc/crisprScore.html vignetteTitles: crisprScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R importsMe: crisprDesign, crisprShiny, crisprVerse dependencyCount: 81 Package: CRISPRseek Version: 1.47.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, GenomeInfoDb, GenomicRanges, dplyr, keras, mltools Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice, MASS, tensorflow, testthat License: GPL (>= 2) MD5sum: 065e900a7ffc79da2ae0881602146bdb NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for the CRISPR editing system including Base Editors and the Prime Editor for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. The package also output indels and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Paul Scemama, Benjamin R. Holmes, Hervé Pagès, Kai Hu, Hui Mao, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: devel git_last_commit: e16802d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CRISPRseek_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CRISPRseek_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CRISPRseek_1.47.0.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R importsMe: GUIDEseq, multicrispr dependencyCount: 107 Package: crisprShiny Version: 1.3.0 Depends: R (>= 4.4.0), shiny Imports: BiocGenerics, Biostrings, BSgenome, crisprBase, crisprDesign, crisprScore, crisprViz, DT, GenomeInfoDb, htmlwidgets, methods, pwalign, S4Vectors, shinyBS, shinyjs, utils, waiter Suggests: BiocStyle, knitr, rmarkdown, shinyFeedback, testthat (>= 3.0.0), BSgenome.Hsapiens.UCSC.hg38 License: MIT + file LICENSE MD5sum: fb8117cd14c2537a4f851a667ea1fc8a NeedsCompilation: no Title: Exploring curated CRISPR gRNAs via Shiny Description: Provides means to interactively visualize guide RNAs (gRNAs) in GuideSet objects via Shiny application. This GUI can be self-contained or as a module within a larger Shiny app. The content of the app reflects the annotations present in the passed GuideSet object, and includes intuitive tools to examine, filter, and export gRNAs, thereby making gRNA design more user-friendly. biocViews: CRISPR, FunctionalGenomics, GeneTarget, GUI Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprShiny VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprShiny/issues git_url: https://git.bioconductor.org/packages/crisprShiny git_branch: devel git_last_commit: d16fcf6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprShiny_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprShiny_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprShiny_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprShiny_1.3.0.tgz vignettes: vignettes/crisprShiny/inst/doc/intro.html vignetteTitles: Introduction to crisprShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprShiny/inst/doc/intro.R dependencyCount: 194 Package: CrispRVariants Version: 1.35.0 Depends: R (>= 4.3.0), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, GenomicFeatures, knitr, rmarkdown, readxl, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 16614aef4e1e9473b10f90a6e1cb0bfa NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization, Sequencing Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: devel git_last_commit: aa34499 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CrispRVariants_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CrispRVariants_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CrispRVariants_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CrispRVariants_1.35.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf, vignettes/CrispRVariants/inst/doc/user_guide.html vignetteTitles: CrispRVariants, CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 96 Package: crisprVerse Version: 1.9.0 Depends: R (>= 4.2.0) Imports: BiocManager, cli, crisprBase, crisprBowtie, crisprScore, crisprScoreData, crisprDesign, crisprViz, rlang, tools, utils Suggests: BiocStyle, knitr, testthat License: MIT + file LICENSE MD5sum: b37f0da41757af2da8a0501da714b933 NeedsCompilation: no Title: Easily install and load the crisprVerse ecosystem for CRISPR gRNA design Description: The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit . biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprVerse VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprVerse/issues git_url: https://git.bioconductor.org/packages/crisprVerse git_branch: devel git_last_commit: e57a5dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/crisprVerse_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprVerse_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprVerse_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprVerse_1.9.0.tgz vignettes: vignettes/crisprVerse/inst/doc/crisprVerse.html vignetteTitles: crisprVerse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprVerse/inst/doc/crisprVerse.R dependencyCount: 181 Package: crisprViz Version: 1.9.0 Depends: R (>= 4.2.0), crisprBase (>= 0.99.15), crisprDesign (>= 0.99.77) Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, grDevices, Gviz, IRanges, methods, S4Vectors, txdbmaker Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, rtracklayer, testthat, utils License: MIT + file LICENSE MD5sum: aa9701a8a430094afeb617342fc3af7f NeedsCompilation: no Title: Visualization Functions for CRISPR gRNAs Description: Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprViz VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprViz/issues git_url: https://git.bioconductor.org/packages/crisprViz git_branch: devel git_last_commit: aa8d738 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crisprViz_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crisprViz_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/crisprViz_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/crisprViz_1.9.0.tgz vignettes: vignettes/crisprViz/inst/doc/intro.html vignetteTitles: Introduction to crisprViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprViz/inst/doc/intro.R importsMe: crisprShiny, crisprVerse dependencyCount: 180 Package: crlmm Version: 1.65.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit License: Artistic-2.0 MD5sum: 6ea296f1e8785d6b708a2252d3008a4b NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: devel git_last_commit: de849d9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/crlmm_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/crlmm_1.65.0.zip vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R dependsOnMe: MAGAR importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 74 Package: CSAR Version: 1.59.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: ef25f502898ca57975345f02a6ad161b NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: devel git_last_commit: 43ca1bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CSAR_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CSAR_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CSAR_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CSAR_1.59.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 24 Package: csaw Version: 1.41.1 Depends: R (>= 3.5.0), GenomicRanges, SummarizedExperiment Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 MD5sum: 52c646317b4d0d5d85936a48ebe4506a NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: devel git_last_commit: 7fb5bc4 git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-16 source.ver: src/contrib/csaw_1.41.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/csaw_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/csaw_1.41.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/csaw_1.41.1.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R dependsOnMe: csawBook importsMe: diffHic, epigraHMM, extraChIPs, icetea, NADfinder, vulcan, treediff suggestsMe: DiffBind, GRaNIE, chipseqDB dependencyCount: 57 Package: csdR Version: 1.13.3 Depends: R (>= 4.1.0) Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr, igraph, dplyr License: GPL-3 MD5sum: d9d280eac136bb20a1d0d679113319a0 NeedsCompilation: yes Title: Differential gene co-expression Description: This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm. biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression, Network Author: Jakob Peder Pettersen [aut, cre] (ORCID: ) Maintainer: Jakob Peder Pettersen URL: https://almaaslab.github.io/csdR, https://github.com/AlmaasLab/csdR VignetteBuilder: knitr BugReports: https://github.com/AlmaasLab/csdR/issues git_url: https://git.bioconductor.org/packages/csdR git_branch: devel git_last_commit: 0df2dbc git_last_commit_date: 2024-11-07 Date/Publication: 2024-11-07 source.ver: src/contrib/csdR_1.13.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/csdR_1.13.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/csdR_1.13.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/csdR_1.13.3.tgz vignettes: vignettes/csdR/inst/doc/csdR.html vignetteTitles: csdR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csdR/inst/doc/csdR.R dependencyCount: 116 Package: CSSQ Version: 1.19.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: 39349e7e129091c130a2e88de9d72f51 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: devel git_last_commit: 9eae002 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CSSQ_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CSSQ_1.19.0.zip vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 98 Package: ctc Version: 1.81.0 Depends: amap License: GPL-2 Archs: x64 MD5sum: cdb6d6e5d5c38d19bfa84312444b4acf NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: devel git_last_commit: d5fd1b9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ctc_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ctc_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ctc_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ctc_1.81.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTdata Version: 1.7.0 Depends: R (>= 4.2) Imports: ExperimentHub, utils Suggests: testthat (>= 3.0.0), DT, BiocStyle, knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment License: Artistic-2.0 Archs: x64 MD5sum: 57472576a9d787b182165170180c68d6 NeedsCompilation: no Title: Data companion to CTexploreR Description: Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics. biocViews: Transcriptomics, Epigenetics, GeneExpression, DataImport, ExperimentHubSoftware Author: Axelle Loriot [aut] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths, cre] (ORCID: ) Maintainer: Laurent Gatto VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTdata/issues git_url: https://git.bioconductor.org/packages/CTdata git_branch: devel git_last_commit: 0cd0dfa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CTdata_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTdata_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTdata_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTdata_1.7.0.tgz vignettes: vignettes/CTdata/inst/doc/CTdata.html vignetteTitles: Cancer Testis Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTdata/inst/doc/CTdata.R dependsOnMe: CTexploreR dependencyCount: 67 Package: CTDquerier Version: 2.15.0 Depends: R (>= 4.1) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: b55b85b456b8de093160d6d21fd3851b NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [cre] Maintainer: Xavier Escribà-Montagut VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: devel git_last_commit: b79a567 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CTDquerier_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTDquerier_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTDquerier_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTDquerier_2.15.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 72 Package: CTexploreR Version: 1.3.0 Depends: R (>= 4.3), CTdata (>= 1.5.3) Imports: BiocGenerics, ComplexHeatmap, grid, SummarizedExperiment, GenomicRanges, IRanges, dplyr, tidyr, tibble, ggplot2, rlang, grDevices, stats, circlize, ggrepel, SingleCellExperiment, MatrixGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), InteractiveComplexHeatmap License: Artistic-2.0 MD5sum: d86618c1ff57aeab00bbb65162cbc8ee NeedsCompilation: no Title: Explores Cancer Testis Genes Description: The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues. biocViews: Transcriptomics, Epigenetics, DifferentialExpression, GeneExpression, DNAMethylation, ExperimentHubSoftware, DataImport Author: Axelle Loriot [aut, cre] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths] (ORCID: ) Maintainer: Axelle Loriot URL: https://github.com/UCLouvain-CBIO/CTexploreR VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTexploreR/issues git_url: https://git.bioconductor.org/packages/CTexploreR git_branch: devel git_last_commit: b8dffc6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-20 source.ver: src/contrib/CTexploreR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTexploreR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTexploreR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTexploreR_1.3.0.tgz vignettes: vignettes/CTexploreR/inst/doc/CTexploreR.html vignetteTitles: Cancer Testis Explorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTexploreR/inst/doc/CTexploreR.R dependencyCount: 110 Package: cTRAP Version: 1.25.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, htmltools, httr, limma, methods, parallel, pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang, scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes License: MIT + file LICENSE MD5sum: 79b60308ddc324c283f6c40a249ea43e NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: devel git_last_commit: b3d1df5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cTRAP_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cTRAP_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cTRAP_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cTRAP_1.25.0.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: identifying candidate causal perturbations from differential gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 160 Package: ctsGE Version: 1.33.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 4773503d4302b596e2660d7ecc6bbf37 NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: devel git_last_commit: ae475ee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ctsGE_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ctsGE_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ctsGE_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ctsGE_1.33.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 72 Package: CTSV Version: 1.9.0 Depends: R (>= 4.2), Imports: stats, pscl, qvalue, BiocParallel, methods, knitr, SpatialExperiment, SummarizedExperiment Suggests: testthat, BiocStyle License: GPL-3 MD5sum: b074ec849f6ba7aa4fcbb2ddaac6e3df NeedsCompilation: yes Title: Identification of cell-type-specific spatially variable genes accounting for excess zeros Description: The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS. biocViews: GeneExpression, StatisticalMethod, Regression, Spatial, Genetics Author: Jinge Yu Developer [aut, cre], Xiangyu Luo Developer [aut] Maintainer: Jinge Yu Developer URL: https://github.com/jingeyu/CTSV VignetteBuilder: knitr BugReports: https://github.com/jingeyu/CTSV/issues git_url: https://git.bioconductor.org/packages/CTSV git_branch: devel git_last_commit: cd7cd49 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CTSV_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CTSV_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CTSV_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CTSV_1.9.0.tgz vignettes: vignettes/CTSV/inst/doc/CTSV.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTSV/inst/doc/CTSV.R dependencyCount: 106 Package: cummeRbund Version: 2.49.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 1d077049bdbb56c7ad659b1c494cbbf1 NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: devel git_last_commit: ddf62c2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cummeRbund_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cummeRbund_2.49.0.zip vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependencyCount: 160 Package: CuratedAtlasQueryR Version: 1.5.0 Depends: R (>= 4.2.0) Imports: dplyr, SummarizedExperiment, SingleCellExperiment, purrr (>= 1.0.0), BiocGenerics, glue, HDF5Array, DBI, tools, httr, cli, assertthat, SeuratObject, Seurat, methods, rlang, stats, S4Vectors, tibble, utils, dbplyr (>= 2.3.0), duckdb, stringr Suggests: zellkonverter, rmarkdown, knitr, testthat, basilisk, arrow, reticulate, spelling, forcats, ggplot2, tidySingleCellExperiment, rprojroot License: GPL-3 MD5sum: 110141ee34456ae28fcc1e4b52c6e2e0 NeedsCompilation: no Title: Queries the Human Cell Atlas Description: Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre, rev] (ORCID: ), Michael Milton [aut, rev] (ORCID: ), Martin Morgan [ctb, rev], Vincent Carey [ctb, rev], Julie Iskander [rev], Tony Papenfuss [rev], Silicon Valley Foundation CZF2019-002443 [fnd], NIH NHGRI 5U24HG004059-18 [fnd], Victoria Cancer Agency ECRF21036 [fnd], NHMRC 1116955 [fnd] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/CuratedAtlasQueryR VignetteBuilder: knitr BugReports: https://github.com/stemangiola/CuratedAtlasQueryR/issues git_url: https://git.bioconductor.org/packages/CuratedAtlasQueryR git_branch: devel git_last_commit: 7febec3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/CuratedAtlasQueryR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CuratedAtlasQueryR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CuratedAtlasQueryR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CuratedAtlasQueryR_1.5.0.tgz vignettes: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.html vignetteTitles: CuratedAtlasQueryR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.R dependencyCount: 181 Package: customCMPdb Version: 1.17.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: c69d45b43d8f59f29c2181b8d4055339 NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics,AnnotationHubSoftware Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: devel git_last_commit: c0072d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/customCMPdb_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/customCMPdb_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/customCMPdb_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/customCMPdb_1.17.0.tgz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 113 Package: customProDB Version: 1.47.0 Depends: R (>= 3.5.0), IRanges, AnnotationDbi, biomaRt (>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, txdbmaker, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 8f984d30426ec22018c61ed5257593a5 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: devel git_last_commit: 695c102 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/customProDB_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/customProDB_1.47.0.zip vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R dependencyCount: 107 Package: cyanoFilter Version: 1.15.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, flowClust, cytometree, ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: ed0c631580079d5e35eb08d724362b66 NeedsCompilation: no Title: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity Description: An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community. biocViews: FlowCytometry, Clustering, OneChannel Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut] Maintainer: Oluwafemi Olusoji URL: https://github.com/fomotis/cyanoFilter VignetteBuilder: knitr BugReports: https://github.com/fomotis/cyanoFilter/issues git_url: https://git.bioconductor.org/packages/cyanoFilter git_branch: devel git_last_commit: aa13f66 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cyanoFilter_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cyanoFilter_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cyanoFilter_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cyanoFilter_1.15.0.tgz vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying Phytplanktons and Cyanobacteria Population in Flow Cytometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R dependencyCount: 117 Package: cycle Version: 1.61.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: c079f6a45d40d08a9587a25d193ade97 NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: devel git_last_commit: 40c4cd1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cycle_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cycle_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cycle_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cycle_1.61.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 18 Package: cydar Version: 1.31.0 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 MD5sum: ac1cf0b54fabbf2c5d0e9da41ef3d74d NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: devel git_last_commit: 8dc413e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cydar_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cydar_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cydar_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cydar_1.31.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 102 Package: cypress Version: 1.3.0 Depends: R(>= 4.4.0) Imports: stats, abind, sirt, MASS,TOAST, tibble, parallel, preprocessCore, SummarizedExperiment, TCA, PROPER, methods,dplyr, utils, RColorBrewer, graphics, edgeR, BiocParallel, checkmate, mvtnorm, DESeq2, rlang, e1071 Suggests: knitr, rmarkdown, MatrixGenerics, htmltools, RUnit, BiocGenerics, BiocManager, BiocStyle, Biobase License: GPL-2 | GPL-3 MD5sum: 0f2d0c3caf6728c436ced1e6eec80710 NeedsCompilation: no Title: Cell-Type-Specific Power Assessment Description: CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes. biocViews: Software, GeneExpression, DataImport, RNASeq, Sequencing Author: Shilin Yu [aut, cre] (ORCID: ), Guanqun Meng [aut], Wen Tang [aut] Maintainer: Shilin Yu URL: https://github.com/renlyly/cypress VignetteBuilder: knitr BugReports: https://github.com/renlyly/cypress/issues git_url: https://git.bioconductor.org/packages/cypress git_branch: devel git_last_commit: f8d19ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cypress_1.3.0.tar.gz vignettes: vignettes/cypress/inst/doc/cypress.html vignetteTitles: cypress Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cypress/inst/doc/cypress.R dependencyCount: 129 Package: CytoDx Version: 1.27.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: a76e2818defebc6ee52d7db89ca05aba NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: devel git_last_commit: 05557f1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CytoDx_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoDx_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoDx_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoDx_1.27.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 48 Package: cytofQC Version: 1.7.0 Imports: CATALYST, flowCore, e1071, EZtune, gbm, ggplot2, hrbrthemes, matrixStats, randomForest, rmarkdown, SingleCellExperiment, stats, SummarizedExperiment, ssc, S4Vectors, graphics, methods Suggests: gridExtra, knitr, RColorBrewer, testthat, uwot License: Artistic-2.0 MD5sum: eae97fef9f8d5cad360eab08db0efebc NeedsCompilation: no Title: Labels normalized cells for CyTOF data and assigns probabilities for each label Description: cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells. biocViews: Software, SingleCell, Annotation Author: Jill Lundell [aut, cre] (ORCID: ), Kelly Street [aut] (ORCID: ) Maintainer: Jill Lundell URL: https://github.com/jillbo1000/cytofQC VignetteBuilder: knitr BugReports: https://github.com/jillbo1000/cytofQC/issues git_url: https://git.bioconductor.org/packages/cytofQC git_branch: devel git_last_commit: c4c778e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytofQC_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytofQC_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytofQC_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytofQC_1.7.0.tgz vignettes: vignettes/cytofQC/inst/doc/cytofQC.html vignetteTitles: Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofQC/inst/doc/cytofQC.R dependencyCount: 228 Package: CytoGLMM Version: 1.15.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: 9a982cf44a2bf596af4ebf47a05cbd7c NeedsCompilation: no Title: Conditional Differential Analysis for Flow and Mass Cytometry Experiments Description: The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, ImmunoOncology, Regression, StatisticalMethod, Software Author: Christof Seiler [aut, cre] (ORCID: ) Maintainer: Christof Seiler URL: https://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM VignetteBuilder: knitr BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues git_url: https://git.bioconductor.org/packages/CytoGLMM git_branch: devel git_last_commit: a3b4bb6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CytoGLMM_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoGLMM_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoGLMM_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoGLMM_1.15.0.tgz vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html vignetteTitles: CytoGLMM Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R dependencyCount: 173 Package: cytoKernel Version: 1.13.0 Depends: R (>= 4.1) Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 Archs: x64 MD5sum: 19ac987980b40a8b3af152f64249d956 NeedsCompilation: yes Title: Differential expression using kernel-based score test Description: cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. biocViews: ImmunoOncology, Proteomics, SingleCell, Software, OneChannel, FlowCytometry, DifferentialExpression, GeneExpression, Clustering Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: devel git_last_commit: ee6062c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytoKernel_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoKernel_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoKernel_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoKernel_1.13.0.tgz vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html vignetteTitles: The CytoK user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R dependencyCount: 86 Package: cytolib Version: 2.19.0 Depends: R (>= 3.4) Imports: RProtoBufLib LinkingTo: BH(>= 1.84.0.0), RProtoBufLib(>= 2.13.1),Rhdf5lib Suggests: knitr, rmarkdown License: AGPL-3.0-only License_restricts_use: no MD5sum: f1ff44367c26145e846cfafb78c60b92 NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: devel git_last_commit: 6ed0d20 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytolib_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytolib_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytolib_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytolib_2.19.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 3 Package: cytomapper Version: 1.19.0 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) MD5sum: 72bc35f6b440d126a9e33854b8c7671d NeedsCompilation: no Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut] (ORCID: ), Nicolas Damond [aut] (ORCID: ), Tobias Hoch [ctb], Lasse Meyer [cre, ctb] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues git_url: https://git.bioconductor.org/packages/cytomapper git_branch: devel git_last_commit: ded2c6e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytomapper_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytomapper_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytomapper_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytomapper_1.19.0.tgz vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html, vignettes/cytomapper/inst/doc/cytomapper.html vignetteTitles: "On disk storage of images", "Visualization of imaging cytometry data in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R, vignettes/cytomapper/inst/doc/cytomapper.R importsMe: cytoviewer, imcRtools, simpleSeg suggestsMe: spicyWorkflow dependencyCount: 145 Package: CytoMDS Version: 1.3.3 Depends: R (>= 4.4), Biobase Imports: methods, stats, rlang, pracma, withr, flowCore, reshape2, ggplot2, ggrepel, ggforce, patchwork, transport, smacof, BiocParallel, CytoPipeline Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, HDCytoData License: GPL-3 MD5sum: 01a2283c6053f7bb66a452a23c72022b NeedsCompilation: no Title: Low Dimensions projection of cytometry samples Description: This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection. biocViews: FlowCytometry, QualityControl, DimensionReduction, MultidimensionalScaling, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoMDS VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoMDS/issues git_url: https://git.bioconductor.org/packages/CytoMDS git_branch: devel git_last_commit: 7214442 git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-09 source.ver: src/contrib/CytoMDS_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoMDS_1.3.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoMDS_1.3.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoMDS_1.3.3.tgz vignettes: vignettes/CytoMDS/inst/doc/CytoMDS.html vignetteTitles: Low Dimensional Projection of Cytometry Samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoMDS/inst/doc/CytoMDS.R dependencyCount: 192 Package: cytoMEM Version: 1.11.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 6b6b20b55ff738e2d72a61333965a930 NeedsCompilation: no Title: Marker Enrichment Modeling (MEM) Description: MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD. biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry, DataRepresentation, DataImport, CellBiology, SingleCell, Clustering Author: Sierra Lima [aut] (ORCID: ), Kirsten Diggins [aut] (ORCID: ), Jonathan Irish [aut, cre] (ORCID: ) Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: devel git_last_commit: d2f1603 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytoMEM_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoMEM_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoMEM_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoMEM_1.11.0.tgz vignettes: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R dependencyCount: 24 Package: CytoML Version: 2.19.0 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, jsonlite, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, stats, tibble LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: AGPL-3.0-only License_restricts_use: no Archs: x64 MD5sum: f544d1272b12eb55647bb965ecdcb930 NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: devel git_last_commit: 57e3e6e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CytoML_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoML_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoML_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoML_2.19.0.tgz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R suggestsMe: FlowSOM, flowWorkspace, openCyto dependencyCount: 85 Package: CytoPipeline Version: 1.7.0 Depends: R (>= 4.4) Imports: methods, stats, utils, withr, rlang, ggplot2 (>= 3.4.1), ggcyto, BiocFileCache, BiocParallel, flowCore, PeacoQC, flowAI, diagram, jsonlite, scales Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, reshape2, dplyr, CytoPipelineGUI License: GPL-3 MD5sum: 9b9c1c2ebaf363445d3211f740a84dea NeedsCompilation: no Title: Automation and visualization of flow cytometry data analysis pipelines Description: This package provides support for automation and visualization of flow cytometry data analysis pipelines. In the current state, the package focuses on the preprocessing and quality control part. The framework is based on two main S4 classes, i.e. CytoPipeline and CytoProcessingStep. The pipeline steps are linked to corresponding R functions - that are either provided in the CytoPipeline package itself, or exported from a third party package, or coded by the user her/himself. The processing steps need to be specified centrally and explicitly using either a json input file or through step by step creation of a CytoPipeline object with dedicated methods. After having run the pipeline, obtained results at all steps can be retrieved and visualized thanks to file caching (the running facility uses a BiocFileCache implementation). The package provides also specific visualization tools like pipeline workflow summary display, and 1D/2D comparison plots of obtained flowFrames at various steps of the pipeline. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipeline VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipeline/issues git_url: https://git.bioconductor.org/packages/CytoPipeline git_branch: devel git_last_commit: 9ee3877 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CytoPipeline_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoPipeline_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipeline_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoPipeline_1.7.0.tgz vignettes: vignettes/CytoPipeline/inst/doc/CytoPipeline.html, vignettes/CytoPipeline/inst/doc/Demo.html vignetteTitles: Automation and Visualization of Flow Cytometry Data Analysis Pipelines, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipeline/inst/doc/CytoPipeline.R, vignettes/CytoPipeline/inst/doc/Demo.R dependsOnMe: CytoPipelineGUI importsMe: CytoMDS dependencyCount: 136 Package: CytoPipelineGUI Version: 1.5.0 Depends: R (>= 4.4), CytoPipeline Imports: shiny, plotly, ggplot2, flowCore Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, patchwork License: GPL-3 MD5sum: e82fb0ecbb5cb5f48ba97ae619db8923 NeedsCompilation: no Title: GUI's for visualization of flow cytometry data analysis pipelines Description: This package is the companion of the `CytoPipeline` package. It provides GUI's (shiny apps) for the visualization of flow cytometry data analysis pipelines that are run with `CytoPipeline`. Two shiny applications are provided, i.e. an interactive flow frame assessment and comparison tool and an interactive scale transformations visualization and adjustment tool. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization, GUI, ShinyApps Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipelineGUI VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipelineGUI/issues git_url: https://git.bioconductor.org/packages/CytoPipelineGUI git_branch: devel git_last_commit: d7627a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/CytoPipelineGUI_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/CytoPipelineGUI_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipelineGUI_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CytoPipelineGUI_1.5.0.tgz vignettes: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.html, vignettes/CytoPipelineGUI/inst/doc/Demo.html vignetteTitles: CytoPipelineGUI : visualization of Flow Cytometry Data Analysis Pipelines run with CytoPipeline, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.R, vignettes/CytoPipelineGUI/inst/doc/Demo.R suggestsMe: CytoPipeline dependencyCount: 148 Package: cytoviewer Version: 1.7.0 Imports: shiny, shinydashboard, utils, colourpicker, shinycssloaders, svgPanZoom, viridis, archive, grDevices, RColorBrewer, svglite, EBImage, methods, cytomapper, SingleCellExperiment, S4Vectors, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: 387a359545f6a1d55d1aceaf1ab1da9d NeedsCompilation: no Title: An interactive multi-channel image viewer for R Description: This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultiChannel, Spatial, DataImport Author: Lasse Meyer [aut, cre] (ORCID: ), Nils Eling [aut] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytoviewer VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytoviewer/issues git_url: https://git.bioconductor.org/packages/cytoviewer git_branch: devel git_last_commit: 43409c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/cytoviewer_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/cytoviewer_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/cytoviewer_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/cytoviewer_1.7.0.tgz vignettes: vignettes/cytoviewer/inst/doc/cytoviewer.html vignetteTitles: "Interactive multi-channel image visualization in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoviewer/inst/doc/cytoviewer.R dependencyCount: 151 Package: dada2 Version: 1.35.0 Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-2 Archs: x64 MD5sum: d767058a803a99a0088bcdc1b36d0a0b NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: devel git_last_commit: 3d68997 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dada2_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dada2_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dada2_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dada2_1.35.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R dependsOnMe: MiscMetabar importsMe: Rbec, DBTC suggestsMe: mia dependencyCount: 93 Package: dagLogo Version: 1.45.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack, httr Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: ca53899791ab56a7ec86040840135b5b NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: devel git_last_commit: e5e73a7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dagLogo_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dagLogo_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dagLogo_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dagLogo_1.45.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 162 Package: daMA Version: 1.79.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 6e4f9cd46aede1142190567da6366c7a NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: devel git_last_commit: d03098c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/daMA_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/daMA_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/daMA_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/daMA_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.19.0 Depends: R (>= 4.0) Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: f6b04e6869776783be1f6b7d096b5020 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (ORCID: ), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: devel git_last_commit: faefd56 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DAMEfinder_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DAMEfinder_1.19.0.zip vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 123 Package: DaMiRseq Version: 2.19.0 Depends: R (>= 3.5.0), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: a0f37a727c6ec9e1a4da2479ef478c5a NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: devel git_last_commit: 83ca733 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DaMiRseq_2.19.0.tar.gz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 251 Package: Damsel Version: 1.3.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, Biostrings, ComplexHeatmap, dplyr, edgeR, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggbio, ggplot2, goseq, magrittr, patchwork, plyranges, reshape2, rlang, Rsamtools, Rsubread, stats, stringr, tidyr, utils Suggests: BiocStyle, biomaRt, biovizBase, BSgenome.Dmelanogaster.UCSC.dm6, knitr, limma, org.Dm.eg.db, rmarkdown, testthat (>= 3.0.0), TxDb.Dmelanogaster.UCSC.dm6.ensGene License: MIT + file LICENSE Archs: x64 MD5sum: e70cdd63daa72624366351094f6647bf NeedsCompilation: no Title: Damsel: an end to end analysis of DamID Description: Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R. biocViews: DifferentialMethylation, PeakDetection, GenePrediction, GeneSetEnrichment Author: Caitlin Page [aut, cre] (ORCID: ) Maintainer: Caitlin Page URL: https://github.com/Oshlack/Damsel VignetteBuilder: knitr BugReports: https://github.com/Oshlack/Damsel git_url: https://git.bioconductor.org/packages/Damsel git_branch: devel git_last_commit: 7bdd2de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Damsel_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Damsel_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Damsel_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Damsel_1.3.0.tgz vignettes: vignettes/Damsel/inst/doc/Damsel-workflow.html vignetteTitles: Damsel-workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Damsel/inst/doc/Damsel-workflow.R dependencyCount: 182 Package: DAPAR Version: 1.39.0 Depends: R (>= 4.3.0) Imports: Biobase, MSnbase, DAPARdata (>= 1.30.0), utils, highcharter, foreach Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph, diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR, factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster, forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr, norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr, limma, preprocessCore, stringr, tidyr, impute, gplots, grDevices, reshape2, graphics, stats, methods, ggplot2, RColorBrewer, Matrix, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 4a962ed160ce46e3fa72e9d6a9fdf327 NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package). biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: c(person(given = "Samuel", family = "Wieczorek", email = "samuel.wieczorek@cea.fr", role = c("aut","cre")), person(given = "Florence", family ="Combes", email = "florence.combes@cea.fr", role = "aut"), person(given = "Thomas", family ="Burger", email = "thomas.burger@cea.fr", role = "aut"), person(given = "Vasile-Cosmin", family ="Lazar", email = "vcosminlazar@gmail.com", role = "ctb"), person(given = "Enora", family ="Fremy", email = "enora.fremy@cea.fr", role = "ctb"), person(given = "Helene", family ="Borges", email = "helene.borges@cea.fr", role = "ctb")) Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: devel git_last_commit: 2500d2a git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/DAPAR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DAPAR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DAPAR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DAPAR_1.39.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: Prostar suggestsMe: DAPARdata, mi4p dependencyCount: 150 Package: DART Version: 1.55.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 36a2a2fcff52bb2bf06ce58191b633de NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: devel git_last_commit: 06d231f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DART_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DART_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DART_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DART_1.55.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 17 Package: dcanr Version: 1.23.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 8d6ed7913505b790d657ee2838168868 NeedsCompilation: no Title: Differential co-expression/association network analysis Description: This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease). biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: devel git_last_commit: 1a99f0d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dcanr_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dcanr_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dcanr_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dcanr_1.23.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: multiWGCNA, SingscoreAMLMutations dependencyCount: 35 Package: DCATS Version: 1.5.0 Depends: R (>= 4.1.0), stats Imports: MCMCpack, matrixStats, robustbase, aod, e1071 Suggests: testthat (>= 3.0.0), knitr, Seurat, SeuratObject, tidyverse, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 1faa56842660f648d975a7435404ab5e NeedsCompilation: no Title: Differential Composition Analysis Transformed by a Similarity matrix Description: Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term. biocViews: SingleCell, Normalization Author: Xinyi Lin [aut, cre] (ORCID: ), Chuen Chau [aut], Yuanhua Huang [aut], Joshua W.K. Ho [aut] Maintainer: Xinyi Lin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DCATS git_branch: devel git_last_commit: 61682fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DCATS_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DCATS_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DCATS_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DCATS_1.5.0.tgz vignettes: vignettes/DCATS/inst/doc/Intro_to_DCATS.html vignetteTitles: Differential Composition Analysis with DCATS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DCATS/inst/doc/Intro_to_DCATS.R dependencyCount: 24 Package: dce Version: 1.13.0 Depends: R (>= 4.1) Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, magrittr, glue, Rgraphviz, harmonicmeanp, org.Hs.eg.db, logger, shadowtext Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, cowplot, ggplotify, dagitty, lmtest, sandwich, devtools, curatedTCGAData, TCGAutils, SummarizedExperiment, RcppParallel, docopt, CARNIVAL License: GPL-3 MD5sum: a369874285bc5f4d10415002ecdc1f03 NeedsCompilation: no Title: Pathway Enrichment Based on Differential Causal Effects Description: Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network, KEGG Author: Kim Philipp Jablonski [aut, cre] (ORCID: ), Martin Pirkl [aut] Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/dce VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/dce/issues git_url: https://git.bioconductor.org/packages/dce git_branch: devel git_last_commit: 642d99a git_last_commit_date: 2024-04-30 Date/Publication: 2024-10-25 source.ver: src/contrib/dce_1.13.0.tar.gz vignettes: vignettes/dce/inst/doc/dce.html, vignettes/dce/inst/doc/pathway_databases.html vignetteTitles: Get started, Overview of pathway network databases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dce/inst/doc/dce.R, vignettes/dce/inst/doc/pathway_databases.R dependencyCount: 243 Package: dcGSA Version: 1.35.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 8b69f62dae97659decd1474bf8dc025a NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: devel git_last_commit: a50f905 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dcGSA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dcGSA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dcGSA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dcGSA_1.35.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: ddCt Version: 1.63.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: testthat (>= 3.0.0), RUnit License: LGPL-3 MD5sum: 504a29359b77f2c92062c045efc9ccd8 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: devel git_last_commit: 3e97954 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ddCt_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ddCt_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ddCt_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ddCt_1.63.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 12 Package: ddPCRclust Version: 1.27.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: fcbab27498957051a01a8cfd2d6bdd08 NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: devel git_last_commit: b05f1a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ddPCRclust_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ddPCRclust_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ddPCRclust_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ddPCRclust_1.27.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R dependencyCount: 104 Package: dearseq Version: 1.19.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr, matrixStats, methods, patchwork, parallel, pbapply, reshape2, rlang, scattermore, stats, statmod, survey, tibble, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: d844621512866203027e15927a6ae97d NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre] (ORCID: ), Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: devel git_last_commit: 2e8a88c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dearseq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dearseq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dearseq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dearseq_1.19.0.tgz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R suggestsMe: GeoTcgaData, TcGSA dependencyCount: 59 Package: debCAM Version: 1.25.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 MD5sum: 5a9e48ce4f179d28d875fcf6934a5c8f NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: devel git_last_commit: f694119 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/debCAM_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/debCAM_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/debCAM_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/debCAM_1.25.0.tgz vignettes: vignettes/debCAM/inst/doc/debcam.html vignetteTitles: debCAM User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/debCAM/inst/doc/debcam.R dependencyCount: 118 Package: debrowser Version: 1.35.0 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, knitr License: GPL-3 + file LICENSE MD5sum: a507a1fa8f86e785267477165496caaa NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: devel git_last_commit: d896173 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/debrowser_1.35.0.tar.gz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 226 Package: DECIPHER Version: 3.3.2 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), stats Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector Suggests: RSQLite (>= 1.1) License: GPL-3 MD5sum: d94ea706af8c8ee1ac9ac9db9eba6993 NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright URL: http://DECIPHER.codes git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: devel git_last_commit: c3647f8 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/DECIPHER_3.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/DECIPHER_3.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DECIPHER_3.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DECIPHER_3.3.2.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/ClusteringSequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf, vignettes/DECIPHER/inst/doc/GrowingTrees.pdf, vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf, vignettes/DECIPHER/inst/doc/SearchForResearch.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Upsize Your Clustering with Clusterize, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers that Yield Group-Specific Signatures, Finding Chimeric Sequences, The Magic of Gene Finding, The Double Life of RNA: Uncovering Non-Coding RNAs, Growing Phylogenetic Trees with Treeline, Detecting Obscure Tandem Repeats in Sequences, Searching biological sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/ClusteringSequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R, vignettes/DECIPHER/inst/doc/GrowingTrees.R, vignettes/DECIPHER/inst/doc/RepeatRepeat.R, vignettes/DECIPHER/inst/doc/SearchForResearch.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: mia, openPrimeR, scifer, copyseparator, ensembleTax suggestsMe: MicrobiotaProcess, microbial, MiscMetabar dependencyCount: 27 Package: decompTumor2Sig Version: 2.23.0 Depends: R(>= 4.0), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, GenomeInfoDb, readxl Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 62e274e3d8f966ccd7083d3209ee96ec NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: devel git_last_commit: 7907da8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/decompTumor2Sig_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decompTumor2Sig_2.23.0.zip vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 114 Package: DeconRNASeq Version: 1.49.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: ffe78f38b7c3ff729592fb67d6863f24 NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: devel git_last_commit: 3a944d5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DeconRNASeq_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeconRNASeq_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeconRNASeq_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeconRNASeq_1.49.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R suggestsMe: ADAPTS dependencyCount: 43 Package: decontam Version: 1.27.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: 7ac6c33f8479c403a0c1f20f6861f2b4 NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix G.M. Ernst [ctb] (ORCID: ) Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: devel git_last_commit: 2222eff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/decontam_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decontam_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/decontam_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/decontam_1.27.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R importsMe: mia dependencyCount: 41 Package: decontX Version: 1.5.0 Depends: R (>= 4.3.0) Imports: celda, dbscan, DelayedArray, ggplot2, Matrix (>= 1.5.3), MCMCprecision, methods, patchwork, plyr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.2.0), S4Vectors, scater, Seurat, SingleCellExperiment, SummarizedExperiment, withr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, dplyr, knitr, rmarkdown, scran, SingleCellMultiModal, TENxPBMCData, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: bb83b24c89c91b8693aa68bed709e0ae NeedsCompilation: yes Title: Decontamination of single cell genomics data Description: This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset. biocViews: SingleCell, Bayesian Author: Yuan Yin [aut, cre] (ORCID: ), Masanao Yajima [aut] (ORCID: ), Joshua Campbell [aut] (ORCID: ) Maintainer: Yuan Yin SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/decontX git_branch: devel git_last_commit: b7aed7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/decontX_1.5.0.tar.gz vignettes: vignettes/decontX/inst/doc/decontPro.html, vignettes/decontX/inst/doc/decontX.html vignetteTitles: decontPro, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decontX/inst/doc/decontPro.R, vignettes/decontX/inst/doc/decontX.R dependencyCount: 237 Package: deconvR Version: 1.13.0 Depends: R (>= 4.1), data.table (>= 1.14.0) Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>= 2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0), stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1), matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8), nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7), tidyr (>= 1.1.3), assertthat, minfi Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>= 1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2), reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools (>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator, RefManageR License: Artistic-2.0 MD5sum: 53e436b5a8ba6a51a18e5c53f4e4f3da NeedsCompilation: no Title: Simulation and Deconvolution of Omic Profiles Description: This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier. biocViews: DNAMethylation, Regression, GeneExpression, RNASeq, SingleCell, StatisticalMethod, Transcriptomics Author: Irem B. Gündüz [aut, cre] (ORCID: ), Veronika Ebenal [aut] (ORCID: ), Altuna Akalin [aut] (ORCID: ) Maintainer: Irem B. Gündüz URL: https://github.com/BIMSBbioinfo/deconvR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/deconvR git_url: https://git.bioconductor.org/packages/deconvR git_branch: devel git_last_commit: 02212ba git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-27 source.ver: src/contrib/deconvR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deconvR_1.13.0.zip vignettes: vignettes/deconvR/inst/doc/deconvRVignette.html vignetteTitles: deconvRVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deconvR/inst/doc/deconvRVignette.R dependencyCount: 183 Package: decoupleR Version: 2.13.0 Depends: R (>= 4.0) Imports: BiocParallel, broom, dplyr, magrittr, Matrix, parallelly, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1-7), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel, patchwork, magick License: GPL-3 + file LICENSE Archs: x64 MD5sum: b062e20601a2d330e2a75b63cb7c56c2 NeedsCompilation: no Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (ORCID: ), Jesús Vélez-Santiago [aut] (ORCID: ), Jana Braunger [aut] (ORCID: ), Celina Geiss [aut] (ORCID: ), Daniel Dimitrov [aut] (ORCID: ), Sophia Müller-Dott [aut] (ORCID: ), Petr Taus [aut] (ORCID: ), Aurélien Dugourd [aut] (ORCID: ), Christian H. Holland [aut] (ORCID: ), Ricardo O. Ramirez Flores [aut] (ORCID: ), Julio Saez-Rodriguez [aut] (ORCID: ) Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues git_url: https://git.bioconductor.org/packages/decoupleR git_branch: devel git_last_commit: 1d86763 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/decoupleR_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/decoupleR_2.13.0.zip vignettes: vignettes/decoupleR/inst/doc/decoupleR.html, vignettes/decoupleR/inst/doc/pw_bk.html, vignettes/decoupleR/inst/doc/pw_sc.html, vignettes/decoupleR/inst/doc/tf_bk.html, vignettes/decoupleR/inst/doc/tf_sc.html vignetteTitles: Introduction, Pathway activity inference in bulk RNA-seq, Pathway activity activity inference from scRNA-seq, Transcription factor activity inference in bulk RNA-seq, Transcription factor activity inference from scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R, vignettes/decoupleR/inst/doc/pw_bk.R, vignettes/decoupleR/inst/doc/pw_sc.R, vignettes/decoupleR/inst/doc/tf_bk.R, vignettes/decoupleR/inst/doc/tf_sc.R importsMe: cosmosR, easier, progeny suggestsMe: SCpubr dependencyCount: 42 Package: DeepPINCS Version: 1.15.0 Depends: keras, R (>= 4.1) Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 9b9c27503b422628bfaf8f426062b3fc NeedsCompilation: no Title: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning Description: The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences. biocViews: Software, Network, GraphAndNetwork, NeuralNetwork Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: devel git_last_commit: 70f36a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DeepPINCS_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeepPINCS_1.15.0.tgz vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html vignetteTitles: DeepPINCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R importsMe: GenProSeq, VAExprs dependencyCount: 145 Package: deepSNV Version: 1.53.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.27.6), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 MD5sum: a66619f626641914eacc934bd78c3126 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: devel git_last_commit: d480433 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/deepSNV_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deepSNV_1.53.0.zip vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R importsMe: mitoClone2 suggestsMe: GenomicFiles dependencyCount: 82 Package: DeepTarget Version: 1.1.0 Depends: R (>= 4.2.0) Imports: fgsea, ggplot2, stringr, ggpubr, BiocParallel, pROC, stats, grDevices, graphics, depmap, readr, dplyr Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: b47b6fabcd4147b8f5e8b2f50c9ec4ba NeedsCompilation: no Title: Deep characterization of cancer drugs Description: This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example. biocViews: GeneTarget, GenePrediction,Pathways, GeneExpression, RNASeq, ImmunoOncology,DifferentialExpression, GeneSetEnrichment, ReportWriting,CRISPR Author: Sanju Sinha [aut], Trinh Nguyen [aut, cre] (ORCID: ), Ying Hu [aut] Maintainer: Trinh Nguyen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepTarget git_branch: devel git_last_commit: 3398b5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DeepTarget_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeepTarget_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeepTarget_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeepTarget_1.1.0.tgz vignettes: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.html vignetteTitles: Workflow Demonstration for Deep characterization of cancer drugs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.R dependencyCount: 139 Package: DEFormats Version: 1.35.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: 54bcb46a47b211c2be35f0279b5f98be NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: devel git_last_commit: 05370ad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEFormats_1.35.0.tar.gz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R suggestsMe: ideal dependencyCount: 83 Package: DegCre Version: 1.3.0 Depends: R (>= 4.4) Imports: GenomicRanges, InteractionSet, plotgardener, S4Vectors, stats, graphics, grDevices, BiocGenerics, GenomeInfoDb, IRanges, BiocParallel, qvalue, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, utils Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg38, BiocStyle, magick, knitr, rmarkdown, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 53dd1a5436d63aef7abdbe68af6231ca NeedsCompilation: no Title: Probabilistic association of DEGs to CREs from differential data Description: DegCre generates associations between differentially expressed genes (DEGs) and cis-regulatory elements (CREs) based on non-parametric concordance between differential data. The user provides GRanges of DEG TSS and CRE regions with differential p-value and optionally log-fold changes and DegCre returns an annotated Hits object with associations and their calculated probabilities. Additionally, the package provides functionality for visualization and conversion to other formats. biocViews: GeneExpression, GeneRegulation, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Brian S. Roberts [aut, cre] (ORCID: ) Maintainer: Brian S. Roberts URL: https://github.com/brianSroberts/DegCre VignetteBuilder: knitr BugReports: https://github.com/brianSroberts/DegCre/issues git_url: https://git.bioconductor.org/packages/DegCre git_branch: devel git_last_commit: a35e1e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DegCre_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DegCre_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DegCre_1.3.0.tgz vignettes: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.html vignetteTitles: DegCre Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.R dependencyCount: 122 Package: DegNorm Version: 1.17.0 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, txdbmaker, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) MD5sum: a270193c5a572ef23c5e4f5507f920ad NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Bin Xiong and Ji-Ping Wang Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: devel git_last_commit: 3be95e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DegNorm_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DegNorm_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DegNorm_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DegNorm_1.17.0.tgz vignettes: vignettes/DegNorm/inst/doc/DegNorm.html vignetteTitles: DegNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R dependencyCount: 163 Package: DEGraph Version: 1.59.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: ffc8b9f4519a263767159b1f9372e139 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: devel git_last_commit: 6c12c51 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEGraph_1.59.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 65 Package: DEGreport Version: 1.43.0 Depends: R (>= 4.0.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, dendextend, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, magrittr, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, stringi, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: 867aaed2658d76b50baa36f5492074d2 NeedsCompilation: no Title: Report of DEG analysis Description: Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb], Ivo Zeller [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: devel git_last_commit: d93b674 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEGreport_1.43.0.tar.gz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs dependencyCount: 121 Package: DEGseq Version: 1.61.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) MD5sum: eefebb29199988229ea722e8d7759939 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang , Xiaowo Wang and Xuegong Zhang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: devel git_last_commit: 8770bb2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEGseq_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEGseq_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEGseq_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEGseq_1.61.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 42 Package: DelayedArray Version: 0.33.3 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.53.3), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3), S4Arrays (>= 1.5.4), SparseArray (>= 1.5.42) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 MD5sum: 9e101b768fd9908a9904bc1510627ede NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès [aut, cre], Aaron Lun [ctb], Peter Hickey [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: devel git_last_commit: a782607 git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-02 source.ver: src/contrib/DelayedArray_0.33.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedArray_0.33.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedArray_0.33.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedArray_0.33.3.tgz vignettes: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/B-Implementing_a_backend.html vignetteTitles: 1. Working with large arrays in R (slides from July 2017), 3. A DelayedArray / HDF5Array update (slides from April 2021), 2. Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.R dependsOnMe: chihaya, DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, Rarr, rhdf5client, SCArray, singleCellTK, SQLDataFrame, TileDBArray, VCFArray importsMe: adverSCarial, alabaster.matrix, AUCell, batchelor, beachmat, beachmat.hdf5, BiocSingular, bsseq, celaref, celda, Cepo, ChromSCape, clusterExperiment, concordexR, CRISPRseek, cytomapper, decontX, DelayedTensor, DEScan2, dreamlet, DropletUtils, ELMER, EWCE, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, Macarron, mariner, mbkmeans, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData, MultiAssayExperiment, mumosa, NetActivity, netSmooth, NewWave, orthogene, orthos, PCAtools, ResidualMatrix, RTCGAToolbox, ScaledMatrix, SCArray.sat, scater, scDblFinder, scFeatures, scMerge, scmeth, scPCA, scran, scrapper, scry, scuttle, signatureSearch, SingleCellAlleleExperiment, SingleCellExperiment, SingleR, sketchR, SpliceWiz, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, Voyager, weitrix, xcore, zellkonverter, ZygosityPredictor, celldex, imcdatasets, scRNAseq, ebvcube, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, gwascat, hermes, iSEE, lute, MAST, MatrixGenerics, ProteoDisco, S4Arrays, S4Vectors, satuRn, SPOTlight, TrajectoryUtils, Seurat, SeuratObject, SpatialDDLS dependencyCount: 22 Package: DelayedDataFrame Version: 1.23.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, BiocStyle, SeqArray, GDSArray License: GPL-3 Archs: x64 MD5sum: aa4af3adaf7326d0ed26d2e320ca864b NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: devel git_last_commit: 049374e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DelayedDataFrame_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedDataFrame_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedDataFrame_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedDataFrame_1.23.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R importsMe: VariantExperiment dependencyCount: 23 Package: DelayedMatrixStats Version: 1.29.0 Depends: MatrixGenerics (>= 1.15.1), DelayedArray (>= 0.31.7) Imports: methods, sparseMatrixStats (>= 1.13.2), Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10), SparseArray (>= 1.5.19) Suggests: testthat, knitr, rmarkdown, BiocStyle, microbenchmark, profmem, HDF5Array, matrixStats (>= 1.0.0) License: MIT + file LICENSE MD5sum: 321110b0bba988b8aa287257c23a1bff NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: devel git_last_commit: bfe5e8b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DelayedMatrixStats_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedMatrixStats_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedMatrixStats_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedMatrixStats_1.29.0.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: AUCell, batchelor, biscuiteer, bsseq, Cepo, dmrseq, dreamlet, DropletUtils, FRASER, glmGamPoi, GSVA, lemur, methrix, methylSig, mia, minfi, mumosa, NetActivity, PCAtools, recountmethylation, SCArray, scFeatures, scMerge, scone, singleCellTK, SingleR, sparrow, SpliceWiz, weitrix, celldex suggestsMe: condiments, DelayedArray, EWCE, HDF5Array, lute, MatrixGenerics, mbkmeans, ScaledMatrix, scater, scPCA, scran, scuttle, slingshot, SplineDV, tradeSeq, TrajectoryUtils, SpatialDDLS dependencyCount: 25 Package: DelayedRandomArray Version: 1.15.0 Depends: SparseArray (>= 1.5.15), DelayedArray (>= 0.31.6) Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 Archs: x64 MD5sum: 2d3c6f79e034dd9e1809382d6cd8a0cd NeedsCompilation: yes Title: Delayed Arrays of Random Values Description: Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters. biocViews: DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/DelayedRandomArray SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/DelayedRandomArray/issues git_url: https://git.bioconductor.org/packages/DelayedRandomArray git_branch: devel git_last_commit: 7ae113b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DelayedRandomArray_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedRandomArray_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedRandomArray_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedRandomArray_1.15.0.tgz vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R importsMe: DelayedTensor dependencyCount: 27 Package: DelayedTensor Version: 1.13.0 Depends: R (>= 4.1.0) Imports: methods, utils, S4Arrays, SparseArray, DelayedArray (>= 0.31.8), HDF5Array, BiocSingular, rTensor, DelayedRandomArray (>= 1.13.1), irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 Archs: x64 MD5sum: c2708e6eab7fa0e20accee421c0d3116 NeedsCompilation: no Title: R package for sparse and out-of-core arithmetic and decomposition of Tensor Description: DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum. biocViews: Software, Infrastructure, DataRepresentation, DimensionReduction Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues git_url: https://git.bioconductor.org/packages/DelayedTensor git_branch: devel git_last_commit: 35f745f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DelayedTensor_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DelayedTensor_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DelayedTensor_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DelayedTensor_1.13.0.tgz vignettes: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.html vignetteTitles: DelayedTensor, TensorArithmetic, TensorDecomposition, Einsum hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.R dependencyCount: 51 Package: DELocal Version: 1.7.0 Imports: DESeq2, dplyr, reshape2, limma, SummarizedExperiment, ggplot2, matrixStats, stats Suggests: biomaRt, knitr, rmarkdown, stringr, BiocStyle License: MIT + file LICENSE MD5sum: 6f70db054ef9559d18011b75f96f899d NeedsCompilation: no Title: Identifies differentially expressed genes with respect to other local genes Description: The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location. It has been shown that genes of related functions are generally very far from each other in the chromosome. DELocal utilzes this information to identify DE genes comparing with their neighbouring genes. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/DELocal VignetteBuilder: knitr BugReports: https://github.com/dasroy/DELocal/issues git_url: https://git.bioconductor.org/packages/DELocal git_branch: devel git_last_commit: b84dfd4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DELocal_1.7.0.tar.gz vignettes: vignettes/DELocal/inst/doc/DELocal.html vignetteTitles: DELocal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DELocal/inst/doc/DELocal.R importsMe: broadSeq dependencyCount: 85 Package: deltaCaptureC Version: 1.21.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 906a86248863e4b8cadcc85421302d3c NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: devel git_last_commit: 10a66b3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/deltaCaptureC_1.21.0.tar.gz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 78 Package: deltaGseg Version: 1.47.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 Archs: x64 MD5sum: b0aaec8022fe71ee6b63e03006d0fe0f NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: devel git_last_commit: 6042fb0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/deltaGseg_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/deltaGseg_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/deltaGseg_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/deltaGseg_1.47.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 55 Package: DeMAND Version: 1.37.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: f7b6b188d3d60f7a336c680b767601ff NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: devel git_last_commit: 4822c1c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DeMAND_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeMAND_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeMAND_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeMAND_1.37.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.23.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown, DSS, dendextend, psych, sva Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 MD5sum: 45925f5de82015e41f7daf8d1cb783f4 NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Ruonan Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: devel git_last_commit: eb1e461 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DeMixT_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DeMixT_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeMixT_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DeMixT_1.23.0.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 150 Package: demuxmix Version: 1.9.0 Depends: R (>= 4.0.0) Imports: stats, MASS, Matrix, ggplot2, gridExtra, methods Suggests: BiocStyle, cowplot, DropletUtils, knitr, reshape2, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 33a16f9d3f8904d577533fa81e5513ad NeedsCompilation: no Title: Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models Description: A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided. biocViews: SingleCell, Sequencing, Preprocessing, Classification, Regression Author: Hans-Ulrich Klein [aut, cre] (ORCID: ) Maintainer: Hans-Ulrich Klein URL: https://github.com/huklein/demuxmix VignetteBuilder: knitr BugReports: https://github.com/huklein/demuxmix/issues git_url: https://git.bioconductor.org/packages/demuxmix git_branch: devel git_last_commit: 6169ab1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/demuxmix_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/demuxmix_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/demuxmix_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/demuxmix_1.9.0.tgz vignettes: vignettes/demuxmix/inst/doc/demuxmix.html vignetteTitles: Demultiplexing cells with demuxmix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxmix/inst/doc/demuxmix.R importsMe: demuxSNP dependencyCount: 36 Package: demuxSNP Version: 1.5.0 Depends: R (>= 4.3.0), SingleCellExperiment, VariantAnnotation, ensembldb Imports: MatrixGenerics, BiocGenerics, class, GenomeInfoDb, IRanges, Matrix, SummarizedExperiment, demuxmix, methods, KernelKnn, dplyr Suggests: knitr, rmarkdown, ComplexHeatmap, viridisLite, ggpubr, dittoSeq, EnsDb.Hsapiens.v86, BiocStyle, RefManageR, testthat (>= 3.0.0), Seurat License: GPL-3 MD5sum: 9f3c3723cc7f3284256bda2e68cf5f1c NeedsCompilation: no Title: scRNAseq demultiplexing using cell hashing and SNPs Description: This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework. biocViews: Classification, SingleCell Author: Michael Lynch [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Michael Lynch URL: https://github.com/michaelplynch/demuxSNP VignetteBuilder: knitr BugReports: https://github.com/michaelplynch/demuxSNP/issues git_url: https://git.bioconductor.org/packages/demuxSNP git_branch: devel git_last_commit: 5ce455e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/demuxSNP_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/demuxSNP_1.5.0.zip vignettes: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.html vignetteTitles: Supervised Demultiplexing using Cell Hashing and SNPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.R dependencyCount: 113 Package: densvis Version: 1.17.0 Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat License: MIT + file LICENSE MD5sum: b377783448749a39b9503547d67058be NeedsCompilation: yes Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/densvis VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: devel git_last_commit: af1be7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/densvis_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/densvis_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/densvis_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/densvis_1.17.0.tgz vignettes: vignettes/densvis/inst/doc/densvis.html vignetteTitles: Introduction to densvis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/densvis/inst/doc/densvis.R suggestsMe: scater dependencyCount: 27 Package: DEP Version: 1.29.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 MD5sum: 034d1a7a29c1259dd8ebf6245721a0ff NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: devel git_last_commit: 44e537d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEP_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEP_1.29.0.zip vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA, RforProteomics dependencyCount: 171 Package: DepecheR Version: 1.23.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1), collapse (>= 1.9.2), ClusterR (>= 1.3.2) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: d631e6d53a21f8deda581c7b425ce05f NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre] (ORCID: ), Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: devel git_last_commit: 68cbb01 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DepecheR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DepecheR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DepecheR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DepecheR_1.23.0.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 111 Package: DepInfeR Version: 1.11.0 Depends: R (>= 4.2.0) Imports: matrixStats, glmnet, stats, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble, ggplot2, missForest, pheatmap, RColorBrewer, ggrepel, BiocStyle, ggbeeswarm License: GPL-3 MD5sum: 77423bf73f100a9bcfa5ac7085169be5 NeedsCompilation: no Title: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Description: DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864. biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics, FunctionalGenomics Author: Junyan Lu [aut, cre] (ORCID: ), Alina Batzilla [aut] Maintainer: Junyan Lu VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues git_url: https://git.bioconductor.org/packages/DepInfeR git_branch: devel git_last_commit: 903c5e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DepInfeR_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DepInfeR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DepInfeR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DepInfeR_1.11.0.tgz vignettes: vignettes/DepInfeR/inst/doc/vignette.html vignetteTitles: DepInfeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DepInfeR/inst/doc/vignette.R dependencyCount: 27 Package: DEqMS Version: 1.25.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: 10ce878650a4980d9f0c2d86d60f34cf NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian,ExperimentHubSoftware Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: devel git_last_commit: bdb1c60 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEqMS_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEqMS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEqMS_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEqMS_1.25.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R importsMe: PRONE dependencyCount: 38 Package: derfinder Version: 1.41.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 Archs: x64 MD5sum: ae6046cca4147a0c7695b4ee55312cbf NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: devel git_last_commit: 5234647 git_last_commit_date: 2024-12-13 Date/Publication: 2024-12-13 source.ver: src/contrib/derfinder_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinder_1.41.0.zip vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: derfinderPlot, recount, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 145 Package: derfinderHelper Version: 1.41.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: 1373aa17454dfa24799b599d1b3704e8 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: devel git_last_commit: 4dbfb34 git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-13 source.ver: src/contrib/derfinderHelper_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinderHelper_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/derfinderHelper_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/derfinderHelper_1.41.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 13 Package: derfinderPlot Version: 1.41.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 Archs: x64 MD5sum: f565592709ac0744a07ccb059f820f86 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: devel git_last_commit: b8a1580 git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-13 source.ver: src/contrib/derfinderPlot_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/derfinderPlot_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/derfinderPlot_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/derfinderPlot_1.41.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: recountWorkflow suggestsMe: derfinder, GenomicState dependencyCount: 175 Package: DEScan2 Version: 1.27.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 MD5sum: abac8e353578145f5909d40f4fdad9ca NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: devel git_last_commit: b34d6d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEScan2_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEScan2_1.27.0.zip vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 135 Package: DESeq2 Version: 1.47.1 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, matrixStats, methods, stats4, locfit, ggplot2 (>= 3.4.0), Rcpp (>= 0.11.0), MatrixGenerics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) MD5sum: e1fda65b18b39ec278dd0cfc7d9506de NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: devel git_last_commit: 30cc350 git_last_commit_date: 2024-11-14 Date/Publication: 2024-11-14 source.ver: src/contrib/DESeq2_1.47.1.tar.gz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, metaseqR2, octad, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, BatchQC, broadSeq, CeTF, circRNAprofiler, CleanUpRNAseq, consensusDE, coseq, countsimQC, cypress, DaMiRseq, debrowser, DEFormats, DEGreport, DELocal, deltaCaptureC, DEsubs, DiffBind, easier, EBSEA, ERSSA, GDCRNATools, GeneTonic, gg4way, Glimma, GRaNIE, hermes, HTSFilter, HybridExpress, icetea, ideal, INSPEcT, iSEEde, isomiRs, kissDE, magpie, microbiomeExplorer, MIRit, MLSeq, mobileRNA, mosdef, MultiRNAflow, muscat, NBAMSeq, NetActivity, ORFik, OUTRIDER, pcaExplorer, phantasus, POMA, proActiv, RegEnrich, ReportingTools, RiboDiPA, Rmmquant, scBFA, scGPS, SEtools, singleCellTK, SNPhood, systemPipeTools, TBSignatureProfiler, TEKRABber, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, homosapienDEE2CellScore, IHWpaper, ExpHunterSuite, recountWorkflow, bulkAnalyseR, cinaR, ExpGenetic, HeritSeq, limorhyde2, microbial, RCPA, RNAseqQC, sRNAGenetic, TransProR, wilson suggestsMe: aggregateBioVar, apeglm, bambu, BindingSiteFinder, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, compcodeR, dearseq, derfinder, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, EWCE, extraChIPs, fishpond, gage, GeDi, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, OPWeight, pathlinkR, PCAtools, progeny, raer, recount, ribosomeProfilingQC, roastgsa, RUVSeq, Rvisdiff, scran, sparrow, spatialHeatmap, SpliceWiz, subSeq, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, GSE62944, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, seqpac, bakR, cellpypes, conos, FateID, GiANT, glmmSeq, grandR, lfc, LorMe, metaRNASeq, MiscMetabar, pctax, pmartR, RaceID, rliger, SCdeconR, seqgendiff, Seurat, SQMtools, volcano3D dependencyCount: 76 Package: DEsingle Version: 1.27.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 814f7f28c08dc3ef74ca533d4f359bd1 NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: devel git_last_commit: 56ad390 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEsingle_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DEsingle_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DEsingle_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DEsingle_1.27.0.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 39 Package: DESpace Version: 1.7.0 Depends: R (>= 4.3.0) Imports: edgeR, limma, dplyr, stats, Matrix, SpatialExperiment, ggplot2, ggpubr, scales, SummarizedExperiment, S4Vectors, BiocGenerics, data.table, assertthat, cowplot, ggforce, ggnewscale, patchwork, BiocParallel, methods Suggests: knitr, rmarkdown, testthat, BiocStyle, ExperimentHub, concaveman, spatialLIBD, purrr, scuttle, utils License: GPL-3 MD5sum: 9d825aa7eb388e5718d6fd522579486e NeedsCompilation: no Title: DESpace: a framework to discover spatially variable genes Description: Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the best of our knowledge, it is the only SV approach that allows: - performing a SV test on each individual spatial cluster, hence identifying the key regions of the tissue affected by spatial variability; - jointly fitting multiple samples, targeting genes with consistent spatial patterns across replicates. biocViews: Spatial, SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, DifferentialExpression,StatisticalMethod, Visualization Author: Peiying Cai [aut, cre] (ORCID: ), Simone Tiberi [aut] (ORCID: ) Maintainer: Peiying Cai URL: https://github.com/peicai/DESpace VignetteBuilder: knitr BugReports: https://github.com/peicai/DESpace/issues git_url: https://git.bioconductor.org/packages/DESpace git_branch: devel git_last_commit: 2b11443 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DESpace_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DESpace_1.7.0.zip vignettes: vignettes/DESpace/inst/doc/DESpace.html vignetteTitles: A framework to discover spatially variable genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESpace/inst/doc/DESpace.R dependencyCount: 138 Package: destiny Version: 3.21.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, rlang, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse, gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq, org.Mm.eg.db, scran, repr Enhances: rgl, SingleCellExperiment License: GPL-3 MD5sum: a2a7e04846f826461005fca7b86ba56b NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (ORCID: ), Laleh Haghverdi [ctb], Maren Büttner [ctb] (ORCID: ), Fabian Theis [ctb] (ORCID: ), Carsten Marr [ctb] (ORCID: ), Florian Büttner [ctb] (ORCID: ) Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: devel git_last_commit: 339d0ba git_last_commit_date: 2024-11-15 Date/Publication: 2024-11-15 source.ver: src/contrib/destiny_3.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/destiny_3.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/destiny_3.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/destiny_3.21.0.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html, vignettes/destiny/inst/doc/Diffusion-Maps.html, vignettes/destiny/inst/doc/DPT.html, vignettes/destiny/inst/doc/Gene-Relevance.html, vignettes/destiny/inst/doc/Global-Sigma.html, vignettes/destiny/inst/doc/tidyverse.html vignetteTitles: Reproduce the Diffusion Map vignette with the supplied data(), destiny main vignette: Start here!, destiny 2.0 brought the Diffusion Pseudo Time (DPT) class, detecting relevant genes with destiny 3, The effects of a global vs. local kernel, tidyverse and ggplot integration with destiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R, vignettes/destiny/inst/doc/Diffusion-Maps.R, vignettes/destiny/inst/doc/DPT.R, vignettes/destiny/inst/doc/Gene-Relevance.R, vignettes/destiny/inst/doc/Global-Sigma.R, vignettes/destiny/inst/doc/tidyverse.R suggestsMe: CelliD, CellTrails, monocle dependencyCount: 120 Package: DEsubs Version: 1.33.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-3 MD5sum: 7482de722eeb342c9260cc190dacba9b NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: devel git_last_commit: e463abc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEsubs_1.33.0.tar.gz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 116 Package: DEWSeq Version: 1.21.0 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, tidyverse, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: d2871376b909562c5dfbbde475c44038 NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: devel git_last_commit: 93a7801 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DEWSeq_1.21.0.tar.gz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 81 Package: DExMA Version: 1.15.0 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct, RColorBrewer, grDevices Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 MD5sum: f2fba0b54ad74ec858b2416c0e2a9d8a NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results. biocViews: DifferentialExpression, GeneExpression, StatisticalMethod, QualityControl Author: Juan Antonio Villatoro-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: devel git_last_commit: 655549d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DExMA_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DExMA_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DExMA_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DExMA_1.15.0.tgz vignettes: vignettes/DExMA/inst/doc/DExMA.pdf vignetteTitles: Differential Expression Meta-Analysis with DExMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DExMA/inst/doc/DExMA.R dependencyCount: 133 Package: DFP Version: 1.65.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 Archs: x64 MD5sum: 43ae91652af37f4fccf82df9d3083404 NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: devel git_last_commit: c67c641 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DFP_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DFP_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DFP_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DFP_1.65.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 7 Package: DFplyr Version: 1.1.0 Depends: dplyr Imports: BiocGenerics, methods, rlang, S4Vectors, tidyselect Suggests: BiocStyle, GenomeInfoDb, GenomicRanges, IRanges, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), tibble License: GPL-3 MD5sum: 1ef95960b26acdafe17983cf70bbde94 NeedsCompilation: no Title: A `DataFrame` (`S4Vectors`) backend for `dplyr` Description: Provides `dplyr` verbs (`mutate`, `select`, `filter`, etc...) supporting `S4Vectors::DataFrame` objects. Importantly, this is achieved without conversion to an intermediate `tibble`. Adds grouping infrastructure to `DataFrame` which is respected by the transformation verbs. biocViews: DataRepresentation, Infrastructure, Software Author: Jonathan Carroll [aut, cre] (ORCID: ) Maintainer: Jonathan Carroll URL: https://github.com/jonocarroll/DFplyr VignetteBuilder: knitr BugReports: https://github.com/jonocarroll/DFplyr/issues git_url: https://git.bioconductor.org/packages/DFplyr git_branch: devel git_last_commit: c81f630 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DFplyr_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DFplyr_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DFplyr_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DFplyr_1.1.0.tgz vignettes: vignettes/DFplyr/inst/doc/example_usage.html vignetteTitles: Example Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFplyr/inst/doc/example_usage.R dependencyCount: 24 Package: DiffBind Version: 3.17.0 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.13.1), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.99.1), Rcpp Suggests: BiocStyle, testthat, xtable, rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 MD5sum: 1acfa441f9dc0c5f9f9c0edfdc8cebb7 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq, DifferentialPeakCalling, DifferentialMethylation, GeneRegulation, HistoneModification, PeakDetection, BiomedicalInformatics, CellBiology, MultipleComparison, Normalization, ReportWriting, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: devel git_last_commit: 67a8591 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DiffBind_3.17.0.tar.gz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan dependencyCount: 148 Package: diffcoexp Version: 1.27.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery, RUnit License: GPL (>2) MD5sum: cf939b233c0dac583cea3d983c38aff4 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: devel git_last_commit: 8501abb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/diffcoexp_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffcoexp_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffcoexp_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffcoexp_1.27.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R importsMe: ExpHunterSuite, easyDifferentialGeneCoexpression dependencyCount: 130 Package: diffcyt Version: 1.27.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE Archs: x64 MD5sum: eeca3698a9eaf140653033965d034f5b NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: devel git_last_commit: c9fca52 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/diffcyt_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffcyt_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffcyt_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffcyt_1.27.0.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: censcyt, cytofWorkflow importsMe: treeclimbR, treekoR suggestsMe: CATALYST, tidytof dependencyCount: 144 Package: DifferentialRegulation Version: 2.5.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2, tximport, gridExtra LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 36e977958c3e1e23013c3f3b38872253 NeedsCompilation: yes Title: Differentially regulated genes from scRNA-seq data Description: DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget Author: Simone Tiberi [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/DifferentialRegulation SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/DifferentialRegulation/issues git_url: https://git.bioconductor.org/packages/DifferentialRegulation git_branch: devel git_last_commit: f41d98e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DifferentialRegulation_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DifferentialRegulation_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DifferentialRegulation_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DifferentialRegulation_2.5.0.tgz vignettes: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html vignetteTitles: DifferentialRegulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R dependencyCount: 98 Package: diffGeneAnalysis Version: 1.89.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 7d75d52a1424532b6ce561b33bfa0894 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: devel git_last_commit: 0f55259 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/diffGeneAnalysis_1.89.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffGeneAnalysis_1.89.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffGeneAnalysis_1.89.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffGeneAnalysis_1.89.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.39.1 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: c23c38db84cda9ca6cfffc79e47efb21 NeedsCompilation: yes Title: Differential Analysis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun, Gordon Smyth Maintainer: Aaron Lun , Gordon Smyth , Hannah Coughlin SystemRequirements: C++, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: devel git_last_commit: de5cfac git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-16 source.ver: src/contrib/diffHic_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffHic_1.39.1.zip vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: OHCA dependencyCount: 71 Package: DiffLogo Version: 2.31.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: adcf5ba9d4beca94614d75128f3af703 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: devel git_last_commit: 33af0b4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DiffLogo_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DiffLogo_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DiffLogo_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DiffLogo_2.31.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffuStats Version: 1.27.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 MD5sum: f654b32696e6377036c61ecd5a983910 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: devel git_last_commit: 6ad732c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/diffuStats_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diffuStats_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diffuStats_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diffuStats_1.27.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 49 Package: diggit Version: 1.39.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE Archs: x64 MD5sum: d9922171ff24749c3baf83676c272561 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: devel git_last_commit: e13e6e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/diggit_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/diggit_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/diggit_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/diggit_1.39.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 96 Package: Dino Version: 1.13.0 Depends: R (>= 4.0.0) Imports: BiocParallel, BiocSingular, SummarizedExperiment, SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats, parallel, scran, grDevices, stats, methods Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools, ggplot2, gridExtra, ggpubr, grid, magick, hexbin License: GPL-3 MD5sum: b91dcfe0bdea54f9e11a651daa96e00c NeedsCompilation: no Title: Normalization of Single-Cell mRNA Sequencing Data Description: Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge. biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Regression, CellBasedAssays Author: Jared Brown [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Jared Brown URL: https://github.com/JBrownBiostat/Dino VignetteBuilder: knitr BugReports: https://github.com/JBrownBiostat/Dino/issues git_url: https://git.bioconductor.org/packages/Dino git_branch: devel git_last_commit: 8abb017 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Dino_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Dino_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Dino_1.13.0.tgz vignettes: vignettes/Dino/inst/doc/Dino.html vignetteTitles: Normalization by distributional resampling of high throughput single-cell RNA-sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Dino/inst/doc/Dino.R dependencyCount: 192 Package: dinoR Version: 1.3.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, edgeR, GenomicRanges, ggplot2, Matrix, methods, rlang, stats, stringr, tibble, tidyr, tidyselect Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 228edc55576b1c5285b00e67f0413dd8 NeedsCompilation: no Title: Differential NOMe-seq analysis Description: dinoR tests for significant differences in NOMe-seq footprints between two conditions, using genomic regions of interest (ROI) centered around a landmark, for example a transcription factor (TF) motif. This package takes NOMe-seq data (GCH methylation/protection) in the form of a Ranged Summarized Experiment as input. dinoR can be used to group sequencing fragments into 3 or 5 categories representing characteristic footprints (TF bound, nculeosome bound, open chromatin), plot the percentage of fragments in each category in a heatmap, or averaged across different ROI groups, for example, containing a common TF motif. It is designed to compare footprints between two sample groups, using edgeR's quasi-likelihood methods on the total fragment counts per ROI, sample, and footprint category. biocViews: NucleosomePositioning, Epigenetics, MethylSeq, DifferentialMethylation, Coverage, Transcription, Sequencing, Software Author: Michaela Schwaiger [aut, cre] (ORCID: ) Maintainer: Michaela Schwaiger URL: https://github.com/xxxmichixxx/dinoR VignetteBuilder: knitr BugReports: https://github.com/xxxmichixxx/dinoR/issues git_url: https://git.bioconductor.org/packages/dinoR git_branch: devel git_last_commit: eaf277e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dinoR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dinoR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dinoR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dinoR_1.3.0.tgz vignettes: vignettes/dinoR/inst/doc/dinoR-vignette.html vignetteTitles: dinoR-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dinoR/inst/doc/dinoR-vignette.R dependencyCount: 90 Package: dir.expiry Version: 1.15.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: 8de15560fbcd2b1d639c5ee0b836c098 NeedsCompilation: no Title: Managing Expiration for Cache Directories Description: Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. biocViews: Software, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: devel git_last_commit: 42c6808 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dir.expiry_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dir.expiry_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dir.expiry_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dir.expiry_1.15.0.tgz vignettes: vignettes/dir.expiry/inst/doc/userguide.html vignetteTitles: Managing directory expiration hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dir.expiry/inst/doc/userguide.R importsMe: basilisk, basilisk.utils, rebook dependencyCount: 2 Package: Director Version: 1.33.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE Archs: x64 MD5sum: f1efbb93767cc1edac8c08e34c79f087 NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: devel git_last_commit: 5099794 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Director_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Director_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Director_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Director_1.33.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R dependencyCount: 7 Package: DirichletMultinomial Version: 1.49.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, DT, knitr, rmarkdown, BiocStyle License: LGPL-3 MD5sum: 8bc79040f15522671a6e389d2dd9d382 NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan [aut, cre] (ORCID: ) Maintainer: Martin Morgan URL: https://mtmorgan.github.io/DirichletMultinomial/ SystemRequirements: gsl VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/DirichletMultinomial/issues git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: devel git_last_commit: 776fd59 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DirichletMultinomial_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DirichletMultinomial_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DirichletMultinomial_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DirichletMultinomial_1.49.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.html vignetteTitles: DirichletMultinomial for Clustering and Classification of Microbiome Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools suggestsMe: bluster, MicrobiotaProcess dependencyCount: 9 Package: discordant Version: 1.31.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 38398e7f1ec6460034588e43e091245d NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016). biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris [aut, cph, ths] Maintainer: McGrath Max URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: devel git_last_commit: e2c5e77 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/discordant_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/discordant_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/discordant_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/discordant_1.31.0.tgz vignettes: vignettes/discordant/inst/doc/Using_discordant.html vignetteTitles: The discordant R Package: A Novel Approach to Differential Correlation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Using_discordant.R dependencyCount: 30 Package: DiscoRhythm Version: 1.23.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 MD5sum: 347eb7f593ffb6f5c5303992d5c06470 NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: devel git_last_commit: 9e4ec1f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DiscoRhythm_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DiscoRhythm_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DiscoRhythm_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DiscoRhythm_1.23.0.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 159 Package: distinct Version: 1.19.0 Depends: R (>= 4.3) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, UpSetR, BiocStyle License: GPL (>= 3) MD5sum: 63a3b0ed24cc1214e69100dbd71f6189 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: devel git_last_commit: ce83fb1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/distinct_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/distinct_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/distinct_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/distinct_1.19.0.tgz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R importsMe: condiments dependencyCount: 116 Package: dittoSeq Version: 1.19.0 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran License: MIT + file LICENSE MD5sum: 7a7d0203aff164b521fc73203bf6c67b NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: devel git_last_commit: 849ef42 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dittoSeq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dittoSeq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dittoSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dittoSeq_1.19.0.tgz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R importsMe: CRISPRball, SPIAT suggestsMe: demuxSNP, tidySingleCellExperiment, magmaR, scCustomize dependencyCount: 74 Package: divergence Version: 1.23.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: a9d2f36a82c152f847a183ef9e72c1ca NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: devel git_last_commit: ef1c130 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/divergence_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/divergence_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/divergence_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/divergence_1.23.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 37 Package: dks Version: 1.53.0 Depends: R (>= 2.8) Imports: cubature License: GPL Archs: x64 MD5sum: 92a497c5055c5637e5c1d5e515654ba3 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: devel git_last_commit: 7806109 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dks_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dks_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dks_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dks_1.53.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.21.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: e79afca1aa5471d1baadcb7bb1f6d4d2 NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] (ORCID: ) Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: devel git_last_commit: 931dcfd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DMCFB_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMCFB_1.21.0.zip vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 97 Package: DMCHMM Version: 1.29.0 Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: bae753a2f5f3cafc1e4c36e655ddba86 NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: devel git_last_commit: d270747 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DMCHMM_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMCHMM_1.29.0.zip vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden Markov Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 69 Package: DMRcaller Version: 1.39.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10) Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 819431e473505cab31a0f8f4e70629af NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: devel git_last_commit: 13a82ad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DMRcaller_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMRcaller_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRcaller_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMRcaller_1.39.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 38 Package: DMRcate Version: 3.3.1 Depends: R (>= 4.3.0) Imports: AnnotationHub, ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment, biomaRt, grDevices Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata, EPICv2manifest License: file LICENSE MD5sum: 0a19832d3f5a7d5a07cbe6a2e2f8cc77 NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: devel git_last_commit: 52f38aa git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-18 source.ver: src/contrib/DMRcate_3.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/DMRcate_3.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRcate_3.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DMRcate_3.3.1.tgz vignettes: vignettes/DMRcate/inst/doc/EPICv1_and_450K.pdf, vignettes/DMRcate/inst/doc/EPICv2.pdf, vignettes/DMRcate/inst/doc/sequencing.pdf vignetteTitles: DMRcate for EPICv1 and 450K assays, DMR calling from EPICv2 arrays, DMRcate for bisulfite sequencing assays (WGBS and RRBS) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/EPICv1_and_450K.R, vignettes/DMRcate/inst/doc/EPICv2.R, vignettes/DMRcate/inst/doc/sequencing.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 224 Package: dmrseq Version: 1.27.0 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 8b6eff5f6dce9ecc2873c58d938eed38 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: devel git_last_commit: ea859e7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dmrseq_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dmrseq_1.27.0.zip vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 149 Package: DNABarcodeCompatibility Version: 1.23.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, stats, utils, methods, Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 20e9daec0e78088b5d751c8dbb81912f NeedsCompilation: yes Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (ORCID: ), Jacques Boutet de Monvel [aut] (ORCID: ), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] (ORCID: ) Maintainer: Céline Trébeau URL: https://dnabarcodecompatibility.pasteur.fr/ VignetteBuilder: knitr BugReports: https://gitlab.pasteur.fr/ida-public/dnabarcodecompatibility/-/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: devel git_last_commit: 2a16130 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DNABarcodeCompatibility_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNABarcodeCompatibility_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodeCompatibility_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodeCompatibility_1.23.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 30 Package: DNABarcodes Version: 1.37.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: cc7e4318d686a7875a8a28486082e4e0 NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: devel git_last_commit: 0b621bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DNABarcodes_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNABarcodes_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodes_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodes_1.37.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R dependencyCount: 11 Package: DNAcopy Version: 1.81.0 License: GPL (>= 2) MD5sum: fc6cac7f73017c8148f7d12f64f76aaf NeedsCompilation: yes Title: DNA Copy Number Data Analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: devel git_last_commit: 8c04c59 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DNAcopy_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAcopy_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAcopy_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAcopy_1.81.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2, conumee, GWASTools, maftools, MDTS, MEDIPS, MinimumDistance, QDNAseq, Repitools, SCOPE, jointseg, PSCBS suggestsMe: cn.mops, CopyNumberPlots, fastseg, nullranges, sesame, ACNE, aroma.cn, aroma.core, calmate dependencyCount: 0 Package: DNAfusion Version: 1.9.0 Depends: R (>= 4.2.0) Imports: bamsignals, GenomicRanges, IRanges, Rsamtools, GenomicAlignments, BiocBaseUtils, S4Vectors, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocGenerics Suggests: knitr, rmarkdown, testthat, sessioninfo, BiocStyle License: GPL-3 MD5sum: 3e97235bbc3b7d7c9fbc50f7b970789f NeedsCompilation: no Title: Identification of gene fusions using paired-end sequencing Description: DNAfusion can identify gene fusions such as EML4-ALK based on paired-end sequencing results. This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes. biocViews: TargetedResequencing, Genetics, GeneFusionDetection, Sequencing Author: Christoffer Trier Maansson [aut, cre] (ORCID: ), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd] Maintainer: Christoffer Trier Maansson URL: https://github.com/CTrierMaansson/DNAfusion VignetteBuilder: knitr BugReports: https://github.com/CTrierMaansson/DNAfusion/issues git_url: https://git.bioconductor.org/packages/DNAfusion git_branch: devel git_last_commit: b215285 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DNAfusion_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAfusion_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAfusion_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAfusion_1.9.0.tgz vignettes: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html vignetteTitles: Introduction to DNAfusion hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R dependencyCount: 82 Package: DNAshapeR Version: 1.35.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 MD5sum: b738a616d0475594a94b8959702b98ed NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: devel git_last_commit: 090780d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DNAshapeR_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DNAshapeR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DNAshapeR_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DNAshapeR_1.35.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 34 Package: DominoEffect Version: 1.27.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, pwalign, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 215d286429324a0ef85f364e17ba890a NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: devel git_last_commit: deeeeac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DominoEffect_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DominoEffect_1.27.0.zip vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 105 Package: dominoSignal Version: 1.1.0 Depends: R(>= 4.2.0), Imports: biomaRt, ComplexHeatmap, circlize, ggpubr, grDevices, grid, igraph, Matrix, methods, plyr, stats, utils Suggests: knitr, patchwork, rmarkdown, Seurat, testthat, formatR, BiocFileCache, SingleCellExperiment License: GPL-3 | file LICENSE Archs: x64 MD5sum: 7e87bc7f76a17696f7dd4f0ba471de9e NeedsCompilation: no Title: Cell Communication Analysis for Single Cell RNA Sequencing Description: dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis. biocViews: SystemsBiology, SingleCell, Transcriptomics, Network Author: Christopher Cherry [aut] (ORCID: ), Jacob T Mitchell [aut, cre] (ORCID: ), Sushma Nagaraj [aut] (ORCID: ), Kavita Krishnan [aut] (ORCID: ), Dmitrijs Lvovs [aut], Elana Fertig [ctb] (ORCID: ), Jennifer Elisseeff [ctb] (ORCID: ) Maintainer: Jacob T Mitchell URL: https://fertiglab.github.io/dominoSignal/ VignetteBuilder: knitr BugReports: https://github.com/FertigLab/dominoSignal/issues git_url: https://git.bioconductor.org/packages/dominoSignal git_branch: devel git_last_commit: 865183f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dominoSignal_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dominoSignal_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dominoSignal_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dominoSignal_1.1.0.tgz vignettes: vignettes/dominoSignal/inst/doc/domino_object_vignette.html, vignettes/dominoSignal/inst/doc/dominoSignal.html, vignettes/dominoSignal/inst/doc/plotting_vignette.html vignetteTitles: Interacting with domino Objects, Get Started with dominoSignal, Plotting Functions and Options hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dominoSignal/inst/doc/domino_object_vignette.R, vignettes/dominoSignal/inst/doc/dominoSignal.R, vignettes/dominoSignal/inst/doc/plotting_vignette.R dependencyCount: 134 Package: doppelgangR Version: 1.35.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) Archs: x64 MD5sum: 45b4f71a9c59d512e32d61984907348f NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: devel git_last_commit: 0cd0783 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/doppelgangR_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doppelgangR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/doppelgangR_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/doppelgangR_1.35.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 82 Package: Doscheda Version: 1.29.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 071c4ec8871c7fe965fa78607f4525c4 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: devel git_last_commit: 305a40a git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/Doscheda_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Doscheda_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Doscheda_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Doscheda_1.29.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 152 Package: DOSE Version: 4.1.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocParallel, fgsea, ggplot2, GOSemSim (>= 2.31.2), methods, qvalue, reshape2, stats, utils, yulab.utils (>= 0.1.6) Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, memoise, org.Hs.eg.db, rmarkdown, testthat License: Artistic-2.0 MD5sum: 9141c32d267c2d1e45095490348bd546 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: devel git_last_commit: 898262a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DOSE_4.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DOSE_4.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DOSE_4.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DOSE_4.1.0.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, clusterProfiler, debrowser, enrichplot, enrichViewNet, GDCRNATools, meshes, miRSM, miRspongeR, Moonlight2R, MoonlightR, Pigengene, ReactomePA, RegEnrich, scTensor, signatureSearch, SVMDO, TDbasedUFEadv, ExpHunterSuite, GseaVis, immcp suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS, aPEAR dependencyCount: 96 Package: doseR Version: 1.23.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: x64 MD5sum: c2109bc5e0b7e49fc9ca57d93d9f7596 NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: devel git_last_commit: 44de376 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/doseR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doseR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/doseR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/doseR_1.23.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 54 Package: doubletrouble Version: 1.7.0 Depends: R (>= 4.2.0) Imports: syntenet, GenomicRanges, Biostrings, mclust, MSA2dist (>= 1.1.5), ggplot2, rlang, stats, utils, AnnotationDbi, GenomicFeatures Suggests: txdbmaker, testthat (>= 3.0.0), knitr, feature, patchwork, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: f6ef3c50e3b971f05257f2f66336ce51 NeedsCompilation: no Title: Identification and classification of duplicated genes Description: doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks. biocViews: Software, WholeGenome, ComparativeGenomics, FunctionalGenomics, Phylogenetics, Network, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/doubletrouble VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/doubletrouble git_url: https://git.bioconductor.org/packages/doubletrouble git_branch: devel git_last_commit: 4db21ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/doubletrouble_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/doubletrouble_1.7.0.zip vignettes: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.html vignetteTitles: Identification and classification of duplicated genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.R dependencyCount: 144 Package: drawProteins Version: 1.27.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 935d75853df050f5a744bad0dd7d516e NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: devel git_last_commit: fc1cfc1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/drawProteins_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/drawProteins_1.27.0.zip vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R importsMe: factR dependencyCount: 61 Package: dreamlet Version: 1.5.0 Depends: R (>= 4.3.0), variancePartition (>= 1.33.11), SingleCellExperiment, ggplot2 Imports: edgeR, SummarizedExperiment, DelayedMatrixStats, sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr, GSEABase, data.table, zenith (>= 1.1.2), mashr (>= 0.2.52), ashr, dplyr, BiocParallel, ggbeeswarm, S4Vectors, IRanges, irlba, limma, metafor, remaCor, broom, tidyr, rlang, BiocGenerics, S4Arrays, SparseArray, DelayedArray, gtools, reshape2, ggrepel, scattermore, Rcpp, lme4 (>= 1.1-33), MASS, Rdpack, utils, stats LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, muscData, scater, scuttle License: Artistic-2.0 MD5sum: 64e0536c54b223d0606cdb3cb89fc04f NeedsCompilation: yes Title: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs Description: Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, Regression, GeneSetEnrichment, GeneRegulation, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, SingleCell, Preprocessing, Sequencing, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeurogenomics.github.io/dreamlet SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeurogenomics/dreamlet/issues git_url: https://git.bioconductor.org/packages/dreamlet git_branch: devel git_last_commit: dd6cd83 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-12 source.ver: src/contrib/dreamlet_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dreamlet_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dreamlet_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dreamlet_1.5.0.tgz vignettes: vignettes/dreamlet/inst/doc/cell_covs.html, vignettes/dreamlet/inst/doc/dreamlet.html, vignettes/dreamlet/inst/doc/errors.html, vignettes/dreamlet/inst/doc/h5ad_on_disk.html, vignettes/dreamlet/inst/doc/mashr.html, vignettes/dreamlet/inst/doc/non_lin_eff.html vignetteTitles: Modeling continuous cell-level covariates, Dreamlet analysis of single cell RNA-seq, Error handling, Loading large-scale H5AD datasets, mashr analysis following dreamlet, Testing non-linear effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dreamlet/inst/doc/cell_covs.R, vignettes/dreamlet/inst/doc/dreamlet.R, vignettes/dreamlet/inst/doc/errors.R, vignettes/dreamlet/inst/doc/h5ad_on_disk.R, vignettes/dreamlet/inst/doc/non_lin_eff.R dependencyCount: 191 Package: DRIMSeq Version: 1.35.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: 8a625cf608e725315651690307d2e9e0 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: devel git_last_commit: bffc717 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DRIMSeq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DRIMSeq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DRIMSeq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DRIMSeq_1.35.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS dependencyCount: 73 Package: DriverNet Version: 1.47.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 5de66b21ba86c160a6fd6769b7aa0bcd NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: devel git_last_commit: a5b4009 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DriverNet_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DriverNet_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DriverNet_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DriverNet_1.47.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.27.2 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, SparseArray (>= 1.5.18), DelayedArray (>= 0.31.9), DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 MD5sum: 0d0f51d02dd01250c0ef55f9828f33d8 NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy [ctb], Dongze He [ctb], Rob Patro [ctb] Maintainer: Jonathan Griffiths SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: devel git_last_commit: 5aea486 git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-18 source.ver: src/contrib/DropletUtils_1.27.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/DropletUtils_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DropletUtils_1.27.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DropletUtils_1.27.2.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R dependsOnMe: OSCA.intro, OSCA.workflows importsMe: FLAMES, scCB2, scPipe, singleCellTK, Spaniel, SpatialFeatureExperiment, visiumStitched suggestsMe: alabaster.spatial, demuxmix, mumosa, Nebulosa, SingleCellAlleleExperiment, SpatialExperiment, SPOTlight, tidySpatialExperiment, DropletTestFiles, MerfishData, muscData, spatialLIBD, scCustomize, SoupX dependencyCount: 67 Package: drugTargetInteractions Version: 1.15.0 Depends: methods, R (>= 4.1) Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb, BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT, EnsDb.Hsapiens.v86 License: Artistic-2.0 MD5sum: 5d8be70766cd4f6bb9bb405e08d0bc00 NeedsCompilation: no Title: Drug-Target Interactions Description: Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, Proteomics, Metabolomics Author: Thomas Girke [cre, aut] Maintainer: Thomas Girke URL: https://github.com/girke-lab/drugTargetInteractions VignetteBuilder: knitr BugReports: https://github.com/girke-lab/drugTargetInteractions git_url: https://git.bioconductor.org/packages/drugTargetInteractions git_branch: devel git_last_commit: 262cba9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/drugTargetInteractions_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/drugTargetInteractions_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/drugTargetInteractions_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/drugTargetInteractions_1.15.0.tgz vignettes: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html vignetteTitles: Drug-Target Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R dependencyCount: 109 Package: DrugVsDisease Version: 2.49.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: bbe8b86958c3e739fccedd85bd652d7a NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: devel git_last_commit: a631f4f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DrugVsDisease_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DrugVsDisease_2.49.0.zip vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 134 Package: DSS Version: 2.55.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines Suggests: BiocStyle, knitr, rmarkdown, edgeR License: GPL Archs: x64 MD5sum: 672b711143db263aef806d6733ba75ff NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: devel git_last_commit: 23fba50 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DSS_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DSS_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DSS_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DSS_2.55.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R dependsOnMe: DeMixT importsMe: borealis, kissDE, metaseqR2, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 91 Package: dStruct Version: 1.13.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, tidyverse, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 8890f0c7e8384e1e5ee6a78f17b6d491 NeedsCompilation: no Title: Identifying differentially reactive regions from RNA structurome profiling data Description: dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method. biocViews: StatisticalMethod, StructuralPrediction, Sequencing, Software Author: Krishna Choudhary [aut, cre] (ORCID: ), Sharon Aviran [aut] (ORCID: ) Maintainer: Krishna Choudhary URL: https://github.com/dataMaster-Kris/dStruct VignetteBuilder: knitr BugReports: https://github.com/dataMaster-Kris/dStruct/issues git_url: https://git.bioconductor.org/packages/dStruct git_branch: devel git_last_commit: 32829a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dStruct_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dStruct_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dStruct_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dStruct_1.13.0.tgz vignettes: vignettes/dStruct/inst/doc/dStruct.html vignetteTitles: Differential RNA structurome analysis using `dStruct` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dStruct/inst/doc/dStruct.R dependencyCount: 49 Package: DTA Version: 2.53.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 1582cef58372e425bb8732a036e7a966 NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: devel git_last_commit: 3ccd65f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DTA_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DTA_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DTA_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DTA_2.53.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R importsMe: rifiComparative dependencyCount: 5 Package: Dune Version: 1.19.0 Depends: R (>= 3.6) Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: 469581728c1a9070af78518ebca71180 NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: devel git_last_commit: 4205bcf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Dune_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Dune_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Dune_1.19.0.tgz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 98 Package: DuplexDiscovereR Version: 1.1.0 Depends: R (>= 4.4), InteractionSet Imports: Gviz, Biostrings, rtracklayer, GenomicAlignments, GenomicRanges, ggsci, igraph, rlang, scales, stringr, dplyr, tibble, tidyr, purrr, methods, grDevices, stats, utils Suggests: knitr, UpSetR, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: c9ff3e6dbec14fa10f50447c54ca095d NeedsCompilation: no Title: Analysis of the data from RNA duplex probing experiments Description: DuplexDiscovereR is a package designed for analyzing data from RNA cross-linking and proximity ligation protocols such as SPLASH, PARIS, LIGR-seq, and others. DuplexDiscovereR accepts input in the form of chimerically or split-aligned reads. It includes procedures for alignment classification, filtering, and efficient clustering of individual chimeric reads into duplex groups (DGs). Once DGs are identified, the package predicts RNA duplex formation and their hybridization energies. Additional metrics, such as p-values for random ligation hypothesis or mean DG alignment scores, can be calculated to rank final set of RNA duplexes. Data from multiple experiments or replicates can be processed separately and further compared to check the reproducibility of the experimental method. biocViews: Sequencing, Transcriptomics, StructuralPrediction, Clustering, SplicedAlignment Author: Egor Semenchenko [aut, cre, cph] (ORCID: ), Volodymyr Tsybulskyi [ctb] (ORCID: ), Irmtraud M. Meyer [aut, cph] (ORCID: ) Maintainer: Egor Semenchenko URL: https://github.com/Egors01/DuplexDiscovereR/ VignetteBuilder: knitr BugReports: https://github.com/Egors01/DuplexDiscovereR/issues/ git_url: https://git.bioconductor.org/packages/DuplexDiscovereR git_branch: devel git_last_commit: 37477e1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DuplexDiscovereR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DuplexDiscovereR_1.1.0.zip vignettes: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.html vignetteTitles: DuplexDiscovereR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.R dependencyCount: 160 Package: dupRadar Version: 1.37.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: c90e148780dae1051ad7153e868bef3d NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein URL: https://www.bioconductor.org/packages/dupRadar, https://ssayols.github.io/dupRadar/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: devel git_last_commit: 9afe62b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dupRadar_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dupRadar_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dupRadar_1.37.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 10 Package: dyebias Version: 1.67.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 933ca5288ff17d17d76952f9da9cda9d NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: devel git_last_commit: c649c70 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/dyebias_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/dyebias_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dyebias_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/dyebias_1.67.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 11 Package: DynDoc Version: 1.85.0 Depends: methods, utils Imports: methods License: Artistic-2.0 Archs: x64 MD5sum: b802c3d469d9f5fd94ad4073a8dd1b98 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: devel git_last_commit: 9092b54 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/DynDoc_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/DynDoc_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DynDoc_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/DynDoc_1.85.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easier Version: 1.13.0 Depends: R (>= 4.2.0) Imports: progeny, easierData, dorothea (>= 1.6.0), decoupleR, quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr, DESeq2, utils, dplyr, tidyr, tibble, matrixStats, rlang, BiocParallel, reshape2, rstatix, ggrepel, magrittr, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment, viper License: MIT + file LICENSE MD5sum: 21842e3166ea7bbdfbc679475b71edbb NeedsCompilation: no Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Arsenij Ustjanzew [aut] (ORCID: ), Francesca Finotello [aut] (ORCID: ), Federica Eduati [aut] (ORCID: ) Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easier git_branch: devel git_last_commit: 060a509 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/easier_1.13.0.tar.gz vignettes: vignettes/easier/inst/doc/easier_user_manual.html vignetteTitles: easier User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easier/inst/doc/easier_user_manual.R dependencyCount: 162 Package: EasyCellType Version: 1.9.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, ggplot2, magrittr, rlang, stats, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, vctrs (>= 0.6.4), BiocStyle Suggests: knitr, rmarkdown, testthat (>= 3.0.0), Seurat, BiocManager, devtools, BiocStyle License: Artistic-2.0 MD5sum: 18fb6a1f24cdb6fd5cf2b95bbef18f8b NeedsCompilation: no Title: Annotate cell types for scRNA-seq data Description: We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster. biocViews: SingleCell, Software, GeneExpression, GeneSetEnrichment Author: Ruoxing Li [aut, cre, ctb], Ziyi Li [ctb] Maintainer: Ruoxing Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EasyCellType git_branch: devel git_last_commit: 902a85b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EasyCellType_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EasyCellType_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EasyCellType_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EasyCellType_1.9.0.tgz vignettes: vignettes/EasyCellType/inst/doc/my-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyCellType/inst/doc/my-vignette.R dependencyCount: 144 Package: easylift Version: 1.5.0 Depends: GenomicRanges, BiocFileCache Imports: rtracklayer, GenomeInfoDb, R.utils, tools, methods Suggests: testthat (>= 3.0.0), IRanges, knitr, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: af65b22b32498b63c0a920611bff70ba NeedsCompilation: no Title: An R package to perform genomic liftover Description: The easylift package provides a convenient tool for genomic liftover operations between different genome assemblies. It seamlessly works with Bioconductor's GRanges objects and chain files from the UCSC Genome Browser, allowing for straightforward handling of genomic ranges across various genome versions. One noteworthy feature of easylift is its integration with the BiocFileCache package. This integration automates the management and caching of chain files necessary for liftover operations. Users no longer need to manually specify chain file paths in their function calls, reducing the complexity of the liftover process. biocViews: Software, WorkflowStep, Sequencing, Coverage, GenomeAssembly, DataImport Author: Abdullah Al Nahid [aut, cre] (ORCID: ), Hervé Pagès [aut, rev], Michael Love [aut, rev] (ORCID: ) Maintainer: Abdullah Al Nahid URL: https://github.com/nahid18/easylift, https://nahid18.github.io/easylift VignetteBuilder: knitr BugReports: https://github.com/nahid18/easylift/issues git_url: https://git.bioconductor.org/packages/easylift git_branch: devel git_last_commit: 16c9c5b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/easylift_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easylift_1.5.0.zip vignettes: vignettes/easylift/inst/doc/easylift.html vignetteTitles: Perform Genomic Liftover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easylift/inst/doc/easylift.R dependencyCount: 92 Package: easyreporting Version: 1.19.0 Depends: R (>= 3.5.0) Imports: rmarkdown, methods, tools, shiny, rlang Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: 3cee64fa0654e5d7e6a1cd6ff1bc5717 NeedsCompilation: no Title: Helps creating report for improving Reproducible Computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: devel git_last_commit: 0f50bcf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/easyreporting_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easyreporting_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easyreporting_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easyreporting_1.19.0.tgz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 43 Package: easyRNASeq Version: 2.43.0 Imports: Biobase (>= 2.64.0), BiocFileCache (>= 2.12.0), BiocGenerics (>= 0.50.0), BiocParallel (>= 1.38.0), biomaRt (>= 2.60.1), Biostrings (>= 2.72.1), edgeR (>= 4.2.1), GenomeInfoDb (>= 1.40.1), genomeIntervals (>= 1.60.0), GenomicAlignments (>= 1.40.0), GenomicRanges (>= 1.56.1), SummarizedExperiment (>= 1.34.0), graphics, IRanges (>= 2.38.1), LSD (>= 4.1-0), methods, parallel, rappdirs (>= 0.3.3), Rsamtools (>= 2.20.0), S4Vectors (>= 0.42.1), ShortRead (>= 1.62.0), utils Suggests: BiocStyle (>= 2.32.1), BSgenome (>= 1.72.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.33) License: Artistic-2.0 MD5sum: ae7b560cb851f0aaa2434bed507c2003 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: devel git_last_commit: d32c68e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/easyRNASeq_2.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/easyRNASeq_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/easyRNASeq_2.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/easyRNASeq_2.43.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 111 Package: EBarrays Version: 2.71.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) MD5sum: 844beddb438532c29a8da6bc00c53498 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: devel git_last_commit: 3c3b71c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EBarrays_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBarrays_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBarrays_2.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBarrays_2.71.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 11 Package: EBcoexpress Version: 1.51.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) MD5sum: b40535a5279edc92df3b541d69da44b5 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: devel git_last_commit: 7d83d90 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EBcoexpress_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBcoexpress_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBcoexpress_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBcoexpress_1.51.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R suggestsMe: dcanr dependencyCount: 15 Package: EBImage Version: 4.49.0 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL MD5sum: 2fb13cb9b0acedabee242aae27ebfde6 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: devel git_last_commit: ad8004f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EBImage_4.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBImage_4.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBImage_4.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBImage_4.49.0.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, cytomapper, flowcatchR, DonaPLLP2013, furrowSeg, MerfishData, GiNA, nucim importsMe: bnbc, Cardinal, CatsCradle, cytoviewer, flowCHIC, heatmaps, imcRtools, MoleculeExperiment, RBioFormats, simpleSeg, SpatialFeatureExperiment, SpatialOmicsOverlay, synapsis, xenLite, yamss, BioImageDbs, bioimagetools, GoogleImage2Array, LFApp, LOMAR, RockFab, SAFARI suggestsMe: HilbertVis, Voyager, spicyWorkflow, aroma.core, cooltools, ExpImage, glow, ijtiff, juicr, lidR, metagear, pliman, rcaiman dependencyCount: 45 Package: EBSEA Version: 1.35.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: bbd1e1144c92bdb4472877b4dc09e5b8 NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: devel git_last_commit: be7ea14 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EBSEA_1.35.0.tar.gz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 78 Package: EBSeq Version: 2.5.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0) LinkingTo: Rcpp,RcppEigen,BH License: Artistic-2.0 MD5sum: 44b18bb410e3689daf341f86e0071ebf NeedsCompilation: yes Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Xiuyu Ma [cre, aut], Ning Leng [aut], Christina Kendziorski [ctb], Michael A. Newton [ctb] Maintainer: Xiuyu Ma SystemRequirements: c++11 git_url: https://git.bioconductor.org/packages/EBSeq git_branch: devel git_last_commit: 4664faa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EBSeq_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EBSeq_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EBSeq_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EBSeq_2.5.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: Oscope importsMe: BatchQC, broadSeq, DEsubs, scDD suggestsMe: compcodeR dependencyCount: 43 Package: ecolitk Version: 1.79.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 7f2b281de463d6f765a678230b0de291 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: devel git_last_commit: 926c981 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ecolitk_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ecolitk_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ecolitk_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ecolitk_1.79.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 7 Package: EDASeq Version: 2.41.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2, rmarkdown License: Artistic-2.0 MD5sum: eddb6c6de525dcb3c56a13b682eea9a6 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: devel git_last_commit: 68d2eba git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EDASeq_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EDASeq_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EDASeq_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EDASeq_2.41.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: RUVSeq importsMe: consensusDE, DaMiRseq, metaseqR2, octad, ribosomeProfilingQC suggestsMe: awst, DEScan2, easyreporting, GRaNIE, HTSFilter, MOSClip, TCGAbiolinks dependencyCount: 117 Package: edge Version: 2.39.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: x64 MD5sum: dbed0bf91790338911dc048ce00641ca NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: devel git_last_commit: b391071 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/edge_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/edge_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/edge_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/edge_2.39.0.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 96 Package: edgeR Version: 4.5.1 Depends: R (>= 3.6.0), limma (>= 3.61.9) Imports: methods, graphics, stats, utils, locfit Suggests: jsonlite, readr, rhdf5, splines, knitr, AnnotationDbi, Biobase, BiocStyle, SummarizedExperiment, org.Hs.eg.db, Matrix, SeuratObject License: GPL (>=2) Archs: x64 MD5sum: 50e4e31f8cf302dea6fc1e10067a2c97 NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics, SingleCell Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Lizhong Chen, Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: https://bioinf.wehi.edu.au/edgeR/, https://bioconductor.org/packages/edgeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/edgeR git_branch: devel git_last_commit: 380f86e git_last_commit_date: 2024-11-30 Date/Publication: 2024-12-01 source.ver: src/contrib/edgeR_4.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/edgeR_4.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/edgeR_4.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/edgeR_4.5.1.tgz vignettes: vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf, vignettes/edgeR/inst/doc/intro.html vignetteTitles: edgeR User's Guide, A brief introduction to edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/edgeR/inst/doc/intro.R dependsOnMe: ASpli, methylMnM, miloR, octad, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.workflows, babel, BALLI, BioInsight, SCdeconR importsMe: affycoretools, ATACseqQC, autonomics, AWFisher, baySeq, beer, BioQC, broadSeq, censcyt, ChromSCape, circRNAprofiler, CleanUpRNAseq, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, csaw, cypress, DaMiRseq, Damsel, dce, debrowser, DEFormats, DEGreport, DESpace, DEsubs, diffcyt, diffHic, dinoR, DMRcate, doseR, dreamlet, DRIMSeq, DropletUtils, easyRNASeq, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs, GDCRNATools, GenomicPlot, GEOexplorer, gg4way, gINTomics, Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv, iSEEde, KnowSeq, Maaslin2, mastR, MEB, MEDIPS, metaseqR2, MIRit, MLSeq, moanin, mobileRNA, MOSim, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, phantasus, PhIPData, ppcseq, PRONE, PROPER, psichomics, regsplice, Repitools, RNAseqCovarImpute, ROSeq, Rvisdiff, scCB2, scde, scone, scran, ScreenR, SEtools, SIMD, SingleCellSignalR, singscore, SpaNorm, sparrow, spatialHeatmap, speckle, splatter, sSNAPPY, standR, STATegRa, sva, TBSignatureProfiler, TCseq, tradeSeq, treeclimbR, treekoR, tweeDEseq, vidger, xcore, yarn, zinbwave, emtdata, spatialLIBD, ExpHunterSuite, recountWorkflow, SingscoreAMLMutations, aIc, bulkAnalyseR, CAMML, cinaR, CoreMicrobiomeR, HTSCluster, microbial, RCPA, RVA, scITD, SCRIP, scRNAtools, SPUTNIK, ssizeRNA, TransProR, TSGS suggestsMe: ABSSeq, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq, DEScan2, DiffBind, dittoSeq, DSS, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, goseq, GSAR, GSVA, ideal, iSEEpathways, iSEEu, lemur, missMethyl, MoonlightR, multiMiR, raer, recount, ribosomeProfilingQC, satuRn, scider, SeqGate, SpliceWiz, stageR, subSeq, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zenith, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, easybio, glmmSeq, MiscMetabar, palasso, pctax, pmartR, seqgendiff, SIBERG, volcano3D dependencyCount: 10 Package: EDIRquery Version: 1.7.0 Depends: R (>= 4.2.0) Imports: tibble (>= 3.1.6), tictoc (>= 1.0.1), utils (>= 4.1.3), stats (>= 4.1.3), readr (>= 2.1.2), InteractionSet (>= 1.22.0), GenomicRanges (>= 1.46.1) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: aca8b76dd94c493cf6639e75c1827145 NeedsCompilation: no Title: Query the EDIR Database For Specific Gene Description: EDIRquery provides a tool to search for genes of interest within the Exome Database of Interspersed Repeats (EDIR). A gene name is a required input, and users can additionally specify repeat sequence lengths, minimum and maximum distance between sequences, and whether to allow a 1-bp mismatch. Outputs include a summary of results by repeat length, as well as a dataframe of query results. Example data provided includes a subset of the data for the gene GAA (ENSG00000171298). To query the full database requires providing a path to the downloaded database files as a parameter. biocViews: Genetics, SequenceMatching Author: Laura D.T. Vo Ngoc [aut, cre] (ORCID: ) Maintainer: Laura D.T. Vo Ngoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EDIRquery git_branch: devel git_last_commit: 6892942 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EDIRquery_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EDIRquery_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EDIRquery_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EDIRquery_1.7.0.tgz vignettes: vignettes/EDIRquery/inst/doc/EDIRquery.pdf vignetteTitles: EDIRquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDIRquery/inst/doc/EDIRquery.R dependencyCount: 63 Package: eds Version: 1.9.0 Depends: Matrix Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, tximportData, testthat (>= 3.0.0) License: GPL-2 Archs: x64 MD5sum: a222a8466fd5c23685395d8a47b63f03 NeedsCompilation: yes Title: eds: Low-level reader for Alevin EDS format Description: This packages provides a single function, readEDS. This is a low-level utility for reading in Alevin EDS format into R. This function is not designed for end-users but instead the package is predominantly for simplifying package dependency graph for other Bioconductor packages. biocViews: Sequencing, RNASeq, GeneExpression, SingleCell Author: Avi Srivastava [aut, cre], Michael Love [aut, ctb] Maintainer: Avi Srivastava URL: https://github.com/mikelove/eds SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eds git_branch: devel git_last_commit: 9cf419e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/eds_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eds_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eds_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eds_1.9.0.tgz vignettes: vignettes/eds/inst/doc/eds.html vignetteTitles: eds: Low-level reader function for Alevin EDS format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eds/inst/doc/eds.R importsMe: singleCellTK suggestsMe: tximport dependencyCount: 9 Package: EGAD Version: 1.35.0 Depends: R(>= 3.5) Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat, rmarkdown, markdown License: GPL-2 MD5sum: 7364c6d2370e1ec73ee96a12f4898d12 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: devel git_last_commit: 65e168f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EGAD_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EGAD_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EGAD_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EGAD_1.35.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 92 Package: EGSEA Version: 1.35.0 Depends: R (>= 4.3.0), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 652146426f4da9f7a18ac171137e1518 NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh [aut, cre], Luyi Tian [aut], Milica Ng [aut], Matthew Ritchie [ctb] Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: devel git_last_commit: bc1e988 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EGSEA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EGSEA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EGSEA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EGSEA_1.35.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependsOnMe: EGSEA123 suggestsMe: tidybulk, EGSEAdata dependencyCount: 200 Package: eiR Version: 1.47.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown,RSQLite License: Artistic-2.0 Archs: x64 MD5sum: 2a8dd46b295784556bd3a13e2e23ff8b NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: devel git_last_commit: e9e5231 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/eiR_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eiR_1.47.0.zip vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR: Accelerated Similarity Searching of Small Molecules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 81 Package: eisaR Version: 1.19.0 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR (>= 4.0), methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, txdbmaker, rtracklayer License: GPL-3 Archs: x64 MD5sum: cee013ad7592666590e492b498defb7b NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: devel git_last_commit: d8c11e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/eisaR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eisaR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eisaR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eisaR_1.19.0.tgz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 41 Package: ELMER Version: 2.31.0 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer (>= 1.61.2), DelayedArray Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: eac9ad812c49757fa74ccb07d72ff339 NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: devel git_last_commit: 1f88893 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ELMER_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ELMER_2.31.0.zip vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAWorkflow dependencyCount: 213 Package: EMDomics Version: 2.37.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE Archs: x64 MD5sum: 45681860bf8d9c9364cb4ba03999d0b8 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: devel git_last_commit: 593aa0c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EMDomics_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EMDomics_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EMDomics_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EMDomics_2.37.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 49 Package: EmpiricalBrownsMethod Version: 1.35.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 2ac0d9bd7a5d53286c2d235669a255b5 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: devel git_last_commit: 73676a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EmpiricalBrownsMethod_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EmpiricalBrownsMethod_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EmpiricalBrownsMethod_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EmpiricalBrownsMethod_1.35.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA dependencyCount: 0 Package: EnhancedVolcano Version: 1.25.0 Depends: ggplot2, ggrepel Imports: methods Suggests: ggalt, ggrastr, RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: be827e677511fbbd2aeb29bf3110daef NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles Lewis [aut] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: devel git_last_commit: 88a8e6a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EnhancedVolcano_1.25.0.tar.gz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R importsMe: ExpHunterSuite suggestsMe: rliger dependencyCount: 37 Package: enhancerHomologSearch Version: 1.13.1 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, pwalign, rtracklayer, Rcpp, S4Vectors, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb, testthat, TFBSTools License: GPL (>= 2) MD5sum: 21b31a0dcf40e99914ed18c5de01395d NeedsCompilation: yes Title: Identification of putative mammalian orthologs to given enhancer Description: Get ENCODE data of enhancer region via H3K4me1 peaks and search homolog regions for given sequences. The candidates of enhancer homolog regions can be filtered by distance to target TSS. The top candidates from human and mouse will be aligned to each other and then exported as multiple alignments with given enhancer. biocViews: Sequencing, GeneRegulation, Alignment Author: Jianhong Ou [aut, cre] (ORCID: ), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou URL: https://jianhong.github.io/enhancerHomologSearch VignetteBuilder: knitr BugReports: https://github.com/jianhong/enhancerHomologSearch/issues git_url: https://git.bioconductor.org/packages/enhancerHomologSearch git_branch: devel git_last_commit: 672f113 git_last_commit_date: 2024-11-18 Date/Publication: 2024-11-19 source.ver: src/contrib/enhancerHomologSearch_1.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/enhancerHomologSearch_1.13.1.zip vignettes: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html vignetteTitles: enhancerHomologSearch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R dependencyCount: 135 Package: EnMCB Version: 1.19.0 Depends: R (>= 4.0) Imports: survivalROC, glmnet, rms, mboost, Matrix, igraph, methods, survivalsvm, ggplot2, boot, e1071, survival, BiocFileCache Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, limma, rmarkdown License: GPL-2 Archs: x64 MD5sum: 115608284ef76981ca7edc3067aca08f NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: devel git_last_commit: a5bc079 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EnMCB_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnMCB_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnMCB_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnMCB_1.19.0.tgz vignettes: vignettes/EnMCB/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnMCB/inst/doc/vignette.R dependencyCount: 128 Package: ENmix Version: 1.43.2 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats,R (>= 3.5.0) Imports: grDevices,graphics,matrixStats,methods,utils,irlba, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 18d7f6ac2e91efd1222304afd7146f35 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/ENmix VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ENmix/issues git_url: https://git.bioconductor.org/packages/ENmix git_branch: devel git_last_commit: 7c51d75 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/ENmix_1.43.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/ENmix_1.43.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ENmix_1.43.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ENmix_1.43.2.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.html vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 163 Package: EnrichDO Version: 1.1.1 Depends: R (>= 4.0.0) Imports: BiocGenerics, Rgraphviz, clusterProfiler, hash, S4Vectors, dplyr, ggplot2, graph, magrittr, methods, pheatmap, graphics, utils, purrr, tidyr, stats Suggests: knitr, rmarkdown, org.Hs.eg.db, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: 7f906e0b549aa2bfdef20f22c1b27465 NeedsCompilation: no Title: a Global Weighted Model for Disease Ontology Enrichment Analysis Description: To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data. biocViews: Annotation, Visualization, GeneSetEnrichment, Software Author: Liang Cheng [aut], Haixiu Yang [aut], Hongyu Fu [cre] Maintainer: Hongyu Fu <2287531995@qq.com> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichDO git_branch: devel git_last_commit: ef3d0ef git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-29 source.ver: src/contrib/EnrichDO_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichDO_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichDO_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichDO_1.1.1.tgz vignettes: vignettes/EnrichDO/inst/doc/EnrichDO.html vignetteTitles: EnrichDO: Disease Ontology Enrichment Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichDO/inst/doc/EnrichDO.R dependencyCount: 128 Package: EnrichedHeatmap Version: 1.37.0 Depends: R (>= 3.6.0), methods, grid, ComplexHeatmap (>= 2.11.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: 4c2c644daf9ec8f4dfa8b53aecc3cf79 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: devel git_last_commit: 6e3469a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EnrichedHeatmap_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichedHeatmap_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichedHeatmap_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichedHeatmap_1.37.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R importsMe: profileplyr suggestsMe: ComplexHeatmap, epistack, extraChIPs, InteractiveComplexHeatmap dependencyCount: 48 Package: EnrichmentBrowser Version: 2.37.0 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown, statmod License: Artistic-2.0 MD5sum: b99ec3f4af1dbbca85ed27e822948cb1 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Rohit Satyam [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: devel git_last_commit: 0601e6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EnrichmentBrowser_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EnrichmentBrowser_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EnrichmentBrowser_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EnrichmentBrowser_2.37.0.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.html vignetteTitles: Seamless navigation through combined results of set- & network-based enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR, zenith suggestsMe: epiregulon.extra, GenomicSuperSignature, roastgsa, bugphyzz dependencyCount: 95 Package: enrichplot Version: 1.27.3 Depends: R (>= 3.5.0) Imports: aplot (>= 0.2.1), DOSE (>= 3.31.2), ggfun (>= 0.1.7), ggnewscale, ggplot2, ggrepel (>= 0.9.0), ggtangle (>= 0.0.5), graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, rlang, stats, utils, scatterpie, GOSemSim (>= 2.31.2), magrittr, ggtree, yulab.utils (>= 0.1.6) Suggests: clusterProfiler, dplyr, europepmc, ggarchery, ggupset, glue, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, ggHoriPlot, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggstar, scales, ggtreeExtra, tidydr License: Artistic-2.0 Archs: x64 MD5sum: 0ecddbe9d5bff6e8e6eae1a6fe3c2ac7 NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (ORCID: ), Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: devel git_last_commit: 0ca2fcf git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/enrichplot_1.27.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/enrichplot_1.27.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/enrichplot_1.27.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/enrichplot_1.27.3.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maEndToEnd importsMe: CBNplot, ChIPseeker, clusterProfiler, debrowser, enrichViewNet, meshes, MicrobiomeProfiler, ReactomePA, TDbasedUFEadv, ExpHunterSuite suggestsMe: GeoTcgaData, mastR, methylGSA, ReporterScore, SCpubr dependencyCount: 121 Package: enrichViewNet Version: 1.5.0 Depends: R (>= 4.2.0) Imports: gprofiler2, strex, RCy3, jsonlite, stringr, enrichplot, DOSE, methods Suggests: BiocStyle, knitr, rmarkdown, ggplot2, testthat, magick License: Artistic-2.0 MD5sum: bed8197731a8b8d52b0dd546efdd59eb NeedsCompilation: no Title: From functional enrichment results to biological networks Description: This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes. biocViews: BiologicalQuestion, Software, Network, NetworkEnrichment, GO Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Robert L. Faure [aut] (ORCID: ), Maria J. Fernandes [aut] (ORCID: ), Alexander Krasnitz [aut], David A. Tuveson [aut] (ORCID: ) Maintainer: Astrid Deschênes URL: https://github.com/adeschen/enrichViewNet, https://adeschen.github.io/enrichViewNet/ VignetteBuilder: knitr BugReports: https://github.com/adeschen/enrichViewNet/issues git_url: https://git.bioconductor.org/packages/enrichViewNet git_branch: devel git_last_commit: b9b8a23 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/enrichViewNet_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/enrichViewNet_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/enrichViewNet_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/enrichViewNet_1.5.0.tgz vignettes: vignettes/enrichViewNet/inst/doc/enrichViewNet.html vignetteTitles: From functional enrichment results to biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichViewNet/inst/doc/enrichViewNet.R dependencyCount: 162 Package: ensembldb Version: 2.31.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.49.6), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: cc0c3b5af780df595713644ad36a0dcb NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto Christian Weichenberger and Boyu Yu. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: devel git_last_commit: 9c06161 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ensembldb_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ensembldb_2.31.0.zip vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, demuxSNP, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: biovizBase, BUSpaRse, ChIPpeakAnno, CleanUpRNAseq, consensusDE, epimutacions, epivizrData, ggbio, GRaNIE, Gviz, RAIDS, RITAN, scanMiRApp, scFeatures, singleCellTK, TVTB, tximeta, GenomicDistributionsData, scRNAseq, crosstalkr, GRIN2, locuszoomr, MOCHA, RNAseqQC suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, CNVRanger, eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock, multicrispr, nullranges, satuRn, txdbmaker, wiggleplotr, celldex, GeneSelectR dependencyCount: 81 Package: epialleleR Version: 1.15.1 Depends: R (>= 4.1) Imports: stats, methods, utils, data.table, BiocGenerics, GenomicRanges, Rcpp LinkingTo: Rcpp, BH, Rhtslib Suggests: GenomeInfoDb, SummarizedExperiment, VariantAnnotation, RUnit, knitr, rmarkdown, ggplot2 License: Artistic-2.0 MD5sum: e28f7acd21d27b9f091d33b1f3a27e16 NeedsCompilation: yes Title: Fast, Epiallele-Aware Methylation Caller and Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation. biocViews: DNAMethylation, Epigenetics, MethylSeq, LongRead Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/epialleleR SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: devel git_last_commit: c786e6e git_last_commit_date: 2024-11-16 Date/Publication: 2024-11-17 source.ver: src/contrib/epialleleR_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/epialleleR_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epialleleR_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epialleleR_1.15.1.tgz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html, vignettes/epialleleR/inst/doc/values.html vignetteTitles: epialleleR, values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R, vignettes/epialleleR/inst/doc/values.R dependencyCount: 28 Package: EpiCompare Version: 1.11.0 Depends: R (>= 4.2.0) Imports: AnnotationHub, ChIPseeker, data.table, genomation, GenomicRanges, IRanges, GenomeInfoDb, ggplot2 (>= 3.5.0), htmltools, methods, plotly, reshape2, rmarkdown, rtracklayer, stats, stringr, utils, BiocGenerics, downloadthis, parallel Suggests: rworkflows, BiocFileCache, BiocParallel, BiocStyle, clusterProfiler, GenomicAlignments, grDevices, knitr, org.Hs.eg.db, testthat (>= 3.0.0), tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, ComplexUpset, plyranges, scales, Matrix, consensusSeekeR, heatmaply, viridis License: GPL-3 Archs: x64 MD5sum: 5ad7c2754505af5391abba542cdd42b7 NeedsCompilation: no Title: Comparison, Benchmarking & QC of Epigenomic Datasets Description: EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq Author: Sera Choi [aut] (ORCID: ), Brian Schilder [aut] (ORCID: ), Leyla Abbasova [aut], Alan Murphy [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ), Thomas Roberts [ctb], Hiranyamaya Dash [cre] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues git_url: https://git.bioconductor.org/packages/EpiCompare git_branch: devel git_last_commit: 5b7c54e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EpiCompare_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiCompare_1.11.0.zip vignettes: vignettes/EpiCompare/inst/doc/docker.html, vignettes/EpiCompare/inst/doc/EpiCompare.html, vignettes/EpiCompare/inst/doc/example_report.html vignetteTitles: docker, EpiCompare, example_report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiCompare/inst/doc/docker.R, vignettes/EpiCompare/inst/doc/EpiCompare.R, vignettes/EpiCompare/inst/doc/example_report.R dependencyCount: 193 Package: epidecodeR Version: 1.15.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 110d6263f49ca2fc1fd37b817180e819 NeedsCompilation: no Title: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation Description: epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes. biocViews: DifferentialExpression, GeneRegulation, HistoneModification, FunctionalPrediction, Transcription, GeneExpression, Epitranscriptomics, Epigenetics, FunctionalGenomics, SystemsBiology, Transcriptomics, ChipOnChip Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut] Maintainer: Kandarp Joshi URL: https://github.com/kandarpRJ/epidecodeR, https://epidecoder.shinyapps.io/shinyapp VignetteBuilder: knitr BugReports: https://github.com/kandarpRJ/epidecodeR/issues git_url: https://git.bioconductor.org/packages/epidecodeR git_branch: devel git_last_commit: 787612b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epidecodeR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epidecodeR_1.15.0.zip vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html vignetteTitles: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R dependencyCount: 124 Package: EpiDISH Version: 2.23.1 Depends: R (>= 4.1) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: 31ba6a7dc7c1bf5904d6b9ba13154405 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: devel git_last_commit: 150c50f git_last_commit_date: 2024-11-11 Date/Publication: 2024-11-11 source.ver: src/contrib/EpiDISH_2.23.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiDISH_2.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiDISH_2.23.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiDISH_2.23.1.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: planet dependencyCount: 26 Package: epigenomix Version: 1.47.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: c1c8152a61c4c779d2682db6ad7373e7 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: devel git_last_commit: 6b8078f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epigenomix_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epigenomix_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epigenomix_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epigenomix_1.47.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 108 Package: epigraHMM Version: 1.15.0 Depends: R (>= 3.5.0) Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib Suggests: testthat, knitr, rmarkdown, BiocStyle, BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData License: MIT + file LICENSE MD5sum: 0e85fd394d77a5e1684fd675387d6cc1 NeedsCompilation: yes Title: Epigenomic R-based analysis with hidden Markov models Description: epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions. biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics Author: Pedro Baldoni [aut, cre] Maintainer: Pedro Baldoni SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epigraHMM git_branch: devel git_last_commit: 524cd3d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epigraHMM_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epigraHMM_1.15.0.zip vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html vignetteTitles: Consensus and Differential Peak Calling With epigraHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R dependencyCount: 137 Package: EpiMix Version: 1.9.0 Depends: R (>= 4.2.0), EpiMix.data (>= 1.2.2) Imports: AnnotationHub, AnnotationDbi, Biobase, biomaRt, data.table, doParallel, doSNOW, downloader, dplyr, ELMER.data, ExperimentHub, foreach, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, impute, IRanges, limma, methods, parallel, plyr, progress, R.matlab, RColorBrewer, RCurl, rlang, RPMM, S4Vectors, stats, SummarizedExperiment, tibble, tidyr, utils Suggests: BiocStyle, clusterProfiler, DT, GEOquery, karyoploteR, knitr, org.Hs.eg.db, regioneR, Seurat, survival, survminer, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, multiMiR, miRBaseConverter License: GPL-3 Archs: x64 MD5sum: 3e493ced5bc2ab34dab0301693c3a12e NeedsCompilation: no Title: EpiMix: an integrative tool for the population-level analysis of DNA methylation Description: EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. biocViews: Software, Epigenetics, Preprocessing, DNAMethylation, GeneExpression, DifferentialMethylation Author: Yuanning Zheng [aut, cre], Markus Sujansky [aut], John Jun [aut], Olivier Gevaert [aut] Maintainer: Yuanning Zheng VignetteBuilder: knitr BugReports: https://github.com/gevaertlab/EpiMix/issues git_url: https://git.bioconductor.org/packages/EpiMix git_branch: devel git_last_commit: 0b423e1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EpiMix_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiMix_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiMix_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiMix_1.9.0.tgz vignettes: vignettes/EpiMix/inst/doc/Methylation_Modeling.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiMix/inst/doc/Methylation_Modeling.R importsMe: Moonlight2R dependencyCount: 138 Package: epimutacions Version: 1.11.0 Depends: R (>= 4.3.0), epimutacionsData Imports: minfi, bumphunter, isotree, robustbase, ggplot2, GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment, stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt, BiocParallel, GenomeInfoDb, Homo.sapiens, purrr, tibble, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, AnnotationDbi, AnnotationHub, ExperimentHub, reshape2, grid, ensembldb, gridExtra, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, ggrepel Suggests: testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra, methods, grDevices License: MIT + file LICENSE MD5sum: 3b139888cb6d8d8bfc12f55729a25eaa NeedsCompilation: yes Title: Robust outlier identification for DNA methylation data Description: The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ), Carles Hernandez-Ferrer [aut] (ORCID: ), Leire Abarrategui [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/epimutacions VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/epimutacions/issues git_url: https://git.bioconductor.org/packages/epimutacions git_branch: devel git_last_commit: 8a6e409 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epimutacions_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epimutacions_1.11.0.zip vignettes: vignettes/epimutacions/inst/doc/epimutacions.html vignetteTitles: Detection of epimutations with state of the art methods in methylation data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epimutacions/inst/doc/epimutacions.R dependencyCount: 223 Package: epiNEM Version: 1.31.0 Depends: R (>= 4.1) Imports: BoutrosLab.plotting.general, BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem, latex2exp Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db, BiocStyle License: GPL-3 MD5sum: ecb80a0c5c040677b87a82f5df5b9a99 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/epiNEM/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/epiNEM/issues git_url: https://git.bioconductor.org/packages/epiNEM git_branch: devel git_last_commit: 6d6ef3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epiNEM_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epiNEM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epiNEM_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epiNEM_1.31.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: bnem, dce, nempi suggestsMe: mnem dependencyCount: 113 Package: EpipwR Version: 1.1.0 Depends: R (>= 4.4.0) Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2 Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo License: Artistic-2.0 MD5sum: e18f3eae20a1c90e2185739208b8bfbd NeedsCompilation: no Title: Efficient Power Analysis for EWAS with Continuous or Binary Outcomes Description: A quasi-simulation based approach to performing power analysis for EWAS (Epigenome-wide association studies) with continuous or binary outcomes. 'EpipwR' relies on empirical EWAS datasets to determine power at specific sample sizes while keeping computational cost low. EpipwR can be run with a variety of standard statistical tests, controlling for either a false discovery rate or a family-wise type I error rate. biocViews: Epigenetics, ExperimentalDesign Author: Jackson Barth [aut, cre] (ORCID: ), Austin Reynolds [aut], Mary Lauren Benton [ctb], Carissa Fong [ctb] Maintainer: Jackson Barth URL: https://github.com/jbarth216/EpipwR VignetteBuilder: knitr BugReports: https://github.com/jbarth216/EpipwR git_url: https://git.bioconductor.org/packages/EpipwR git_branch: devel git_last_commit: d393930 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EpipwR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpipwR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpipwR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpipwR_1.1.0.tgz vignettes: vignettes/EpipwR/inst/doc/EpipwR.html vignetteTitles: EpipwR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpipwR/inst/doc/EpipwR.R dependencyCount: 85 Package: epiregulon Version: 1.3.2 Depends: R (>= 4.4), SingleCellExperiment Imports: AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp, S4Vectors, SummarizedExperiment, bluster, checkmate, entropy, lifecycle, methods, scran, scuttle, stats, utils, scMultiome (>= 1.7.1), GenomeInfoDb, GenomicRanges, AUCell, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, beachmat LinkingTo: Rcpp, beachmat, assorthead Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater, beachmat.hdf5 License: MIT + file LICENSE MD5sum: 29c068863b0aca497846d0b700e30e24 NeedsCompilation: yes Title: Gene regulatory network inference from single cell epigenomic data Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: SingleCell, GeneRegulation,NetworkInference,Network, GeneExpression, Transcription, GeneTarget Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Aaron Lun [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon/issues git_url: https://git.bioconductor.org/packages/epiregulon git_branch: devel git_last_commit: 16f994a git_last_commit_date: 2024-12-20 Date/Publication: 2024-12-20 source.ver: src/contrib/epiregulon_1.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/epiregulon_1.3.2.zip vignettes: vignettes/epiregulon/inst/doc/multiome.mae.html vignetteTitles: Epiregulon tutorial with MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon/inst/doc/multiome.mae.R suggestsMe: epiregulon.extra dependencyCount: 215 Package: epiregulon.extra Version: 1.3.0 Depends: R (>= 4.4), SingleCellExperiment Imports: scran, ComplexHeatmap, Matrix, SummarizedExperiment, checkmate, circlize, clusterProfiler, ggplot2, ggraph, igraph, lifecycle, patchwork, reshape2, scales, scater, stats Suggests: epiregulon, knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), EnrichmentBrowser, msigdbr, dorothea, scMultiome, S4Vectors, scuttle, vdiffr, ggrastr, ggrepel License: MIT + file LICENSE Archs: x64 MD5sum: 9d8b99f64ef6ccbba39f3d6ba76d5d0a NeedsCompilation: no Title: Companion package to epiregulon with additional plotting, differential and graph functions Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: GeneRegulation, Network, GeneExpression, Transcription, ChipOnChip, DifferentialExpression, GeneTarget, Normalization, GraphAndNetwork Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Timothy Keyes [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon.extra/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon.extra/issues git_url: https://git.bioconductor.org/packages/epiregulon.extra git_branch: devel git_last_commit: 788ca6a git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/epiregulon.extra_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epiregulon.extra_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epiregulon.extra_1.3.0.tgz vignettes: vignettes/epiregulon.extra/inst/doc/Data_visualization.html vignetteTitles: Data visualization with epiregulon.extra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon.extra/inst/doc/Data_visualization.R dependencyCount: 185 Package: epistack Version: 1.13.0 Depends: R (>= 4.1) Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors, IRanges, graphics, plotrix, grDevices, stats, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: 4fe2007e8a3a5219e937f574ce839987 NeedsCompilation: no Title: Heatmaps of Stack Profiles from Epigenetic Signals Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provided as a `GRanges` (easily obtained from `gtf` or `bed` files). 3) a score to sort the features, such as peak height or gene expression value. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression, Coverage Author: SACI Safia [aut], DEVAILLY Guillaume [cre, aut] Maintainer: DEVAILLY Guillaume URL: https://github.com/GenEpi-GenPhySE/epistack VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: devel git_last_commit: dab3b9d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epistack_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epistack_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epistack_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epistack_1.13.0.tgz vignettes: vignettes/epistack/inst/doc/using_epistack.html vignetteTitles: Using epistack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epistack/inst/doc/using_epistack.R dependencyCount: 38 Package: epistasisGA Version: 1.9.0 Depends: R (>= 4.2) Imports: BiocParallel, data.table, matrixStats, stats, survival, igraph, batchtools, qgraph, grDevices, parallel, ggplot2, grid, bigmemory, graphics, utils LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory Suggests: BiocStyle, knitr, rmarkdown, magrittr, kableExtra, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: eaa51002720a5d4f3b2e4f071a9ed4a8 NeedsCompilation: yes Title: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method Description: This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results. biocViews: Genetics, SNP, GeneticVariability Author: Michael Nodzenski [aut, cre], Juno Krahn [ctb] Maintainer: Michael Nodzenski URL: https://github.com/mnodzenski/epistasisGA VignetteBuilder: knitr BugReports: https://github.com/mnodzenski/epistasisGA/issues git_url: https://git.bioconductor.org/packages/epistasisGA git_branch: devel git_last_commit: a8c940b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epistasisGA_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epistasisGA_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epistasisGA_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epistasisGA_1.9.0.tgz vignettes: vignettes/epistasisGA/inst/doc/E_GADGETS.html, vignettes/epistasisGA/inst/doc/GADGETS.html, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.html vignetteTitles: E-GADGETS, GADGETS, Detecting Maternal-SNP Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epistasisGA/inst/doc/E_GADGETS.R, vignettes/epistasisGA/inst/doc/GADGETS.R, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.R dependencyCount: 117 Package: EpiTxDb Version: 1.19.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, rex, GenomicFeatures, txdbmaker, GenomicRanges, GenomeInfoDb, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: 37b0707c145961ac3d61bdd9aa073432 NeedsCompilation: no Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues git_url: https://git.bioconductor.org/packages/EpiTxDb git_branch: devel git_last_commit: 49a7f33 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EpiTxDb_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EpiTxDb_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EpiTxDb_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EpiTxDb_1.19.0.tgz vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html, vignettes/EpiTxDb/inst/doc/EpiTxDb.html vignetteTitles: EpiTxDb-creation, EpiTxDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R, vignettes/EpiTxDb/inst/doc/EpiTxDb.R dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3 dependencyCount: 122 Package: epivizr Version: 2.37.0 Depends: R (>= 3.5.0), methods Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi, rmarkdown License: Artistic-2.0 MD5sum: 146b736e811b17d71e0877d27069811e NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: devel git_last_commit: 4241f76 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epivizr_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizr_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizr_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizr_2.37.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone, scTreeViz dependencyCount: 124 Package: epivizrChart Version: 1.29.0 Depends: R (>= 3.5.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 Archs: x64 MD5sum: e59a113e0396dcb233c1eacefcd0bb33 NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: devel git_last_commit: 08a6956 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epivizrChart_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrChart_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrChart_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrChart_1.29.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 118 Package: epivizrData Version: 1.35.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI, matrixStats License: MIT + file LICENSE MD5sum: dd767cfce4b72d751c4a911d0f880c50 NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: devel git_last_commit: 0d48393 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epivizrData_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrData_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrData_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrData_1.35.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, scTreeViz dependencyCount: 115 Package: epivizrServer Version: 1.35.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: f71c0e0802fd906a9585b087585fa6a4 NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: devel git_last_commit: c1b4447 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-31 source.ver: src/contrib/epivizrServer_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrServer_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrServer_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrServer_1.35.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, scTreeViz dependencyCount: 14 Package: epivizrStandalone Version: 1.35.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 9d327d681d5c95be00f790dc1699711d NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: devel git_last_commit: d990d4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/epivizrStandalone_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/epivizrStandalone_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/epivizrStandalone_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/epivizrStandalone_1.35.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: scTreeViz dependencyCount: 126 Package: erccdashboard Version: 1.41.0 Depends: R (>= 4.0), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr, knitr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 90ca6a92236ef5e17be6cce4b48fbb1e NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: devel git_last_commit: e3aeeef git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/erccdashboard_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/erccdashboard_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/erccdashboard_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/erccdashboard_1.41.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.html vignetteTitles: erccdashboard introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 58 Package: erma Version: 1.23.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: c9321d28f6f8d63eb0b931af03a3f1f6 NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: devel git_last_commit: 646e63a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/erma_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/erma_1.23.0.zip vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R dependencyCount: 142 Package: ERSSA Version: 1.25.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), apeglm (>= 1.4.2), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 0f93d4ccb606a9742da91832aec42e63 NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: devel git_last_commit: 63a52f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ERSSA_1.25.0.tar.gz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 90 Package: esATAC Version: 1.29.0 Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicAlignments, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot, prettydoc License: GPL-3 | file LICENSE Archs: x64 MD5sum: 53d1c1e27d82624f135ff31aa35ba99f NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: devel git_last_commit: 508f345 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/esATAC_1.29.0.tar.gz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 200 Package: escape Version: 2.3.0 Depends: R (>= 4.1) Imports: AUCell, BiocParallel, grDevices, dplyr, ggdist, ggplot2, ggpointdensity, GSEABase, GSVA, SingleCellExperiment, ggridges, msigdbr, stats, reshape2, patchwork, MatrixGenerics, utils, SummarizedExperiment, UCell, stringr, methods, SeuratObject, Matrix Suggests: Seurat, hexbin, scran, knitr, rmarkdown, markdown, BiocStyle, RColorBrewer, rlang, spelling, testthat (>= 3.0.0), vdiffr License: MIT + file LICENSE MD5sum: 3a8549a14c3930ae5df297f3813d72e7 NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut], Alexei Martsinkovskiy [ctb] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/escape git_branch: devel git_last_commit: 78d6ceb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/escape_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/escape_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/escape_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/escape_2.3.0.tgz vignettes: vignettes/escape/inst/doc/vignette.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escape/inst/doc/vignette.R suggestsMe: Cepo dependencyCount: 173 Package: escheR Version: 1.7.0 Depends: ggplot2, R (>= 4.3) Imports: SpatialExperiment (>= 1.6.1), SingleCellExperiment, rlang, SummarizedExperiment Suggests: STexampleData, BumpyMatrix, knitr, rmarkdown, BiocStyle, ggpubr, scran, scater, scuttle, Seurat, hexbin License: MIT + file LICENSE MD5sum: 6c6b1f456e96392582fdc25e12cfa86a NeedsCompilation: no Title: Unified multi-dimensional visualizations with Gestalt principles Description: The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. biocViews: Spatial, SingleCell, Transcriptomics, Visualization, Software Author: Boyi Guo [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ), Erik D. Nelson [ctb] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/escheR VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/escheR/issues git_url: https://git.bioconductor.org/packages/escheR git_branch: devel git_last_commit: 6c6c207 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/escheR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/escheR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/escheR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/escheR_1.7.0.tgz vignettes: vignettes/escheR/inst/doc/more_than_visium.html, vignettes/escheR/inst/doc/SRT_eg.html vignetteTitles: beyond_visium, Getting Start with `escheR` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escheR/inst/doc/more_than_visium.R, vignettes/escheR/inst/doc/SRT_eg.R importsMe: SpotSweeper suggestsMe: tpSVG dependencyCount: 87 Package: esetVis Version: 1.33.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, plotly, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment, GO.db License: GPL-3 MD5sum: a7a2593f81e68ecaa4f63134e69b7b82 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: devel git_last_commit: 84fc5bd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/esetVis_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/esetVis_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/esetVis_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/esetVis_1.33.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 59 Package: eudysbiome Version: 1.37.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 29232592154e028bfecf74ee3d043260 NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: devel git_last_commit: 89cab1f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/eudysbiome_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/eudysbiome_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/eudysbiome_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/eudysbiome_1.37.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 45 Package: evaluomeR Version: 1.23.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15), RSKC (>= 2.4.2), sparcl (>= 1.0.4) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra, dplyr, dendextend (>= 1.16.0) Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 7f35c5e339a0764bfeed7433cd9137ff NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: devel git_last_commit: b2ae8f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/evaluomeR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/evaluomeR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/evaluomeR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/evaluomeR_1.23.0.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 125 Package: EventPointer Version: 3.15.0 Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings, glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport, fgsea Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: c4f7f8128c0d830799b4c4f13915f9b4 NeedsCompilation: yes Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre], Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo [aut], Ander Aramburu [aut], Angel Rubio [aut] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: devel git_last_commit: 8a000b5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EventPointer_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EventPointer_3.15.0.zip vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 150 Package: EWCE Version: 1.15.0 Depends: R (>= 4.2), RNOmni (>= 1.0) Imports: stats, utils, methods, ewceData (>= 1.7.1), dplyr, ggplot2, reshape2, limma, stringr, HGNChelper, Matrix, parallel, SingleCellExperiment, SummarizedExperiment, DelayedArray, BiocParallel, orthogene (>= 0.99.8), data.table Suggests: rworkflows, remotes, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2, MAST, DelayedMatrixStats, ggdendro, scales, patchwork License: GPL-3 MD5sum: 10f525b27828ef1053ed9876e12408db NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics, SingleCell Author: Alan Murphy [cre] (ORCID: ), Brian Schilder [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Alan Murphy URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr BugReports: https://github.com/NathanSkene/EWCE/issues git_url: https://git.bioconductor.org/packages/EWCE git_branch: devel git_last_commit: 0293aa5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/EWCE_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/EWCE_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/EWCE_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/EWCE_1.15.0.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html, vignettes/EWCE/inst/doc/extended.html vignetteTitles: Getting started, Extended examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R, vignettes/EWCE/inst/doc/extended.R dependencyCount: 192 Package: ExCluster Version: 1.25.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: d644973362555239eb94a7e45af0d753 NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: devel git_last_commit: 7e5c843 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExCluster_1.25.0.tar.gz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 60 Package: ExiMiR Version: 2.49.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: eb76e2b35dac49e6cf07656891c13a83 NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: devel git_last_commit: 4ca7e42 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExiMiR_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExiMiR_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExiMiR_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExiMiR_2.49.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 15 Package: ExperimentHub Version: 2.15.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.3.6), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub, GenomicAlignments Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: e937c1b2175175ea0f5bc0821b6275e6 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: devel git_last_commit: dace2c4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExperimentHub_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentHub_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHub_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHub_2.15.0.tgz vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, CoSIA, iSEEhub, LRcell, octad, SeqSQC, BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, CoSIAdata, crisprScoreData, curatedAdipoChIP, CytoMethIC, DMRcatedata, eoPredData, EpiMix.data, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, multiWGCNAdata, muscData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, octad.db, RNAmodR.Data, scMultiome, scpdata, sesameData, SimBenchData, SpatialDatasets, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, TENxXeniumData, VectraPolarisData, WeberDivechaLCdata importsMe: BiocHubsShiny, BloodGen3Module, CBNplot, coMethDMR, CTdata, DMRcate, EpiMix, epimutacions, EpipwR, epiregulon, ExperimentHubData, GSEABenchmarkeR, hpar, m6Aboost, MACSr, MatrixQCvis, MethReg, methylclock, Moonlight2R, MsDataHub, orthos, PhyloProfile, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, cfToolsData, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, easierData, emtdata, EpipwR.data, FieldEffectCrc, gDNAinRNAseqData, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HCATonsilData, HMP16SData, homosapienDEE2CellScore, humanHippocampus2024, imcdatasets, JohnsonKinaseData, LRcellTypeMarkers, marinerData, MerfishData, methylclockData, MethylSeqData, microbiomeDataSets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, orthosData, PhyloProfileData, preciseTADhub, ProteinGymR, raerdata, scaeData, scRNAseq, SFEData, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, xcoredata suggestsMe: AlphaMissenseR, ANF, AnnotationHub, bambu, Banksy, celaref, CellMapper, DESpace, dreamlet, ELMER, genomicInstability, HDF5Array, lute, mariner, missMethyl, MsBackendRawFileReader, multiWGCNA, muscat, nullranges, quantiseqr, rawDiag, rawrr, recountmethylation, SingleMoleculeFootprinting, SparseArray, SPOTlight, standR, TCGAbiolinks, TENxIO, Voyager, xcore, BioPlex, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, muleaData, smokingMouse, SubcellularSpatialData, tissueTreg, TransOmicsData dependencyCount: 66 Package: ExperimentHubData Version: 1.33.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub License: Artistic-2.0 MD5sum: f59c22bba53e21b309f77e18dd443ff0 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: devel git_last_commit: c820abe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExperimentHubData_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentHubData_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHubData_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHubData_1.33.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RNAmodR.Data importsMe: methylclockData suggestsMe: HubPub, MsDataHub, cfToolsData, homosapienDEE2CellScore, humanHippocampus2024, JohnsonKinaseData, marinerData, scMultiome, smokingMouse dependencyCount: 125 Package: ExperimentSubset Version: 1.17.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment, SpatialExperiment, TreeSummarizedExperiment Imports: methods, Matrix, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData, airway License: MIT + file LICENSE MD5sum: 3476fc9334c565b2acb699bb85215539 NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (ORCID: ), Muhammad Asif [aut, ths] (ORCID: ), Joshua D. Campbell [aut] (ORCID: ) Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: devel git_last_commit: 48fa542 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/ExperimentSubset_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExperimentSubset_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentSubset_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExperimentSubset_1.17.0.tgz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 92 Package: ExploreModelMatrix Version: 1.19.0 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 674f43803b15b909ad14dfce07ae17a8 NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression, ShinyApps Author: Charlotte Soneson [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Michael Love [aut] (ORCID: ), Florian Geier [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: devel git_last_commit: 9158d60 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExploreModelMatrix_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExploreModelMatrix_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExploreModelMatrix_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExploreModelMatrix_1.19.0.tgz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R dependencyCount: 87 Package: ExpressionAtlas Version: 1.35.0 Depends: R (>= 4.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2, RCurl, jsonlite, BiocStyle Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 09ba95f3fdcf829380ebd84da9b7dbd1 NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays [aut] (ORCID: ), Pedro Madrigal [cre] (ORCID: ) Maintainer: Pedro Madrigal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: devel git_last_commit: 08329fa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ExpressionAtlas_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ExpressionAtlas_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ExpressionAtlas_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ExpressionAtlas_1.35.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: spatialHeatmap dependencyCount: 69 Package: extraChIPs Version: 1.11.0 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 3.5.0), ggside (>= 0.3.1), SummarizedExperiment, tibble Imports: BiocIO, broom, ComplexUpset, csaw, dplyr (>= 1.1.1), edgeR (>= 4.0), forcats, GenomeInfoDb, GenomicAlignments, GenomicInteractions, ggforce, ggrepel, glue, grDevices, grid, InteractionSet, IRanges, matrixStats, methods, patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, utils, vctrs, VennDiagram Suggests: apeglm, BiocStyle, covr, DESeq2, EnrichedHeatmap, Gviz, harmonicmeanp, here, knitr, limma, magrittr, plyranges, quantro, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 090f03599f2f243e332e8d8c18f6bef9 NeedsCompilation: yes Title: Additional functions for working with ChIP-Seq data Description: This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented. biocViews: ChIPSeq, HiC, Sequencing, Coverage Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/extraChIPs VignetteBuilder: knitr BugReports: https://github.com/smped/extraChIPs/issues git_url: https://git.bioconductor.org/packages/extraChIPs git_branch: devel git_last_commit: f411e4c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/extraChIPs_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/extraChIPs_1.11.0.zip vignettes: vignettes/extraChIPs/inst/doc/differential_signal_fixed.html, vignettes/extraChIPs/inst/doc/differential_signal_sliding.html, vignettes/extraChIPs/inst/doc/range_based_functions.html vignetteTitles: Differential Signal Analysis (Fixed-Width Windows), Differential Signal Analysis (Sliding Windows), Range-Based Operations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/extraChIPs/inst/doc/differential_signal_fixed.R, vignettes/extraChIPs/inst/doc/differential_signal_sliding.R, vignettes/extraChIPs/inst/doc/range_based_functions.R suggestsMe: motifTestR, transmogR dependencyCount: 177 Package: fabia Version: 2.53.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 6f88996bb803626d6b467569484b3ce4 NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: devel git_last_commit: c680892 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fabia_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fabia_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fabia_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fabia_2.53.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust importsMe: miRSM, mosbi, BcDiag suggestsMe: fabiaData dependencyCount: 8 Package: factDesign Version: 1.83.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: bc9f865a0992acc847d3be2ff52cf188 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: devel git_last_commit: f7b7be7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/factDesign_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/factDesign_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/factDesign_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/factDesign_1.83.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 7 Package: factR Version: 1.9.0 Depends: R (>= 4.2) Imports: BiocGenerics (>= 0.46), Biostrings (>= 2.68), GenomeInfoDb (>= 1.36), dplyr (>= 1.1), GenomicFeatures (>= 1.52), GenomicRanges (>= 1.52), IRanges (>= 2.34), purrr (>= 1.0), rtracklayer (>= 1.60), tidyr (>= 1.3), methods (>= 4.3), BiocParallel (>= 1.34), S4Vectors (>= 0.38), data.table (>= 1.14), rlang (>= 1.1), tibble (>= 3.2), wiggleplotr (>= 1.24), RCurl (>= 1.98), XML (>= 3.99), drawProteins (>= 1.20), ggplot2 (>= 3.4), stringr (>= 1.5), pbapply (>= 1.7), stats (>= 4.3), utils (>= 4.3), graphics (>= 4.3), crayon (>= 1.5) Suggests: AnnotationHub (>= 2.22), BSgenome (>= 1.58), BSgenome.Mmusculus.UCSC.mm10, testthat, knitr, rmarkdown, markdown, zeallot, rmdformats, bio3d (>= 2.4), signalHsmm (>= 1.5), tidyverse (>= 1.3), covr, patchwork License: file LICENSE MD5sum: 8de097d63ba83be56f44f612a5bd6995 NeedsCompilation: no Title: Functional Annotation of Custom Transcriptomes Description: factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts. biocViews: AlternativeSplicing, FunctionalPrediction, GenePrediction Author: Fursham Hamid [aut, cre] Maintainer: Fursham Hamid URL: https://fursham-h.github.io/factR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/factR git_branch: devel git_last_commit: c6ff9f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/factR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/factR_1.9.0.zip vignettes: vignettes/factR/inst/doc/factR.html vignetteTitles: factR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/factR/inst/doc/factR.R dependencyCount: 117 Package: faers Version: 1.3.0 Depends: R (>= 3.5.0) Imports: BiocParallel, brio, cli, curl (>= 5.0.0), data.table, httr2 (>= 1.0.0), MCMCpack, methods, openEBGM, rlang (>= 1.1.0), rvest, tools, utils, vroom, xml2 Suggests: BiocStyle, countrycode, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c659ef70405a1b857be76f4dce0bf4f5 NeedsCompilation: no Title: R interface for FDA Adverse Event Reporting System Description: The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis. biocViews: Software, DataImport, BiomedicalInformatics, Pharmacogenomics, Pharmacogenomics Author: Yun Peng [aut, cre] (ORCID: ), YuXuan Song [aut], Caipeng Qin [aut], JiaXing Lin [aut] Maintainer: Yun Peng VignetteBuilder: knitr BugReports: https://github.com/Yunuuuu/faers git_url: https://git.bioconductor.org/packages/faers git_branch: devel git_last_commit: f960e35 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/faers_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/faers_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/faers_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/faers_1.3.0.tgz vignettes: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html vignetteTitles: FAERS-Pharmacovigilance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.R dependencyCount: 79 Package: FamAgg Version: 1.35.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 682aa0a5035619b1f74690d3a0d09ed7 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: devel git_last_commit: 898172e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FamAgg_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FamAgg_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FamAgg_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FamAgg_1.35.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 93 Package: famat Version: 1.17.0 Depends: R (>= 4.0) Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 Archs: x64 MD5sum: 1fc08d15147121d5eb1f202772106df0 NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Mathieu Charles [aut, cre] (ORCID: ) Maintainer: Mathieu Charles URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: devel git_last_commit: 3a4f53d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/famat_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/famat_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/famat_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/famat_1.17.0.tgz vignettes: vignettes/famat/inst/doc/famat.html vignetteTitles: famat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/famat/inst/doc/famat.R dependencyCount: 171 Package: fastLiquidAssociation Version: 1.43.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 Archs: x64 MD5sum: c996aa12578383ae42e455c1c742fbdd NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: devel git_last_commit: 62d6792 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fastLiquidAssociation_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastLiquidAssociation_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fastLiquidAssociation_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fastLiquidAssociation_1.43.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 124 Package: FastqCleaner Version: 1.25.0 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 0d04173e47e0b2486ca4ab67d51df272 NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: devel git_last_commit: 2a95050 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FastqCleaner_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FastqCleaner_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FastqCleaner_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FastqCleaner_1.25.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.html vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 101 Package: fastreeR Version: 1.11.0 Depends: R (>= 4.4) Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava, stats, stringr, utils Suggests: BiocFileCache, BiocStyle, graphics, knitr, memuse, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: b599d5d7903295ab2aa4c4789d92c9dc NeedsCompilation: no Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta Files Description: Calculate distances, build phylogenetic trees or perform hierarchical clustering between the samples of a VCF or FASTA file. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on the VCF or FASTA file for fast execution. biocViews: Phylogenetics, Metagenomics, Clustering Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/fastreeR/issues git_url: https://git.bioconductor.org/packages/fastreeR git_branch: devel git_last_commit: 5904abc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fastreeR_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastreeR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fastreeR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fastreeR_1.11.0.tgz vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html vignetteTitles: fastreeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R dependencyCount: 27 Package: fastseg Version: 1.53.2 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo, BiocStyle, knitr License: LGPL (>= 2.0) MD5sum: eec5cca5413736e38e1fa8633c2777f4 NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer [aut], Sonali Kumari [ctb], Alexander Blume [cre] Maintainer: Alexander Blume URL: http://www.bioinf.jku.at/software/fastseg/index.html VignetteBuilder: knitr BugReports: https://github.com/alexg9010/fastseg/issues git_url: https://git.bioconductor.org/packages/fastseg git_branch: devel git_last_commit: aa245e2 git_last_commit_date: 2024-11-22 Date/Publication: 2024-11-22 source.ver: src/contrib/fastseg_1.53.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/fastseg_1.53.2.zip vignettes: vignettes/fastseg/inst/doc/fastseg.html vignetteTitles: An R Package for fast segmentation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 26 Package: fCCAC Version: 1.33.0 Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: b0eca5ee92acc13b0201d00211388ed6 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics, RNASeq, ATACSeq, MNaseSeq Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: devel git_last_commit: 55a4b1b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fCCAC_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fCCAC_1.33.0.zip vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 135 Package: fCI Version: 1.37.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: f1ee633226c453188dc69b085d214810 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: devel git_last_commit: 81521f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fCI_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fCI_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fCI_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fCI_1.37.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 50 Package: fcScan Version: 1.21.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 8acd48c61ad91b0a91f0df8a849a4c11 NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen [ctb], Pierre Khoueiry [aut, cre] Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: devel git_last_commit: 59bb726 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fcScan_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fcScan_1.21.0.zip vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 85 Package: fdrame Version: 1.79.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 161d5e89b1a0b09b7f980909eab8d915 NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: devel git_last_commit: 0ca5353 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fdrame_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fdrame_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fdrame_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fdrame_1.79.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.15.0 Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: 2d1a2bf5cc520ae800a334356b8e4c0d NeedsCompilation: yes Title: FEAture SelcTion (FEAST) for Single-cell clustering Description: Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set. biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: devel git_last_commit: 169e892 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FEAST_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FEAST_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FEAST_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FEAST_1.15.0.tgz vignettes: vignettes/FEAST/inst/doc/FEAST.html vignetteTitles: The FEAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEAST/inst/doc/FEAST.R dependencyCount: 126 Package: FeatSeekR Version: 1.7.0 Imports: pheatmap, MASS, pracma, stats, SummarizedExperiment, methods Suggests: rmarkdown, knitr, BiocStyle, DmelSGI, testthat (>= 3.0.0) License: GPL-3 MD5sum: d677b68a81f93bdd4114ebf9bdb7f5af NeedsCompilation: no Title: FeatSeekR an R package for unsupervised feature selection Description: FeatSeekR performs unsupervised feature selection using replicated measurements. It iteratively selects features with the highest reproducibility across replicates, after projecting out those dimensions from the data that are spanned by the previously selected features. The selected a set of features has a high replicate reproducibility and a high degree of uniqueness. biocViews: Software, StatisticalMethod, FeatureExtraction, MassSpectrometry Author: Tuemay Capraz [cre, aut] (ORCID: ) Maintainer: Tuemay Capraz URL: https://github.com/tcapraz/FeatSeekR VignetteBuilder: knitr BugReports: https://github.com/tcapraz/FeatSeekR/issues git_url: https://git.bioconductor.org/packages/FeatSeekR git_branch: devel git_last_commit: 404e1a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FeatSeekR_1.7.0.tar.gz vignettes: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.html vignetteTitles: `FeatSeekR` user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.R dependencyCount: 52 Package: fedup Version: 1.15.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE Archs: x64 MD5sum: f99a7f5d80e5f8a428934551f48721b0 NeedsCompilation: no Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways Description: An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results. biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network Author: Catherine Ross [aut, cre] Maintainer: Catherine Ross URL: https://github.com/rosscm/fedup VignetteBuilder: knitr BugReports: https://github.com/rosscm/fedup/issues git_url: https://git.bioconductor.org/packages/fedup git_branch: devel git_last_commit: 6f5f84f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fedup_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fedup_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fedup_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fedup_1.15.0.tgz vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html, vignettes/fedup/inst/doc/fedup_mutliTest.html, vignettes/fedup/inst/doc/fedup_singleTest.html vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html, fedup_singleTest.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R, vignettes/fedup/inst/doc/fedup_mutliTest.R, vignettes/fedup/inst/doc/fedup_singleTest.R dependencyCount: 81 Package: FELLA Version: 1.27.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 6746a4f3dc2949cd600dcd7a5dc6154a NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: devel git_last_commit: d1ba32c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FELLA_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FELLA_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FELLA_1.27.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 42 Package: fenr Version: 1.5.0 Depends: R (>= 4.1.0) Imports: tools, methods, assertthat, rlang, dplyr, tidyr, tidyselect, tibble, purrr, readr, stringr, httr2, rvest, progress, BiocFileCache, shiny, ggplot2 Suggests: BiocStyle, testthat, knitr, rmarkdown, topGO License: MIT + file LICENSE MD5sum: 9d9a921be969c9c87d12c578bcd577f3 NeedsCompilation: no Title: Fast functional enrichment for interactive applications Description: Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages. biocViews: FunctionalPrediction, DifferentialExpression, GeneSetEnrichment, GO, KEGG, Reactome, Proteomics Author: Marek Gierlinski [aut, cre] (ORCID: ) Maintainer: Marek Gierlinski URL: https://github.com/bartongroup/fenr VignetteBuilder: knitr BugReports: https://github.com/bartongroup/fenr/issues git_url: https://git.bioconductor.org/packages/fenr git_branch: devel git_last_commit: 5674137 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fenr_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fenr_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fenr_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fenr_1.5.0.tgz vignettes: vignettes/fenr/inst/doc/fenr.html vignetteTitles: Fast functional enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fenr/inst/doc/fenr.R dependencyCount: 93 Package: ffpe Version: 1.51.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 8fe98a07b548c031c65ff2581ce583b3 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: devel git_last_commit: a03eb82 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ffpe_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ffpe_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ffpe_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ffpe_1.51.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 170 Package: fgga Version: 1.15.0 Depends: R (>= 4.3), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl, igraph Suggests: knitr, rmarkdown, GOstats, GO.db, BiocGenerics, pROC, RUnit, BiocStyle License: GPL-3 Archs: x64 MD5sum: 82c13438996d2226e908550f702bdeec NeedsCompilation: no Title: Hierarchical ensemble method based on factor graph Description: Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models. biocViews: Software, StatisticalMethod, Classification, Network, NetworkInference, SupportVectorMachine, GraphAndNetwork, GO Author: Flavio Spetale [aut, cre] Maintainer: Flavio Spetale URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: devel git_last_commit: df19642 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fgga_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fgga_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fgga_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fgga_1.15.0.tgz vignettes: vignettes/fgga/inst/doc/fgga.html vignetteTitles: FGGA: Factor Graph GO Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgga/inst/doc/fgga.R dependencyCount: 63 Package: FGNet Version: 3.41.0 Depends: R (>= 4.2.0) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager License: GPL (>= 2) Archs: x64 MD5sum: 296bb447f054a159036041ac14debaa1 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: devel git_last_commit: a782bd5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FGNet_3.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FGNet_3.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FGNet_3.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FGNet_3.41.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 31 Package: fgsea Version: 1.33.2 Depends: R (>= 4.1) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), cowplot, grid, fastmatch, Matrix, scales, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery, msigdbr, aggregation, Seurat License: MIT + file LICENCE MD5sum: 3c4bfdfd8413ffa779bff50599c36497 NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin [ctb], Nikita Gusak [ctb], Zieman Mark [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: devel git_last_commit: f5458a4 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/fgsea_1.33.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/fgsea_1.33.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fgsea_1.33.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fgsea_1.33.2.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html, vignettes/fgsea/inst/doc/geseca-tutorial.html vignetteTitles: Using fgsea package, Gene set co-regulation analysis tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R, vignettes/fgsea/inst/doc/geseca-tutorial.R dependsOnMe: gsean, metapone, PPInfer importsMe: BioNAR, CelliD, CEMiTool, clustifyr, CoGAPS, cTRAP, DeepTarget, DOSE, EventPointer, fobitools, lipidr, mCSEA, MIRit, MPAC, multiGSEA, NanoTube, nipalsMCIA, omicsViewer, pathlinkR, phantasus, piano, POMA, projectR, RegEnrich, RegionalST, signatureSearch, ViSEAGO, cinaR, DTSEA, mulea, scITD suggestsMe: Cepo, decoupleR, gatom, gCrisprTools, iSEEpathways, mdp, sparrow, SpliceWiz, ttgsea, easybio, genekitr, GeneNMF, goat, grandR, Platypus, RCPA, rliger dependencyCount: 49 Package: FilterFFPE Version: 1.17.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: 95b123bc3a68ee984bd769e45d91b124 NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: devel git_last_commit: 494b4cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FilterFFPE_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FilterFFPE_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FilterFFPE_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FilterFFPE_1.17.0.tgz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 43 Package: findIPs Version: 1.3.1 Depends: graphics, R (>= 4.4.0) Imports: Biobase, BiocParallel, parallel, stats, SummarizedExperiment, survival, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: ba9d75cf5c14d21a1742aaab907909be NeedsCompilation: no Title: Influential Points Detection for Feature Rankings Description: Feature rankings can be distorted by a single case in the context of high-dimensional data. The cases exerts abnormal influence on feature rankings are called influential points (IPs). The package aims at detecting IPs based on case deletion and quantifies their effects by measuring the rank changes (DOI:10.48550/arXiv.2303.10516). The package applies a novel rank comparing measure using the adaptive weights that stress the top-ranked important features and adjust the weights to ranking properties. biocViews: GeneExpression, DifferentialExpression, Regression, Survival Author: Shuo Wang [aut, cre] (ORCID: ), Junyan Lu [aut] Maintainer: Shuo Wang URL: https://github.com/ShuoStat/findIPs VignetteBuilder: knitr BugReports: https://github.com/ShuoStat/findIPs git_url: https://git.bioconductor.org/packages/findIPs git_branch: devel git_last_commit: 342a7ad git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-21 source.ver: src/contrib/findIPs_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/findIPs_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/findIPs_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/findIPs_1.3.1.tgz vignettes: vignettes/findIPs/inst/doc/findIPs.html vignetteTitles: Introduction to package findIPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/findIPs/inst/doc/findIPs.R dependencyCount: 49 Package: FindIT2 Version: 1.13.0 Depends: GenomicRanges, R (>= 3.5.0) Imports: withr, BiocGenerics, GenomeInfoDb, rtracklayer, S4Vectors, GenomicFeatures, dplyr, rlang, patchwork, ggplot2, BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble, tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges, progress, purrr, glmnet, methods Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), TxDb.Athaliana.BioMart.plantsmart28 License: Artistic-2.0 MD5sum: 0a6681e6670668ad7b6148d15007c16c NeedsCompilation: no Title: find influential TF and Target based on multi-omics data Description: This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation, MultipleComparison, GeneTarget Author: Guandong Shang [aut, cre] (ORCID: ) Maintainer: Guandong Shang URL: https://github.com/shangguandong1996/FindIT2 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/FindIT2 git_url: https://git.bioconductor.org/packages/FindIT2 git_branch: devel git_last_commit: 30235ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FindIT2_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FindIT2_1.13.0.zip vignettes: vignettes/FindIT2/inst/doc/FindIT2.html vignetteTitles: Introduction to FindIT2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R dependencyCount: 121 Package: FISHalyseR Version: 1.41.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 78b4d81d5aabf926774d3630e118e6a0 NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: devel git_last_commit: 51ff6ab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FISHalyseR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FISHalyseR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FISHalyseR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FISHalyseR_1.41.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 46 Package: fishpond Version: 2.13.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, IRanges, SummarizedExperiment, GenomicRanges, matrixStats, svMisc, Matrix, SingleCellExperiment, jsonlite Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma, ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi, pheatmap, Gviz, GenomeInfoDb, data.table License: GPL-2 MD5sum: e20339fe0e023b304ef15f4525418f04 NeedsCompilation: no Title: Fishpond: downstream methods and tools for expression data Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb], Jeroen Gilis [ctb] Maintainer: Michael Love URL: https://thelovelab.github.io/fishpond, https://thelovelab.com/mikelove/fishpond VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/fishpond git_url: https://git.bioconductor.org/packages/fishpond git_branch: devel git_last_commit: df45f79 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fishpond_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fishpond_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fishpond_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fishpond_2.13.0.tgz vignettes: vignettes/fishpond/inst/doc/allelic.html, vignettes/fishpond/inst/doc/swish.html vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and Swish, 1. Swish: DE analysis accounting for inferential uncertainty hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/allelic.R, vignettes/fishpond/inst/doc/swish.R suggestsMe: tximeta dependencyCount: 72 Package: FitHiC Version: 1.33.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 77f4113d1dd1bebcfb0b6a79964b17e3 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: devel git_last_commit: 75885eb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FitHiC_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FitHiC_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FitHiC_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FitHiC_1.33.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.63.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) MD5sum: 591c172145b311782550652a081ff340 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography-massspectrometry metabolomics data. biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: devel git_last_commit: 7b496c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flagme_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flagme_1.63.0.zip vignettes: vignettes/flagme/inst/doc/flagme-knitr.pdf, vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data, \texttt{flagme}: Fragment-level analysis of \\ GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme-knitr.R, vignettes/flagme/inst/doc/flagme.R dependencyCount: 168 Package: FLAMES Version: 2.1.3 Depends: R (>= 4.1.0) Imports: basilisk, bambu, BiocParallel, Biostrings, BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, DropletUtils, GenomicRanges, GenomicFeatures, txdbmaker, GenomicAlignments, GenomeInfoDb, ggplot2, ggbio, grid, gridExtra, igraph, jsonlite, magrittr, Matrix, MatrixGenerics, parallel, readr, reticulate, Rsamtools, rtracklayer, RColorBrewer, SingleCellExperiment, SummarizedExperiment, SpatialExperiment, scater, scatterpie, S4Vectors, scuttle, stats, scran, stringr, tidyr, utils, withr, future, methods, tibble, tidyselect, IRanges LinkingTo: Rcpp, Rhtslib, testthat Suggests: BiocStyle, GEOquery, knitr, rmarkdown, BiocFileCache, R.utils, ShortRead, uwot, testthat (>= 3.0.0), xml2 License: GPL (>= 3) MD5sum: 399ad90c3e0aeb626a28b1883db69cfb NeedsCompilation: yes Title: FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data Description: Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution. biocViews: RNASeq, SingleCell, Transcriptomics, DataImport, DifferentialSplicing, AlternativeSplicing, GeneExpression, LongRead Author: Luyi Tian [aut], Changqing Wang [aut, cre], Yupei You [aut], Oliver Voogd [aut], Jakob Schuster [aut], Shian Su [aut], Matthew Ritchie [ctb] Maintainer: Changqing Wang URL: https://mritchielab.github.io/FLAMES SystemRequirements: GNU make, C++17, samtools (>= 1.19), minimap2 (>= 2.17) VignetteBuilder: knitr BugReports: https://github.com/mritchielab/FLAMES/issues git_url: https://git.bioconductor.org/packages/FLAMES git_branch: devel git_last_commit: 80ab567 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-19 source.ver: src/contrib/FLAMES_2.1.3.tar.gz vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html vignetteTitles: FLAMES hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R dependencyCount: 258 Package: flowAI Version: 1.37.0 Depends: R (>= 4.3.0) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) MD5sum: aa15855427ebd549be0f2ace4824d8a4 NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco [aut], Chen Hao [ctb] Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: devel git_last_commit: 1a2a8aa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowAI_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowAI_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowAI_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowAI_1.37.0.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R importsMe: CytoPipeline dependencyCount: 76 Package: flowBeads Version: 1.45.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 2f63d77151964c63750f7d7530c54a0f NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: devel git_last_commit: e4e2a74 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowBeads_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowBeads_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowBeads_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowBeads_1.45.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 32 Package: flowBin Version: 1.43.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: f169c6bcf9a0e52c910a6c3724ce992d NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: devel git_last_commit: 6f2acd3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowBin_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowBin_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowBin_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowBin_1.43.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 37 Package: flowcatchR Version: 1.41.2 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 8b33589b5bfff2904f2576fac678a057 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: devel git_last_commit: d498f26 git_last_commit_date: 2024-12-20 Date/Publication: 2024-12-20 source.ver: src/contrib/flowcatchR_1.41.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowcatchR_1.41.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowcatchR_1.41.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowcatchR_1.41.2.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 104 Package: flowCHIC Version: 1.41.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 Archs: x64 MD5sum: e5cbe7d93d74ff61e7d1067b4574e905 NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: devel git_last_commit: 837b51d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowCHIC_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCHIC_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCHIC_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCHIC_1.41.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 84 Package: flowClean Version: 1.45.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 Archs: x64 MD5sum: 3cf491041e7731167da60244d31a92fa NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: devel git_last_commit: c666a91 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowClean_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowClean_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowClean_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowClean_1.45.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 25 Package: flowClust Version: 3.45.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto, flowStats(>= 4.7.1) License: MIT MD5sum: 57819f2b9b3b23e073714c8bd6cf28af NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo, Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: devel git_last_commit: ace5f62 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowClust_3.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowClust_3.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowClust_3.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowClust_3.45.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: cyanoFilter, flowTrans suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 20 Package: flowCore Version: 2.19.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.13.1), S4Vectors LinkingTo: cpp11, BH(>= 1.81.0.0), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 MD5sum: 8a3b99be4b7fba3f88b1188adb7f6b8a NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: devel git_last_commit: 46f519e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowCore_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCore_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCore_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCore_2.19.0.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, cyanoFilter, cydar, cytofQC, CytoMDS, cytoMEM, CytoML, CytoPipeline, CytoPipelineGUI, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowGate, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowViz, flowWorkspace, GateFinder, MAPFX, MetaCyto, PeacoQC, scDataviz, scifer, Sconify, tidyFlowCore, tidytof suggestsMe: COMPASS, flowPeaks, flowPloidyData, hypergate, segmenTier dependencyCount: 17 Package: flowCut Version: 1.17.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 MD5sum: 93d8aa49eeba59903cc361bd17c37b03 NeedsCompilation: no Title: Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: devel git_last_commit: d6a0c68 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowCut_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCut_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCut_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCut_1.17.0.tgz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 98 Package: flowCyBar Version: 1.43.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 271f0dc9f3e3c45ab610787efb57aac5 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: devel git_last_commit: 2c9345f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowCyBar_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowCyBar_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowCyBar_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowCyBar_1.43.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.41.0 Imports: flowCore, graphics, flowViz (>= 1.42), car, polyclip, gplots, methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: 2d73b52b7ae3ade91d2388ae8973733c NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: devel git_last_commit: 77d7444 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowDensity_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowDensity_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowDensity_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowDensity_1.41.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 93 Package: flowFP Version: 1.65.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: 8960d5dd75c50f0bfb78b56d40df4d43 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst , Wade Rogers git_url: https://git.bioconductor.org/packages/flowFP git_branch: devel git_last_commit: a7c3d15 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowFP_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowFP_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowFP_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowFP_1.65.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 32 Package: flowGate Version: 1.7.0 Depends: flowWorkspace (>= 4.0.6), ggcyto (>= 1.16.0), R (>= 4.2) Imports: shiny (>= 1.5.0), BiocManager (>= 1.30.10), flowCore (>= 2.0.1), dplyr (>= 1.0.0), ggplot2 (>= 3.3.2), rlang (>= 0.4.7), purrr, tibble, methods Suggests: knitr, rmarkdown, stringr, tidyverse, testthat License: MIT + file LICENSE Archs: x64 MD5sum: ef344f078f9cfd659489233cd4308d4c NeedsCompilation: no Title: Interactive Cytometry Gating in R Description: flowGate adds an interactive Shiny app to allow manual GUI-based gating of flow cytometry data in R. Using flowGate, you can draw 1D and 2D span/rectangle gates, quadrant gates, and polygon gates on flow cytometry data by interactively drawing the gates on a plot of your data, rather than by specifying gate coordinates. This package is especially geared toward wet-lab cytometerists looking to take advantage of R for cytometry analysis, without necessarily having a lot of R experience. biocViews: Software, WorkflowStep, FlowCytometry, Preprocessing, ImmunoOncology, DataImport Author: Andrew Wight [aut, cre], Harvey Cantor [aut, ldr] Maintainer: Andrew Wight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowGate git_branch: devel git_last_commit: abd76a4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowGate_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowGate_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowGate_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowGate_1.7.0.tgz vignettes: vignettes/flowGate/inst/doc/flowGate.html vignetteTitles: flowGate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowGate/inst/doc/flowGate.R dependencyCount: 94 Package: flowGraph Version: 1.15.0 Depends: R (>= 4.1) Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2, igraph, Matrix, matrixStats, stats, utils, visNetwork, htmlwidgets, grDevices, methods, stringr, stringi, Rdpack, data.table (>= 1.9.5), gridExtra, Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: x64 MD5sum: 9aa6068aa8cf283d729cba4f4c0d23ea NeedsCompilation: no Title: Identifying differential cell populations in flow cytometry data accounting for marker frequency Description: Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts. biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software, CellBasedAssays, Visualization Author: Alice Yue [aut, cre] Maintainer: Alice Yue URL: https://github.com/aya49/flowGraph VignetteBuilder: knitr BugReports: https://github.com/aya49/flowGraph/issues git_url: https://git.bioconductor.org/packages/flowGraph git_branch: devel git_last_commit: dd112bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowGraph_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowGraph_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowGraph_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowGraph_1.15.0.tgz vignettes: vignettes/flowGraph/inst/doc/flowGraph.html vignetteTitles: flowGraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R dependencyCount: 82 Package: flowMatch Version: 1.43.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: aafae8ac42f571d3ddee11b18cf8504f NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: devel git_last_commit: 8cc0506 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowMatch_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMatch_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMatch_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMatch_1.43.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 18 Package: flowMeans Version: 1.67.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: e57e6b9cfe2dd6923cf6a5051c1922e1 NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: devel git_last_commit: e87c7ac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowMeans_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMeans_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMeans_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMeans_1.67.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 40 Package: flowMerge Version: 2.55.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 04b4264419548b5dc4f1522582913c5a NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: devel git_last_commit: e5ea0ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowMerge_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowMerge_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowMerge_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowMerge_2.55.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 48 Package: flowPeaks Version: 1.53.2 Depends: R (>= 2.12.0) Suggests: flowCore License: Artistic-1.0 Archs: x64 MD5sum: 4b7d0b472113651726bbecbbace04a11 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: devel git_last_commit: 8974c0d git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-22 source.ver: src/contrib/flowPeaks_1.53.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPeaks_1.53.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPeaks_1.53.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowPeaks_1.53.2.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust dependencyCount: 0 Package: flowPloidy Version: 1.33.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: 6af5ce927d0f25adbf60b75066a2a0b6 NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: devel git_last_commit: 1117775 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowPloidy_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPloidy_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPloidy_1.33.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 111 Package: flowPlots Version: 1.55.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: ab78689d9417b284d2f745a0c6034449 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: devel git_last_commit: bd7ddaf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowPlots_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowPlots_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowPlots_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowPlots_1.55.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowSOM Version: 2.15.0 Depends: R (>= 4.0), igraph Imports: stats, utils, colorRamps, ConsensusClusterPlus, dplyr, flowCore, ggforce, ggnewscale, ggplot2, ggpubr, grDevices, magrittr, methods, rlang, Rtsne, tidyr, BiocGenerics, XML Suggests: BiocStyle, testthat, CytoML, flowWorkspace, ggrepel, scattermore, pheatmap, ggpointdensity License: GPL (>= 2) Archs: x64 MD5sum: ae5fca973d7c296c74a4584bb3c6ccf1 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: devel git_last_commit: b617807 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FlowSOM_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FlowSOM_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FlowSOM_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FlowSOM_2.15.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, diffcyt suggestsMe: tidytof, HDCytoData dependencyCount: 99 Package: flowSpecs Version: 1.21.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: e89e12aae367e03e3c36c9cc3c915c85 NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: devel git_last_commit: 5ac3e4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowSpecs_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowSpecs_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowSpecs_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowSpecs_1.21.0.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 62 Package: flowStats Version: 4.19.0 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor, mnormt, clue Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 38e375e92a235038ab9ea6f5b20abeac NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: devel git_last_commit: 463f04e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowStats_4.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowStats_4.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowStats_4.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowStats_4.19.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowClust, flowCore, flowTime, flowViz, ggcyto, openCyto dependencyCount: 101 Package: flowTime Version: 1.31.0 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats, flowClust, openCyto, flowStats, ggcyto License: Artistic-2.0 Archs: x64 MD5sum: 479db96b4a29d2fe0986cbf3e1bcd91b NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: devel git_last_commit: e95fbb1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowTime_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowTime_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowTime_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowTime_1.31.0.tgz vignettes: vignettes/flowTime/inst/doc/gating-vignette.html, vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R, vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 34 Package: flowTrans Version: 1.59.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 22ca6fd59a4043a981a23cb2654a9379 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: devel git_last_commit: 0b7c905 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowTrans_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowTrans_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowTrans_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowTrans_1.59.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 35 Package: flowViz Version: 1.71.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat License: Artistic-2.0 MD5sum: ab8abd5113b81f5b3fa613108d1aac8b NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: devel git_last_commit: c55bec5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowViz_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowViz_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowViz_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowViz_1.71.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 31 Package: flowVS Version: 1.39.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 Archs: x64 MD5sum: bf5414dd3af2812e81c55dc17431a53f NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: devel git_last_commit: 0702a66 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowVS_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowVS_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowVS_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowVS_1.39.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 102 Package: flowWorkspace Version: 4.19.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.13.1), XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, scales(>= 1.3.0), matrixStats, RProtoBufLib, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: AGPL-3.0-only License_restricts_use: no MD5sum: 766c3c1d4af2ec58df4e8f0d10ffc61a NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: devel git_last_commit: 586e9e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/flowWorkspace_4.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/flowWorkspace_4.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/flowWorkspace_4.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/flowWorkspace_4.19.0.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: flowGate, ggcyto, highthroughputassays importsMe: CytoML, flowStats, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM linksToMe: CytoML dependencyCount: 66 Package: fmcsR Version: 1.49.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown, codetools License: Artistic-2.0 MD5sum: 570a38181982605e9ce2d04bb7a15496 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: devel git_last_commit: aacfd16 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fmcsR_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fmcsR_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fmcsR_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fmcsR_1.49.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: chemodiv suggestsMe: ChemmineR, xnet dependencyCount: 79 Package: fmrs Version: 1.17.0 Depends: R (>= 4.3.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 MD5sum: 44e030a991f8b261b65a9abd563ab28d NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Models Description: The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: devel git_last_commit: 6030d8e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/fmrs_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/fmrs_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fmrs_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fmrs_1.17.0.tgz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R importsMe: HhP dependencyCount: 10 Package: fobitools Version: 1.15.1 Depends: R (>= 4.1) Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr, ontologyIndex, purrr, RecordLinkage, stringr, textclean, tictoc, tidygraph, tidyr, vroom Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr, metabolomicsWorkbenchR, POMA, rmarkdown, rvest, SummarizedExperiment, testthat (>= 2.3.2), tidyverse License: GPL-3 MD5sum: d2b13350066900f6f6646133ae5a9f66 NeedsCompilation: no Title: Tools for Manipulating the FOBI Ontology Description: A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data. biocViews: MassSpectrometry, Metabolomics, Software, Visualization, BiomedicalInformatics, GraphAndNetwork, Annotation, Cheminformatics, Pathways, GeneSetEnrichment Author: Pol Castellano-Escuder [aut, cre] (ORCID: ), Alex Sánchez-Pla [aut] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/fobitools/ VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/fobitools/issues git_url: https://git.bioconductor.org/packages/fobitools git_branch: devel git_last_commit: fc658a6 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-29 source.ver: src/contrib/fobitools_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/fobitools_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/fobitools_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/fobitools_1.15.1.tgz vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html, vignettes/fobitools/inst/doc/food_enrichment_analysis.html, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html vignetteTitles: Dietary text annotation, Simple food ORA, Use case ST000291, Use case ST000629 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R, vignettes/fobitools/inst/doc/food_enrichment_analysis.R, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R dependencyCount: 126 Package: FRASER Version: 2.3.0 Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, utils, VGAM LinkingTo: RcppArmadillo, Rcpp Suggests: magick, BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, rtracklayer, SGSeq, ggbio, biovizBase License: MIT + file LICENSE MD5sum: c4af61b252d78b8febe1bb202fee9fb9 NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre] (ORCID: ), Ines Scheller [aut] (ORCID: ), Karoline Lutz [ctb], Vicente Yepez [aut] (ORCID: ), Julien Gagneur [aut] (ORCID: ) Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/FRASER/issues git_url: https://git.bioconductor.org/packages/FRASER git_branch: devel git_last_commit: 1b4ac46 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FRASER_2.3.0.tar.gz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Events in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 186 Package: frenchFISH Version: 1.19.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 4b0aa958dc81d8afdb6942de979bf35c NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: devel git_last_commit: 4b9c985 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/frenchFISH_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/frenchFISH_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/frenchFISH_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/frenchFISH_1.19.0.tgz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 74 Package: FRGEpistasis Version: 1.43.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: e939af95744d1936771efd0b41c98493 NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: devel git_last_commit: 4b9608b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FRGEpistasis_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FRGEpistasis_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FRGEpistasis_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FRGEpistasis_1.43.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 55 Package: frma Version: 1.59.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) Archs: x64 MD5sum: bd9965e9b5f1d422f4620cf2677a002d NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: devel git_last_commit: 93b1951 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/frma_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/frma_1.59.0.zip vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, ath1121501frmavecs, antiProfilesData dependencyCount: 67 Package: frmaTools Version: 1.59.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) Archs: x64 MD5sum: 66320a23dca0b8dea82d0c62c9dc7783 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: devel git_last_commit: 3414630 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/frmaTools_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/frmaTools_1.59.0.zip vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 14 Package: funOmics Version: 1.1.0 Depends: R (>= 4.4.0), NMF Imports: NMF, pathifier, stats, KEGGREST, AnnotationDbi, org.Hs.eg.db, dplyr, stringr Suggests: knitr, rmarkdown, testthat (>= 3.0.0), MultiAssayExperiment, SummarizedExperiment, airway License: MIT + file LICENSE Archs: x64 MD5sum: 27e7da30ae165ca5272166bd8573f58d NeedsCompilation: no Title: Aggregating Omics Data into Higher-Level Functional Representations Description: The 'funOmics' package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules! biocViews: Software, Transcriptomics, Metabolomics, Proteomics, Pathways, GO, KEGG Author: Elisa Gomez de Lope [aut, cre] (ORCID: ), Enrico Glaab [ctb] (ORCID: ) Maintainer: Elisa Gomez de Lope URL: https://github.com/elisagdelope/funomics VignetteBuilder: knitr BugReports: https://github.com/elisagdelope/funomics/issues git_url: https://git.bioconductor.org/packages/funOmics git_branch: devel git_last_commit: f088c60 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/funOmics_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/funOmics_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/funOmics_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/funOmics_1.1.0.tgz vignettes: vignettes/funOmics/inst/doc/funomics_vignette.html vignetteTitles: funOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/funOmics/inst/doc/funomics_vignette.R dependencyCount: 93 Package: funtooNorm Version: 1.31.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 MD5sum: f2ce3720f6da37be24985d0077aa07d9 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: devel git_last_commit: 4e274a7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/funtooNorm_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/funtooNorm_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/funtooNorm_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/funtooNorm_1.31.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 148 Package: FuseSOM Version: 1.9.0 Depends: R (>= 4.2.0) Imports: psych, FCPS, analogue, coop, pheatmap, ggplotify, fastcluster, fpc, ggplot2, stringr, ggpubr, proxy, cluster, diptest, methods, SummarizedExperiment, stats, S4Vectors LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: b3c0a643a6c5d34eb4134f0c04744a09 NeedsCompilation: yes Title: A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets Description: A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays. biocViews: SingleCell, CellBasedAssays, Clustering, Spatial Author: Elijah Willie [aut, cre] Maintainer: Elijah Willie VignetteBuilder: knitr BugReports: https://github.com/ecool50/FuseSOM/issues git_url: https://git.bioconductor.org/packages/FuseSOM git_branch: devel git_last_commit: 5ce2297 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/FuseSOM_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/FuseSOM_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/FuseSOM_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/FuseSOM_1.9.0.tgz vignettes: vignettes/FuseSOM/inst/doc/Introduction.html vignetteTitles: FuseSOM package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FuseSOM/inst/doc/Introduction.R suggestsMe: spicyWorkflow dependencyCount: 138 Package: G4SNVHunter Version: 0.99.4 Depends: R (>= 4.3.0) Imports: Biostrings, stats, GenomicRanges, IRanges, Rcpp, RcppRoll, data.table, GenomeInfoDb, S4Vectors, ggplot2, cowplot, progress, ggseqlogo, viridis, ggpointdensity LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, BiocManager, BSgenome.Hsapiens.UCSC.hg19, DT, rtracklayer, testthat (>= 3.0.0), RBGL License: MIT + file LICENSE MD5sum: 19219bfbb767c723400420bf5bfa6d7d NeedsCompilation: yes Title: Evaluating SNV-Induced Disruption of G-Quadruplex Structures Description: G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming potential and accurately screen user-provided single nucleotide variants (also applicable to single nucleotide polymorphisms) that may destabilize these structures. This enables users to screen key variants for further experimental study, investigating how these variants may influence biological functions, such as gene regulation, by altering G4 formation. biocViews: Epigenetics, SNP Author: Rongxin Zhang [cre, aut] (ORCID: ) Maintainer: Rongxin Zhang URL: https://github.com/rongxinzh/G4SNVHunter VignetteBuilder: knitr BugReports: https://github.com/rongxinzh/G4SNVHunter/issues git_url: https://git.bioconductor.org/packages/G4SNVHunter git_branch: devel git_last_commit: 73edf5e git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-12 source.ver: src/contrib/G4SNVHunter_0.99.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/G4SNVHunter_0.99.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/G4SNVHunter_0.99.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/G4SNVHunter_0.99.4.tgz vignettes: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.html vignetteTitles: Introduction to G4SNVHunter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.R dependencyCount: 67 Package: GA4GHclient Version: 1.31.0 Depends: R (>= 3.5.0), S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: cac55083f03f2e232bdb4215ce11baa6 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: devel git_last_commit: 7451330 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GA4GHclient_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GA4GHclient_1.31.0.zip vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 88 Package: GA4GHshiny Version: 1.29.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 2ed68886abf2042d1c3692dc8aa81f2f NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: devel git_last_commit: 145a45d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GA4GHshiny_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GA4GHshiny_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GA4GHshiny_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GA4GHshiny_1.29.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 124 Package: gaga Version: 2.53.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: 5df9179b409dd05e473add3eba7afb0d NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: devel git_last_commit: 71fadde git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gaga_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gaga_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gaga_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gaga_2.53.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 17 Package: gage Version: 2.57.0 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 93290fa7281f2064318a5f6d6b6ec35f NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: devel git_last_commit: 9f732fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gage_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gage_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gage_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gage_2.57.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 48 Package: GAprediction Version: 1.33.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 918003f283068c5e67a7988827538a47 NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: devel git_last_commit: e77d435 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GAprediction_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GAprediction_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GAprediction_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GAprediction_1.33.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 17 Package: garfield Version: 1.35.0 Suggests: knitr License: GPL-3 Archs: x64 MD5sum: 8dc5320e1c3b24a97960d8339671429d NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: devel git_last_commit: ff65122 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/garfield_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/garfield_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/garfield_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/garfield_1.35.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.27.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 194a80609ef8afa4b82a532611438178 NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: devel git_last_commit: d0acef7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GARS_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GARS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GARS_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GARS_1.27.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 269 Package: GateFinder Version: 1.27.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 24fcd82c35d0c4d05fecdd7114163b3f NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: devel git_last_commit: 77959e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GateFinder_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GateFinder_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GateFinder_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GateFinder_1.27.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 40 Package: gatom Version: 1.5.0 Depends: R (>= 4.3.0) Imports: data.table, igraph, BioNet, plyr, methods, XML, sna, intergraph, network, GGally, grid, ggplot2, mwcsr, pryr, htmlwidgets, htmltools, shinyCyJS (>= 1.0.0) Suggests: testthat, knitr, rmarkdown, KEGGREST, AnnotationDbi, org.Mm.eg.db, reactome.db, fgsea, readr, BiocStyle, R.utils License: MIT + file LICENCE Archs: x64 MD5sum: fc6aedcba23e0bf7010f028ab4402810 NeedsCompilation: no Title: Finding an Active Metabolic Module in Atom Transition Network Description: This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization. biocViews: GeneExpression, DifferentialExpression, Pathways, Network Author: Anastasiia Gainullina [aut], Mariia Emelianova [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/gatom/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/gatom/issues git_url: https://git.bioconductor.org/packages/gatom git_branch: devel git_last_commit: 97bd9cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gatom_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gatom_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gatom_1.5.0.tgz vignettes: vignettes/gatom/inst/doc/gatom-tutorial.html vignetteTitles: Using gatom package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gatom/inst/doc/gatom-tutorial.R dependencyCount: 119 Package: GBScleanR Version: 2.1.1 Depends: SeqArray Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp, RcppParallel, gdsfmt LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 8cfad29276871ffe3a66dfc82e383b28 NeedsCompilation: yes Title: Error correction tool for noisy genotyping by sequencing (GBS) data Description: GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel, Sequencing, QualityControl Author: Tomoyuki Furuta [aut, cre] (ORCID: ) Maintainer: Tomoyuki Furuta URL: https://github.com/tomoyukif/GBScleanR SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/tomoyukif/GBScleanR/issues git_url: https://git.bioconductor.org/packages/GBScleanR git_branch: devel git_last_commit: 56e85ca git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/GBScleanR_2.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/GBScleanR_2.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GBScleanR_2.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GBScleanR_2.1.1.tgz vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html vignetteTitles: BasicUsageOfGBScleanR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R dependencyCount: 69 Package: gcapc Version: 1.31.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 MD5sum: 5b0b88ac974944f87451269bbac643bf NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: devel git_last_commit: 35dd473 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gcapc_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcapc_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcapc_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcapc_1.31.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R suggestsMe: epigraHMM dependencyCount: 62 Package: gcatest Version: 2.7.0 Depends: R (>= 4.0) Imports: methods, lfa Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) Archs: x64 MD5sum: a2d99c00c123b21999aba948039b5ef6 NeedsCompilation: no Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: devel git_last_commit: 98df64e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gcatest_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcatest_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcatest_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcatest_2.7.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R suggestsMe: jackstraw dependencyCount: 13 Package: gCrisprTools Version: 2.13.0 Depends: R (>= 4.1) Imports: Biobase, limma, RobustRankAggreg, ggplot2, SummarizedExperiment, grid, rmarkdown, grDevices, graphics, methods, ComplexHeatmap, stats, utils, parallel Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea License: Artistic-2.0 MD5sum: 63e6b3d7cfdcad01c1d7370b76ee3b8d NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: devel git_last_commit: 1bb7f87 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gCrisprTools_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gCrisprTools_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gCrisprTools_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gCrisprTools_2.13.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Contrast_Comparisons_gCrisprTools, Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 99 Package: gcrma Version: 2.79.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: b9ec3e607db211efa9ff3efd57092a60 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: devel git_last_commit: 7375785 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gcrma_2.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gcrma_2.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gcrma_2.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gcrma_2.79.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 32 Package: GDCRNATools Version: 1.27.0 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat, prettydoc, rmarkdown License: Artistic-2.0 MD5sum: 329645369f7a6e679006263f8d98c63f NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: devel git_last_commit: e6c5e2e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GDCRNATools_1.27.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 228 Package: gDNAx Version: 1.5.0 Depends: R (>= 4.3) Imports: methods, BiocGenerics, BiocParallel, matrixStats, Biostrings, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, GenomicFiles, GenomicAlignments, GenomicFeatures, Rsamtools, AnnotationHub, RColorBrewer, AnnotationDbi, bitops, plotrix, SummarizedExperiment, grDevices, graphics, stats, utils, cli Suggests: BiocStyle, knitr, rmarkdown, RUnit, TxDb.Hsapiens.UCSC.hg38.knownGene, gDNAinRNAseqData License: Artistic-2.0 MD5sum: 39829b1ee3a75485c9ec4aecf97d2324 NeedsCompilation: no Title: Diagnostics for assessing genomic DNA contamination in RNA-seq data Description: Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics, SplicedAlignment, Alignment Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/functionalgenomics/gDNAx VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/gDNAx/issues git_url: https://git.bioconductor.org/packages/gDNAx git_branch: devel git_last_commit: 7191379 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gDNAx_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDNAx_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDNAx_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDNAx_1.5.0.tgz vignettes: vignettes/gDNAx/inst/doc/gDNAx.html vignetteTitles: The gDNAx package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDNAx/inst/doc/gDNAx.R dependencyCount: 102 Package: gDR Version: 1.5.1 Depends: R (>= 4.2), gDRcore (>= 1.1.19), gDRimport (>= 1.1.9), gDRutils (>= 1.1.12) Suggests: BiocStyle, BumpyMatrix, futile.logger, gDRstyle (>= 1.1.5), gDRtestData (>= 1.1.10), kableExtra, knitr, markdown, purrr, rmarkdown, SummarizedExperiment, testthat, yaml License: Artistic-2.0 Archs: x64 MD5sum: 22a6fbb1edd5b8b95cfbf82808a30f84 NeedsCompilation: no Title: Umbrella package for R packages in the gDR suite Description: Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers. biocViews: Software, DataImport, ShinyApps Author: Allison Vuong [aut], Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDR, https://gdrplatform.github.io/gDR/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDR/issues git_url: https://git.bioconductor.org/packages/gDR git_branch: devel git_last_commit: 8e0f13d git_last_commit_date: 2024-11-06 Date/Publication: 2024-12-18 source.ver: src/contrib/gDR_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDR_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDR_1.5.1.tgz vignettes: vignettes/gDR/inst/doc/gDR.html vignetteTitles: Running the drug response processing pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDR/inst/doc/gDR.R dependencyCount: 210 Package: gDRcore Version: 1.5.3 Depends: R (>= 4.2) Imports: BumpyMatrix, BiocParallel, checkmate, futile.logger, gDRutils (>= 1.3.9), MultiAssayExperiment, purrr, stringr, S4Vectors, SummarizedExperiment, data.table Suggests: BiocStyle, gDRstyle (>= 1.1.5), gDRimport (>= 1.1.9), gDRtestData (>= 1.1.10), IRanges, knitr, pkgbuild, qs, testthat, yaml License: Artistic-2.0 MD5sum: 1da5c6430cf1d3fc699da1efebefe3ad NeedsCompilation: yes Title: Processing functions and interface to process and analyze drug dose-response data Description: This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way. biocViews: Software, ShinyApps Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRcore, https://gdrplatform.github.io/gDRcore/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRcore/issues git_url: https://git.bioconductor.org/packages/gDRcore git_branch: devel git_last_commit: a08b10a git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/gDRcore_1.5.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRcore_1.5.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRcore_1.5.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRcore_1.5.3.tgz vignettes: vignettes/gDRcore/inst/doc/gDR-annotation.html, vignettes/gDRcore/inst/doc/gDR-data-model.html, vignettes/gDRcore/inst/doc/gDRcore.html vignetteTitles: gDRcore, Vignette Title, gDRcore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRcore/inst/doc/gDR-annotation.R, vignettes/gDRcore/inst/doc/gDR-data-model.R, vignettes/gDRcore/inst/doc/gDRcore.R dependsOnMe: gDR dependencyCount: 118 Package: gDRimport Version: 1.5.4 Depends: R (>= 4.2) Imports: assertthat, BumpyMatrix, checkmate, CoreGx, PharmacoGx, data.table, futile.logger, gDRutils (>= 1.3.17), magrittr, methods, MultiAssayExperiment, readxl, rio, S4Vectors, stats, stringi, SummarizedExperiment, tibble, tools, utils, XML, yaml, openxlsx Suggests: BiocStyle, gDRtestData (>= 1.3.3), gDRstyle (>= 1.3.3), knitr, purrr, qs, testthat License: Artistic-2.0 MD5sum: f62ac29a2a1ea8fcdd7e8832cf5e32ef NeedsCompilation: no Title: Package for handling the import of dose-response data Description: The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats. biocViews: Software, Infrastructure, DataImport Author: Arkadiusz Gladki [aut, cre] (ORCID: ), Bartosz Czech [aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Sergiu Mocanu [aut], Dariusz Scigocki [aut], Allison Vuong [aut], Luca Gerosa [aut] (ORCID: ), Janina Smola [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRimport, https://gdrplatform.github.io/gDRimport/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRimport/issues git_url: https://git.bioconductor.org/packages/gDRimport git_branch: devel git_last_commit: eb06334 git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-10 source.ver: src/contrib/gDRimport_1.5.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRimport_1.5.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRimport_1.5.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRimport_1.5.4.tgz vignettes: vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.html, vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.html, vignettes/gDRimport/inst/doc/gDRimport.html vignetteTitles: Converting a gDR-generated MultiAssayExperiment object into a PharmacoSet, Converting PharmacoSet Drug Response Data into gDR object, gDRimport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.R, vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.R, vignettes/gDRimport/inst/doc/gDRimport.R dependsOnMe: gDR suggestsMe: gDRcore dependencyCount: 208 Package: gDRstyle Version: 1.5.1 Depends: R (>= 4.2) Imports: BiocCheck, BiocManager, checkmate, desc, git2r, lintr (>= 3.0.0), rcmdcheck, remotes, yaml, rjson, pkgbuild, withr Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: cf279af02844745a4e6211f5e1b4895c NeedsCompilation: no Title: A package with style requirements for the gDR suite Description: Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met. biocViews: Software, Infrastructure Author: Allison Vuong [aut], Dariusz Scigocki [aut], Marcin Kamianowski [aut], Aleksander Chlebowski [ctb], Janina Smola [aut], Arkadiusz Gladki [cre, aut] (ORCID: ), Bartosz Czech [aut] (ORCID: ) Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRstyle, https://gdrplatform.github.io/gDRstyle/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRstyle/issues git_url: https://git.bioconductor.org/packages/gDRstyle git_branch: devel git_last_commit: 21e6fb0 git_last_commit_date: 2024-11-06 Date/Publication: 2024-11-06 source.ver: src/contrib/gDRstyle_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRstyle_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRstyle_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRstyle_1.5.1.tgz vignettes: vignettes/gDRstyle/inst/doc/gDRstyle.html, vignettes/gDRstyle/inst/doc/style_guide.html vignetteTitles: gDRstyle-package, gDR-style-guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRstyle/inst/doc/gDRstyle.R, vignettes/gDRstyle/inst/doc/style_guide.R suggestsMe: gDR, gDRcore, gDRimport, gDRutils, gDRtestData dependencyCount: 95 Package: gDRutils Version: 1.5.5 Depends: R (>= 4.2) Imports: BiocParallel, BumpyMatrix, checkmate, data.table, drc, jsonlite, jsonvalidate, methods, MultiAssayExperiment, S4Vectors, stats, stringr, SummarizedExperiment Suggests: BiocManager, BiocStyle, futile.logger, gDRstyle (>= 1.1.5), gDRtestData (>= 1.1.10), IRanges, knitr, lintr, purrr, qs, rcmdcheck, rmarkdown, scales, testthat, tools, yaml License: Artistic-2.0 Archs: x64 MD5sum: 62892d0c4db02871ebf86da2f61b54bc NeedsCompilation: no Title: A package with helper functions for processing drug response data Description: This package contains utility functions used throughout the gDR platform to fit data, manipulate data, and convert and validate data structures. This package also has the necessary default constants for gDR platform. Many of the functions are utilized by the gDRcore package. biocViews: Software, Infrastructure Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Aleksander Chlebowski [aut], Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRutils, https://gdrplatform.github.io/gDRutils/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRutils/issues git_url: https://git.bioconductor.org/packages/gDRutils git_branch: devel git_last_commit: 10a0dcc git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/gDRutils_1.5.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/gDRutils_1.5.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gDRutils_1.5.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gDRutils_1.5.5.tgz vignettes: vignettes/gDRutils/inst/doc/gDRutils.html vignetteTitles: gDRutils hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDRutils/inst/doc/gDRutils.R dependsOnMe: gDR importsMe: gDRcore, gDRimport dependencyCount: 117 Package: GDSArray Version: 1.27.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: f2472b1c547290281b8a6a194cc8ca21 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: devel git_last_commit: ec35b07 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GDSArray_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GDSArray_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GDSArray_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GDSArray_1.27.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R importsMe: CNVRanger, VariantExperiment suggestsMe: DelayedDataFrame dependencyCount: 40 Package: gdsfmt Version: 1.43.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: 6870cb1e23bde011417fe463e11331ee NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: devel git_last_commit: 84cf146 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gdsfmt_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gdsfmt_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gdsfmt_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gdsfmt_1.43.0.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, RAIDS, SAIGEgds, SCArray, SeqArray, SNPRelate, Mega2R importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools, SCArray.sat, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GeDi Version: 1.3.0 Depends: R (>= 4.4.0) Imports: GOSemSim, Matrix, shiny, shinyWidgets, bs4Dash, rintrojs, utils, DT, dplyr, shinyBS, STRINGdb, igraph, visNetwork, shinycssloaders, fontawesome, grDevices, parallel, stats, ggplot2, plotly, GeneTonic, RColorBrewer, scales, readxl, ggdendro, ComplexHeatmap, BiocNeighbors, tm, wordcloud2, tools, BiocParallel, BiocFileCache, cluster, circlize Suggests: knitr, rmarkdown, testthat (>= 3.0.0), DESeq2, htmltools, pcaExplorer, AnnotationDbi, macrophage, topGO, biomaRt, ReactomePA, clusterProfiler, BiocStyle, org.Hs.eg.db License: MIT + file LICENSE Archs: x64 MD5sum: 5cc01f63020533b4e6eccee1d88b7a2f NeedsCompilation: no Title: Defining and visualizing the distances between different genesets Description: The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage. biocViews: GUI, GeneSetEnrichment, Software, Transcription, RNASeq, Visualization, Clustering, Pathways, ReportWriting, GO, KEGG, Reactome, ShinyApps Author: Annekathrin Nedwed [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Annekathrin Nedwed URL: https://github.com/AnnekathrinSilvia/GeDi VignetteBuilder: knitr BugReports: https://github.com/AnnekathrinSilvia/GeDi/issues git_url: https://git.bioconductor.org/packages/GeDi git_branch: devel git_last_commit: 8157757 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeDi_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeDi_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeDi_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeDi_1.3.0.tgz vignettes: vignettes/GeDi/inst/doc/GeDi_manual.html vignetteTitles: The GeDi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeDi/inst/doc/GeDi_manual.R dependencyCount: 243 Package: GEM Version: 1.33.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 MD5sum: f27b9a0b5fd77003d0af5e4943a957ae NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: devel git_last_commit: 8a904e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GEM_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEM_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEM_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEM_1.33.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 36 Package: gemini Version: 1.21.0 Depends: R (>= 4.1.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 330252ffa777c92b6e0295d5d3df544a NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: devel git_last_commit: 22ea9ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gemini_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gemini_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gemini_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gemini_1.21.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 84 Package: gemma.R Version: 3.3.0 Imports: magrittr, glue, memoise, jsonlite, data.table, rlang, lubridate, utils, stringr, SummarizedExperiment, Biobase, tibble, tidyr, S4Vectors, httr, rappdirs, bit64, assertthat, digest, R.utils, base64enc Suggests: testthat (>= 2.0.0), rmarkdown, knitr, dplyr, covr, ggplot2, ggrepel, BiocStyle, microbenchmark, magick, purrr, pheatmap, viridis, poolr, kableExtra, listviewer, shiny License: Apache License (>= 2) MD5sum: c86375579111b2bf5767268472222a66 NeedsCompilation: no Title: A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses Description: Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles. biocViews: Software, DataImport, Microarray, SingleCell, ThirdPartyClient, DifferentialExpression, GeneExpression, Bayesian, Annotation, ExperimentalDesign, Normalization, BatchEffect, Preprocessing Author: Javier Castillo-Arnemann [aut] (ORCID: ), Jordan Sicherman [aut] (ORCID: ), Ogan Mancarci [cre, aut] (ORCID: ), Guillaume Poirier-Morency [aut] (ORCID: ) Maintainer: Ogan Mancarci URL: https://pavlidislab.github.io/gemma.R/, https://github.com/PavlidisLab/gemma.R VignetteBuilder: knitr BugReports: https://github.com/PavlidisLab/gemma.R/issues git_url: https://git.bioconductor.org/packages/gemma.R git_branch: devel git_last_commit: 04df853 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gemma.R_3.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gemma.R_3.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gemma.R_3.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gemma.R_3.3.0.tgz vignettes: vignettes/gemma.R/inst/doc/gemma.R.html, vignettes/gemma.R/inst/doc/metadata.html, vignettes/gemma.R/inst/doc/metanalysis.html vignetteTitles: Accessing curated gene expression data with gemma.R, A guide to metadata for samples and differential expression analyses, A meta analysis on effects of Parkinson's Disease using Gemma.R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gemma.R/inst/doc/gemma.R.R, vignettes/gemma.R/inst/doc/metadata.R, vignettes/gemma.R/inst/doc/metanalysis.R dependencyCount: 71 Package: genArise Version: 1.83.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 453cbd6a5b9e5f1411844da5d3eb2bca NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: devel git_last_commit: 26bc1fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genArise_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genArise_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genArise_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genArise_1.83.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: geneAttribution Version: 1.33.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: f1ea9e6c0bf1546579c01e0dfcdf3fa1 NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: devel git_last_commit: feca5bb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneAttribution_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneAttribution_1.33.0.zip vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 79 Package: GeneBreak Version: 1.37.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: ed08e1367fe12a67c536532cae31a8c1 NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: devel git_last_commit: 3793a7c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneBreak_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneBreak_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneBreak_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneBreak_1.37.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 60 Package: geneClassifiers Version: 1.31.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: b3f5b0f806bf0467c2240e66284e538c NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] (ORCID: ) Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: devel git_last_commit: ef8632b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneClassifiers_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneClassifiers_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneClassifiers_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneClassifiers_1.31.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 7 Package: GeneExpressionSignature Version: 1.53.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: adb44f8d0655c4d19e0c2163172313da NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb] Maintainer: Yang Cao URL: https://github.com/yiluheihei/GeneExpressionSignature VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/GeneExpressionSignature/issues/ git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: devel git_last_commit: fe30f47 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneExpressionSignature_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneExpressionSignature_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneExpressionSignature_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneExpressionSignature_1.53.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 7 Package: genefilter Version: 1.89.0 Imports: MatrixGenerics (>= 1.11.1), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: c0a7aa5a34dd32fd0e5fd88261c2d009 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman [aut], Vincent J. Carey [aut], Wolfgang Huber [aut], Florian Hahne [aut], Emmanuel Taiwo [ctb] ('howtogenefinder' vignette translation from Sweave to RMarkdown / HTML.), Khadijah Amusat [ctb] (Converted genefilter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: devel git_last_commit: 46934e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genefilter_1.89.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genefilter_1.89.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genefilter_1.89.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genefilter_1.89.0.tgz vignettes: vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/howtogenefilter.html, vignettes/genefilter/inst/doc/howtogenefinder.html vignetteTitles: 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Using the genefilter function to filter genes from a microarray, How to find genes whose expression profile is similar to that of specified genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: CNTools, GeneMeta, sva, maEndToEnd, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, broadSeq, Category, cbaf, ClassifyR, countsimQC, covRNA, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, protGear, SGCP, spatialHeatmap, SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, CoNI, dGAselID, INCATome, MiDA, netgsa suggestsMe: annotate, BioNet, categoryCompare, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array, logicFS, lumi, MMUPHin, npGSEA, oligo, pvac, qpgraph, rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedOvarianData, estrogen, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, oncoPredict, SuperLearner dependencyCount: 56 Package: genefu Version: 2.39.1 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils, iC10TrainingData Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 MD5sum: 084b70ba6d34ccabc01decb6414e8403 NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut], Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker [aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles [ctb], Jermiah Joseph [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: devel git_last_commit: 1ef27ec git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/genefu_2.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/genefu_2.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genefu_2.39.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genefu_2.39.1.tgz vignettes: vignettes/genefu/inst/doc/genefu.html vignetteTitles: genefu: A Package For Breast Cancer Gene Expression Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV, PDATK suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 113 Package: GeneGA Version: 1.57.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 4f83d6ab6ade79efb21d1570af95ec49 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: devel git_last_commit: 29ef0d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneGA_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneGA_1.57.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 17 Package: GeneGeneInteR Version: 1.33.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: ce52e64c41a3fb0331b7e4338cd27081 NeedsCompilation: yes Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. . biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut] Maintainer: Mathieu Emily git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: devel git_last_commit: bbad5d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneGeneInteR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneGeneInteR_1.33.0.zip vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R dependencyCount: 140 Package: GeneMeta Version: 1.79.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 533275774692525b0f6d8b69a0ea281e NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: devel git_last_commit: 7798817 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneMeta_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneMeta_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneMeta_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneMeta_1.79.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 57 Package: GeneNetworkBuilder Version: 1.49.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, RCy3, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, shiny, STRINGdb, BiocStyle, magick, rmarkdown, org.Hs.eg.db License: GPL (>= 2) MD5sum: edfd464618335f78360ca90e4c0064d9 NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: devel git_last_commit: 5385346 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneNetworkBuilder_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneNetworkBuilder_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneNetworkBuilder_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneNetworkBuilder_1.49.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R dependencyCount: 69 Package: GeneOverlap Version: 1.43.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 86d88b9f8468f7baff086509fac46253 NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: devel git_last_commit: 30085d3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneOverlap_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneOverlap_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneOverlap_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneOverlap_1.43.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.33.0 Depends: R (>= 4.0), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) Archs: x64 MD5sum: 2bc0bf20bcc0c9ad776c9a12a041e52a NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: devel git_last_commit: e60530e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneplast_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneplast_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneplast_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneplast_1.33.0.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary analysis of orthologous groups." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R importsMe: geneplast.data suggestsMe: TreeAndLeaf, geneplast.data dependencyCount: 24 Package: geneplotter Version: 1.85.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 5a71dc891376a76babff0a5497d96310 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: Robert Gentleman [aut], Rohit Satyam [ctb] (Converted geneplotter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplotter git_branch: devel git_last_commit: fac451a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneplotter_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneplotter_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneplotter_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneplotter_1.85.0.tgz vignettes: vignettes/geneplotter/inst/doc/visualize.pdf, vignettes/geneplotter/inst/doc/byChroms.html vignetteTitles: Visualization of Microarray Data, How to Assemble a chromLocation Object hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, maEndToEnd importsMe: biocGraph, MethylSeekR suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 52 Package: geneRecommender Version: 1.79.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) Archs: x64 MD5sum: ece1387300be55ebea823259bbdaffc7 NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: devel git_last_commit: 4ad7f25 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneRecommender_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneRecommender_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneRecommender_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneRecommender_1.79.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 7 Package: GeneRegionScan Version: 1.63.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 9e57cfac88df55e5aac12dde4186af4a NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: devel git_last_commit: 1cf7743 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneRegionScan_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneRegionScan_1.63.0.zip vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 29 Package: geneRxCluster Version: 1.43.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 95737029fb44b5c816de39f6de1660a0 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: devel git_last_commit: 9ad2c2b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneRxCluster_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneRxCluster_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geneRxCluster_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geneRxCluster_1.43.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 24 Package: GeneSelectMMD Version: 2.51.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) MD5sum: 62e4a9091a54608ce93ab740c6bb32b6 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: devel git_last_commit: 618717e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneSelectMMD_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneSelectMMD_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeneSelectMMD_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeneSelectMMD_2.51.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 11 Package: GENESIS Version: 2.37.0 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: dc45129f64991877763e2379f72474e7 NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: devel git_last_commit: dd2a91b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GENESIS_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GENESIS_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GENESIS_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GENESIS_2.37.0.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R dependsOnMe: RAIDS dependencyCount: 124 Package: GeneStructureTools Version: 1.27.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 2dc8927a7c3358f42c847d48cfedf8b6 NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: devel git_last_commit: 001986b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneStructureTools_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneStructureTools_1.27.0.zip vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 160 Package: geNetClassifier Version: 1.47.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: dc1abac4f7ed1509a9d4bc0a62024e3b NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: devel git_last_commit: fc4565d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geNetClassifier_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geNetClassifier_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geNetClassifier_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geNetClassifier_1.47.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 18 Package: GeneticsPed Version: 1.69.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: e6abe1c9f8c6713677b635d23d6ee3b8 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: devel git_last_commit: 48e7cab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeneticsPed_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeneticsPed_1.69.0.zip vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R dependencyCount: 11 Package: GeneTonic Version: 3.1.1 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, ComplexUpset, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2 (>= 3.5.0), ggrepel, ggridges, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, mosdef (>= 1.1.0), plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinyAce, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 4934de7c2b1229b82e62bf612ec49dd0 NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ), Annekathrin Ludt [aut] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues git_url: https://git.bioconductor.org/packages/GeneTonic git_branch: devel git_last_commit: fe808f5 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/GeneTonic_3.1.1.tar.gz vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html vignetteTitles: The GeneTonic User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R importsMe: GeDi suggestsMe: mosdef dependencyCount: 220 Package: geneXtendeR Version: 1.33.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) Archs: x64 MD5sum: 9626a7a4d239aa4c3dce8fd3864e7432 NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: devel git_last_commit: 05a7f4c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geneXtendeR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geneXtendeR_1.33.0.zip vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 117 Package: GENIE3 Version: 1.29.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: ba7b397291d20d31a10cabdf3aa7cf86 NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: devel git_last_commit: 709fdc0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GENIE3_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GENIE3_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GENIE3_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GENIE3_1.29.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, bulkAnalyseR dependencyCount: 27 Package: genoCN Version: 1.59.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: x64 MD5sum: e45dd1bb1825e2484e2d676a26dcd3a1 NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun git_url: https://git.bioconductor.org/packages/genoCN git_branch: devel git_last_commit: 32e05e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genoCN_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genoCN_1.59.0.zip vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R dependencyCount: 3 Package: genomation Version: 1.39.0 Depends: R (>= 3.5.0), grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 MD5sum: ddc73d57b46d70e9280b353d6d9c8fa5 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: devel git_last_commit: 9a99fa2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genomation_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomation_1.39.0.zip vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, EpiCompare, fCCAC, GenomicPlot, RCAS suggestsMe: methylKit dependencyCount: 107 Package: GenomAutomorphism Version: 1.9.0 Depends: R (>= 4.4.0), Imports: Biostrings, BiocGenerics, BiocParallel, GenomeInfoDb, GenomicRanges, IRanges, matrixStats, XVector, dplyr, data.table, parallel, doParallel, foreach, methods, S4Vectors, stats, numbers, utils Suggests: spelling, rmarkdown, BiocStyle, testthat (>= 3.0.0), knitr License: Artistic-2.0 MD5sum: 6375add4544342e94c2daa019db2719d NeedsCompilation: no Title: Compute the automorphisms between DNA's Abelian group representations Description: This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order. biocViews: MathematicalBiology, ComparativeGenomics, FunctionalGenomics, MultipleSequenceAlignment, WholeGenome Author: Robersy Sanchez [aut, cre] (ORCID: ) Maintainer: Robersy Sanchez URL: https://github.com/genomaths/GenomAutomorphism VignetteBuilder: knitr BugReports: https://github.com/genomaths/GenomAutomorphism/issues git_url: https://git.bioconductor.org/packages/GenomAutomorphism git_branch: devel git_last_commit: ee61199 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomAutomorphism_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomAutomorphism_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomAutomorphism_1.9.0.tgz vignettes: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.html vignetteTitles: Get started-with GenomAutomorphism hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.R dependencyCount: 57 Package: GenomeInfoDb Version: 1.43.2 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.45.2), IRanges (>= 2.41.1) Imports: stats, stats4, utils, UCSC.utils, GenomeInfoDbData Suggests: R.utils, data.table, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 08c11918e2a46803836f4fffbeaa9de4 NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora [aut], Martin Morgan [aut], Marc Carlson [aut], Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb], Atuhurira Kirabo Kakopo [ctb], Haleema Khan [ctb] (vignette translation from Sweave to Rmarkdown / HTML), Emmanuel Chigozie Elendu [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: devel git_last_commit: 3749662 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/GenomeInfoDb_1.43.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomeInfoDb_1.43.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomeInfoDb_1.43.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomeInfoDb_1.43.2.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.html vignetteTitles: GenomeInfoDb: Introduction to GenomeInfoDb, Submitting your organism to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: Biostrings, BSgenome, BSgenomeForge, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, HelloRanges, IdeoViz, Rsamtools, SCOPE, txdbmaker, VariantAnnotation, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, UCSCRepeatMasker, RTIGER importsMe: alabaster.ranges, AllelicImbalance, amplican, AneuFinder, AnnotationHubData, annotatr, ATACseqQC, ATACseqTFEA, atena, BaalChIP, ballgown, bambu, BindingSiteFinder, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr, cardelino, casper, cBioPortalData, CexoR, cfdnakit, cfDNAPro, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromVAR, circRNAprofiler, cleanUpdTSeq, CleanUpRNAseq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito, comapr, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, CrispRVariants, crisprViz, csaw, customProDB, DAMEfinder, Damsel, decompTumor2Sig, DegCre, demuxSNP, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, DMRcate, dmrseq, DominoEffect, easylift, easyRNASeq, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epigenomix, epigraHMM, EpiMix, epimutacions, epiregulon, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, extraChIPs, factR, FindIT2, FLAMES, FRASER, funtooNorm, G4SNVHunter, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, genomation, GenomAutomorphism, genomeIntervals, GenomicDistributions, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, GenVisR, geomeTriD, ggbio, gmoviz, goseq, GOTHiC, GRaNIE, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HicAggR, HiCBricks, HiCDOC, HiCExperiment, HiContacts, hicVennDiagram, HiTC, idr2d, IMAS, INSPEcT, InteractionSet, IVAS, karyoploteR, katdetectr, MADSEQ, mariner, maser, metagene2, metaseqR2, methimpute, methInheritSim, methylKit, methylPipe, methylSig, methylumi, minfi, MinimumDistance, mobileRNA, monaLisa, mosaics, Motif2Site, motifbreakR, motifmatchr, MotifPeeker, motifTestR, MouseFM, msgbsR, multicrispr, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, nullranges, OGRE, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, pipeFrame, plotgardener, plyinteractions, plyranges, podkat, pram, proActiv, profileplyr, ProteoDisco, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, raer, RaggedExperiment, RareVariantVis, RCAS, RcisTarget, recount, recoup, regioneR, REMP, Repitools, RESOLVE, rfPred, RgnTX, rGREAT, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scanMiR, scanMiRApp, scDblFinder, scmeth, scRNAseqApp, scruff, segmentSeq, SeqArray, seqCAT, seqsetvis, sesame, sevenC, SGSeq, ShortRead, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, spiky, SpliceWiz, SplicingGraphs, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, tadar, TAPseq, TCGAutils, TEKRABber, TENxIO, TFBSTools, tidyCoverage, TitanCNA, TnT, trackViewer, transcriptR, transmogR, tRNAscanImport, TVTB, tximeta, Ularcirc, UMI4Cats, VanillaICE, VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr, YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, chipenrich.data, GenomicDistributionsData, MethylSeqData, sesameData, TCGAWorkflow, crispRdesignR, driveR, GRIN2, ICAMS, locuszoomr, MAAPER, Mega2R, MicroSEC, MOCHA, SeedMatchR, Signac, simMP suggestsMe: AlphaMissenseR, AnnotationForge, AnnotationHub, DFplyr, DiffBind, epialleleR, ExperimentHubData, fishpond, ldblock, megadepth, methrix, OUTRIDER, parglms, QDNAseq, RAIDS, regioneReloaded, scTreeViz, splatter, systemPipeR, TFutils, UCSC.utils, BioMartGOGeneSets, xcoredata, seqpac, gkmSVM, polyRAD, Seurat dependencyCount: 20 Package: genomeIntervals Version: 1.63.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 Archs: x64 MD5sum: 77f0a207a62d086208aedf25d78d93b9 NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: devel git_last_commit: ebab913 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genomeIntervals_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomeIntervals_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomeIntervals_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomeIntervals_1.63.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe importsMe: easyRNASeq dependencyCount: 25 Package: genomes Version: 3.37.0 Depends: readr, curl License: GPL-3 Archs: x64 MD5sum: dae05ba81e818f034fac20dbcec8f55e NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: devel git_last_commit: 4bdaa9c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genomes_3.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomes_3.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomes_3.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomes_3.37.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 31 Package: GenomicAlignments Version: 1.43.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.55.3), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, knitr, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 2e03f7ccaecb69c76382bcac5d4e3849 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan [aut], Fedor Bezrukov [ctb], Robert Castelo [ctb], Halimat C. Atanda [ctb] (Translated 'WorkingWithAlignedNucleotides' vignette from Sweave to RMarkdown / HTML.) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicAlignments VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: devel git_last_commit: db308fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicAlignments_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicAlignments_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicAlignments_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicAlignments_1.43.0.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.html vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides (WORK-IN-PROGRESS!) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, HelloRanges, hiReadsProcessor, igvR, ORFik, recoup, RiboDiPA, ShortRead, SplicingGraphs, sequencing importsMe: AneuFinder, APAlyzer, ASpli, ATACseqQC, ATACseqTFEA, atena, BaalChIP, bambu, biovizBase, breakpointR, CAGEfightR, CAGEr, cfDNAPro, chimeraviz, ChIPpeakAnno, ChIPQC, CNEr, consensusDE, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, DNAfusion, DuplexDiscovereR, easyRNASeq, esATAC, extraChIPs, FLAMES, FRASER, gcapc, gDNAx, genomation, GenomicFiles, GenomicPlot, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, icetea, IMAS, INSPEcT, MADSEQ, MDTS, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, MotifPeeker, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, raer, ramwas, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, scPipe, scruff, seqsetvis, SGSeq, soGGi, spiky, strandCheckR, TAPseq, TCseq, trackViewer, transcriptR, Ularcirc, UMI4Cats, VaSP, VplotR, ZygosityPredictor, leeBamViews, alakazam, iimi, MAAPER, PACVr, VALERIE suggestsMe: amplican, BindingSiteFinder, BiocParallel, csaw, EpiCompare, ExperimentHub, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, GenomicTuples, igvShiny, IRanges, QuasR, Rsamtools, SARC, similaRpeak, Streamer, systemPipeR, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 51 Package: GenomicDataCommons Version: 1.31.0 Depends: R (>= 4.1.0) Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, tibble, tidyr Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools, R.utils, data.table License: Artistic-2.0 Archs: x64 MD5sum: 2614b49ae889fec0028d20c4d4318fa0 NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons, http://bioconductor.github.io/GenomicDataCommons/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: devel git_last_commit: b90f2c6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicDataCommons_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicDataCommons_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDataCommons_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicDataCommons_1.31.0.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons, Questions and answers from over the years, Somatic Mutation Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R importsMe: GDCRNATools, TCGAutils suggestsMe: autonomics dependencyCount: 56 Package: GenomicDistributions Version: 1.15.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, scales, broom, GenomeInfoDb, stats Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown, GenomicDistributionsData Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: a6a9291416dc08b35c7a31187da9240c NeedsCompilation: no Title: GenomicDistributions: fast analysis of genomic intervals with Bioconductor Description: If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another. biocViews: Software, GenomeAnnotation, GenomeAssembly, DataRepresentation, Sequencing, Coverage, FunctionalGenomics, Visualization Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [aut], John Lawson [aut], Jose Verdezoto [aut], Michal Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut] Maintainer: Kristyna Kupkova URL: http://code.databio.org/GenomicDistributions VignetteBuilder: knitr BugReports: http://github.com/databio/GenomicDistributions git_url: https://git.bioconductor.org/packages/GenomicDistributions git_branch: devel git_last_commit: 9720508 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicDistributions_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicDistributions_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDistributions_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicDistributions_1.15.0.tgz vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html, vignettes/GenomicDistributions/inst/doc/intro.html vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started with GenomicDistributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R dependencyCount: 69 Package: GenomicFeatures Version: 1.59.1 Depends: R (>= 3.5.0), BiocGenerics (>= 0.51.2), S4Vectors (>= 0.17.29), IRanges (>= 2.37.1), GenomeInfoDb (>= 1.35.8), GenomicRanges (>= 1.55.2), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, DBI, XVector, Biostrings, rtracklayer Suggests: txdbmaker, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, AnnotationFilter, RUnit, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: 7b12f556e66b1cc86f47c3b8fba885f5 NeedsCompilation: no Title: Query the gene models of a given organism/assembly Description: Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson [aut], H. Pagès [aut, cre], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], D. Sarkar [aut], M. Lawrence [aut], V. Obenchain [aut], S. Arora [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], P. Shannon [ctb], L. Shepherd [ctb], D. Tenenbaum [ctb], D. Van Twisk [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: devel git_last_commit: 2d2ddd6 git_last_commit_date: 2024-11-06 Date/Publication: 2024-11-07 source.ver: src/contrib/GenomicFeatures_1.59.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicFeatures_1.59.1.zip vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html vignetteTitles: Obtaining and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: Cogito, cpvSNP, ensembldb, GSReg, Guitar, HelloRanges, mygene, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, txdbmaker, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpli, atena, bambu, BgeeCall, BindingSiteFinder, biovizBase, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, consensusDE, crisprDesign, crisprViz, CSSQ, customProDB, Damsel, decompTumor2Sig, derfinder, derfinderPlot, DNAfusion, doubletrouble, EDASeq, ELMER, EpiMix, epimutacions, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, factR, FindIT2, FLAMES, FRASER, GA4GHshiny, gDNAx, geneAttribution, GenomicInteractionNodes, GenomicPlot, GenVisR, ggbio, gINTomics, gmapR, gmoviz, goseq, GUIDEseq, Gviz, gwascat, HiLDA, icetea, InPAS, INSPEcT, karyoploteR, lumi, magpie, mCSEA, metaseqR2, methylumi, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, OutSplice, proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, raer, RCAS, recoup, RgnTX, rGREAT, RiboCrypt, RiboProfiling, ribosomeProfilingQC, RITAN, RNAmodR, SARC, scanMiRApp, scruff, SGSeq, sitadela, spatzie, StructuralVariantAnnotation, svaNUMT, svaRetro, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, transmogR, TRESS, txcutr, tximeta, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis, DMRcatedata, geneLenDataBase, GenomicDistributionsData, scRNAseq, ExpHunterSuite, driveR, MAAPER, MOCHA, oncoPredict, SeedMatchR suggestsMe: BANDITS, biomvRCNS, Biostrings, BSgenomeForge, chipseq, chromPlot, CrispRVariants, csaw, cummeRbund, eisaR, fishpond, GenomeInfoDb, GenomicAlignments, GenomicRanges, HDF5Array, HiContacts, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, pageRank, plotgardener, recount, RNAmodR.ML, Rsamtools, rtracklayer, scPipe, ShortRead, SummarizedExperiment, systemPipeR, TFutils, tidyCoverage, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, BioPlex, curatedAdipoChIP, ObMiTi, parathyroidSE, Single.mTEC.Transcriptomes, systemPipeRdata, CAGEWorkflow, polyRAD dependencyCount: 77 Package: GenomicFiles Version: 1.43.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), MatrixGenerics, GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation (>= 1.27.9), GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats, knitr, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: cfea074c49b3ec19fc7ed49c6fa11d48 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut], Sonali Kumari [ctb] (Converted 'GenomicFiles' vignettes from Sweave to RMarkdown / HTML.) Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: devel git_last_commit: dce61b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicFiles_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicFiles_1.43.0.zip vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.html vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, derfinder, gDNAx, QuasR, Rqc, TFutils, VCFArray suggestsMe: ldblock, MungeSumstats dependencyCount: 80 Package: genomicInstability Version: 1.13.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: a36fa1245e429b6b8f227f21577537f0 NeedsCompilation: no Title: Genomic Instability estimation for scRNA-Seq Description: This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell. biocViews: SystemsBiology, GeneExpression, SingleCell Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth [cph] Maintainer: Mariano Alvarez URL: https://github.com/DarwinHealth/genomicInstability BugReports: https://github.com/DarwinHealth/genomicInstability git_url: https://git.bioconductor.org/packages/genomicInstability git_branch: devel git_last_commit: a8d0388 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/genomicInstability_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/genomicInstability_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/genomicInstability_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/genomicInstability_1.13.0.tgz vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf vignetteTitles: Using genomicInstability hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R dependencyCount: 103 Package: GenomicInteractionNodes Version: 1.11.0 Depends: R (>= 4.2.0), stats Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures, GenomeInfoDb, methods, IRanges, RBGL, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: file LICENSE Archs: x64 MD5sum: 60a4a61ea60ad5e946cd41a7762a53f5 NeedsCompilation: no Title: A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data Description: The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation. biocViews: HiC, Sequencing, Software Author: Jianhong Ou [aut, cre], Yarui Diao [fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GenomicInteractionNodes VignetteBuilder: knitr BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes git_branch: devel git_last_commit: 60276ee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicInteractionNodes_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicInteractionNodes_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicInteractionNodes_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicInteractionNodes_1.11.0.tgz vignettes: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html vignetteTitles: GenomicInteractionNodes Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R dependencyCount: 81 Package: GenomicInteractions Version: 1.41.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 72890628f2db2d7333bb6a9188d09fce NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: devel git_last_commit: c7d2904 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicInteractions_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicInteractions_1.41.0.zip vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, extraChIPs, spatzie, OHCA suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 159 Package: GenomicOZone Version: 1.21.0 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) MD5sum: 9719f701ee02bb9fefad6e649f8d086c NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: devel git_last_commit: 423a96e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicOZone_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicOZone_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicOZone_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicOZone_1.21.0.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 167 Package: GenomicPlot Version: 1.5.2 Depends: R (>= 4.4.0), GenomicRanges (>= 1.46.1) Imports: methods, Rsamtools, parallel, tidyr, rtracklayer (>= 1.54.0), plyranges (>= 1.14.0), cowplot (>= 1.1.1), VennDiagram, ggplotify, GenomeInfoDb, IRanges, ComplexHeatmap, RCAS (>= 1.20.0), scales (>= 1.2.0), GenomicAlignments (>= 1.30.0), edgeR, circlize, viridis, ggsignif (>= 0.6.3), ggsci (>= 2.9), ggpubr, grDevices, graphics, stats, utils, GenomicFeatures, genomation (>= 1.36.0), txdbmaker, ggplot2 (>= 3.3.5), BiocGenerics, dplyr, grid Suggests: knitr, rmarkdown, R.utils, Biobase, BiocStyle, testthat, AnnotationDbi License: GPL-2 MD5sum: b7d149ed51b9d1bde0fb21903b5e9948 NeedsCompilation: no Title: Plot profiles of next generation sequencing data in genomic features Description: Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well. biocViews: AlternativeSplicing, ChIPSeq, Coverage, GeneExpression, RNASeq, Sequencing, Software, Transcription, Visualization, Annotation Author: Shuye Pu [aut, cre] (ORCID: ) Maintainer: Shuye Pu URL: https://github.com/shuye2009/GenomicPlot VignetteBuilder: knitr BugReports: https://github.com/shuye2009/GenomicPlot/issues git_url: https://git.bioconductor.org/packages/GenomicPlot git_branch: devel git_last_commit: 2ccb4dc git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/GenomicPlot_1.5.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicPlot_1.5.2.zip vignettes: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.html vignetteTitles: GenomicPlot_vignettes.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.R dependencyCount: 210 Package: GenomicRanges Version: 1.59.1 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.45.2), IRanges (>= 2.41.1), GenomeInfoDb (>= 1.43.1) Imports: utils, stats, XVector (>= 0.29.2) LinkingTo: S4Vectors, IRanges Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, txdbmaker, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 15318a339da11d8cbaa476ae63137912 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: Patrick Aboyoun [aut], Hervé Pagès [aut, cre], Michael Lawrence [aut], Sonali Arora [ctb], Martin Morgan [ctb], Kayla Morrell [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Lori Shepherd [ctb], Dan Tenenbaum [ctb], Daniel van Twisk [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: devel git_last_commit: efa80fa git_last_commit_date: 2024-11-15 Date/Publication: 2024-11-15 source.ver: src/contrib/GenomicRanges_1.59.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicRanges_1.59.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicRanges_1.59.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicRanges_1.59.1.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: alabaster.ranges, AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, betaHMM, BindingSiteFinder, biomvRCNS, BiSeq, bnbc, BPRMeth, breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chromPlot, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, Cogito, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DNAshapeR, easylift, EnrichedHeatmap, ensembldb, epigenomix, esATAC, ExCluster, extraChIPs, fastseg, fCCAC, FindIT2, GeneBreak, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicPlot, GenomicScores, GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, HERON, hiAnnotator, HiCDOC, HiTC, IdeoViz, igvR, igvShiny, InTAD, intansv, InteractionSet, IWTomics, karyoploteR, m6Aboost, maser, MBASED, Melissa, metagene2, methimpute, methylKit, methylPipe, minfi, MotifDb, motifTestR, msgbsR, MutationalPatterns, NADfinder, oncoscanR, ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, recoup, regioneR, RepViz, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, SARC, Scale4C, SCOPE, segmentSeq, seqCAT, SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, spiky, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, tadar, TnT, trackViewer, transmogR, traseR, tRNA, tRNAdbImport, tRNAscanImport, 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HiCExperiment, HiContacts, HiCool, hicVennDiagram, HilbertCurve, HiLDA, hiReadsProcessor, hummingbird, icetea, ideal, idr2d, IMAS, iNETgrate, INSPEcT, ipdDb, isomiRs, IVAS, karyoploteR, katdetectr, knowYourCG, loci2path, LOLA, LoomExperiment, lumi, MADSEQ, magpie, mariner, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2, methInheritSim, MethReg, methrix, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, mitoClone2, MMDiff2, mobileRNA, Modstrings, monaLisa, Moonlight2R, mosaics, Motif2Site, motifbreakR, motifmatchr, MotifPeeker, MouseFM, MSA2dist, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, NanoMethViz, ncRNAtools, nearBynding, normr, nucleR, nullranges, OGRE, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, OutSplice, packFinder, pageRank, panelcn.mops, partCNV, PAST, pcaExplorer, pepStat, pgxRpi, PhIPData, PICS, PING, PIPETS, plotgardener, plyinteractions, pqsfinder, pram, preciseTAD, primirTSS, proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, raer, RAIDS, ramr, RareVariantVis, RCAS, RcisTarget, recount, recount3, regionalpcs, regioneR, regutools, REMP, Repitools, RESOLVE, rfPred, rGADEM, RgnTX, Rhisat2, RiboCrypt, RiboDiPA, RiboProfiling, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, RTCGAToolbox, scanMiR, scanMiRApp, scDblFinder, scmeth, scoreInvHap, scPipe, scRNAseqApp, scruff, scuttle, segmenter, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, snapcount, soGGi, SparseSignatures, spatzie, SpectralTAD, SpliceWiz, SplicingGraphs, strandCheckR, syntenet, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TDbasedUFE, TDbasedUFEadv, TENxIO, terraTCGAdata, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, tidyCoverage, TitanCNA, tLOH, tracktables, transcriptR, transite, TRESS, tricycle, triplex, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, UPDhmm, VariantFiltering, VaSP, VCFArray, wiggleplotr, xcore, XNAString, ZygosityPredictor, BioMartGOGeneSets, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, TENET.AnnotationHub, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, biscuiteerData, chipenrich.data, COSMIC.67, ELMER.data, fourDNData, GenomicDistributionsData, leeBamViews, mCSEAdata, MethylSeqData, pepDat, scMultiome, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, VariantToolsData, ExpHunterSuite, recountWorkflow, seqpac, TCGAWorkflow, cinaR, crispRdesignR, driveR, geneHapR, geno2proteo, GenoPop, hahmmr, hoardeR, ICAMS, karyotapR, locuszoomr, lolliplot, LoopRig, MAAPER, MitoHEAR, MOCHA, noisyr, numbat, oncoPredict, PACVr, RapidoPGS, revert, scPloidy, Signac, simMP, VALERIE suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, Chicago, ComplexHeatmap, cummeRbund, DFplyr, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, iSEE, lute, maftools, MiRaGE, MIRit, omicsPrint, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, shiny.gosling, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CTCF, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, chicane, DGEobj, gkmSVM, MARVEL, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, SNPassoc, updog, valr dependencyCount: 23 Package: GenomicScores Version: 2.19.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, VariantAnnotation, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard, BSgenome.Hsapiens.UCSC.hg38, phastCons100way.UCSC.hg38, MafDb.1Kgenomes.phase1.hs37d5, MafH5.gnomAD.v4.0.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: e1d4d64b94d1be0a1f950e2ceeeb6e31 NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage, AnnotationHubSoftware Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: devel git_last_commit: 51c4e65 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicScores_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicScores_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicScores_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicScores_2.19.0.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phastCons7way.UCSC.hg38, phyloP35way.UCSC.mm39 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 81 Package: GenomicSuperSignature Version: 1.15.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable, irlba Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis, BiocStyle, testthat, forcats, stats, wordcloud, circlize, EnrichmentBrowser, clusterProfiler, msigdbr, cluster, RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr, utils License: Artistic-2.0 MD5sum: 6d24d51cac0fbd8a135e53044f02d556 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package. biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq, Sequencing, Pathways, Clustering Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/GenomicSuperSignature VignetteBuilder: knitr BugReports: https://github.com/shbrief/GenomicSuperSignature/issues git_url: https://git.bioconductor.org/packages/GenomicSuperSignature git_branch: devel git_last_commit: 675b807 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenomicSuperSignature_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicSuperSignature_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicSuperSignature_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicSuperSignature_1.15.0.tgz vignettes: vignettes/GenomicSuperSignature/inst/doc/Contents.html, vignettes/GenomicSuperSignature/inst/doc/Quickstart.html vignetteTitles: Introduction on RAVmodel, Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicSuperSignature/inst/doc/Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 163 Package: GenomicTuples Version: 1.41.1 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, covr, GenomicAlignments, Biostrings License: Artistic-2.0 MD5sum: 0a3008d71c7f4f003dead5a1a8aae444 NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: devel git_last_commit: ff46eeb git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-13 source.ver: src/contrib/GenomicTuples_1.41.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenomicTuples_1.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenomicTuples_1.41.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GenomicTuples_1.41.1.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 26 Package: GenProSeq Version: 1.11.0 Depends: keras, mclust, R (>= 4.2) Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats Suggests: VAExprs, stringdist, knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: b885a9b2d5d2edfe10e03c117825d698 NeedsCompilation: no Title: Generating Protein Sequences with Deep Generative Models Description: Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model. biocViews: Software, Proteomics Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: devel git_last_commit: 45b803f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenProSeq_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GenProSeq_1.11.0.tgz vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html vignetteTitles: GenProSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R dependencyCount: 149 Package: GenVisR Version: 1.39.0 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 87f8f68a1e14b7c50d048ac388449224 NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: devel git_last_commit: cf208dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GenVisR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GenVisR_1.39.0.zip vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 124 Package: GeoDiff Version: 1.13.0 Depends: R (>= 4.1.0), Biobase Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods, graphics, stats, testthat, GeomxTools, NanoStringNCTools LinkingTo: Rcpp, RcppArmadillo, roptim Suggests: knitr, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: c38af61909fd4e14c12f89bf090acca6 NeedsCompilation: yes Title: Count model based differential expression and normalization on GeoMx RNA data Description: A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette. biocViews: GeneExpression, DifferentialExpression, Normalization Author: Nicole Ortogero [cre], Lei Yang [aut], Zhi Yang [aut] Maintainer: Nicole Ortogero URL: https://github.com/Nanostring-Biostats/GeoDiff VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/GeoDiff git_url: https://git.bioconductor.org/packages/GeoDiff git_branch: devel git_last_commit: e81e283 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeoDiff_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeoDiff_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeoDiff_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeoDiff_1.13.0.tgz vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html vignetteTitles: Workflow_WTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R dependencyCount: 150 Package: GEOexplorer Version: 1.13.0 Depends: shiny, limma, Biobase, plotly, enrichR, R (>= 4.1.0) Imports: DT, XML, httr, sva, xfun, edgeR, htmltools, factoextra, heatmaply, pheatmap, scales, shinyHeatmaply, shinybusy, ggplot2, stringr, umap, GEOquery, impute, grDevices, stats, graphics, markdown, knitr, utils, xml2, R.utils, readxl, shinycssloaders, car Suggests: rmarkdown, usethis, testthat (>= 3.0.0) License: GPL-3 MD5sum: d958e826cb3f82d45016c524578a280e NeedsCompilation: no Title: GEOexplorer: a webserver for gene expression analysis and visualisation Description: GEOexplorer is a webserver and R/Bioconductor package and web application that enables users to perform gene expression analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/). biocViews: Software, GeneExpression, mRNAMicroarray, DifferentialExpression, Microarray, MicroRNAArray, Transcriptomics, RNASeq Author: Guy Hunt [aut, cre] (ORCID: ), Rafael Henkin [ctb, ths] (ORCID: ), Alfredo Iacoangeli [ctb, ths] (ORCID: ), Fabrizio Smeraldi [ctb, ths] (ORCID: ), Michael Barnes [ctb, ths] (ORCID: ) Maintainer: Guy Hunt URL: https://github.com/guypwhunt/GEOexplorer/ VignetteBuilder: knitr BugReports: https://github.com/guypwhunt/GEOexplorer/issues git_url: https://git.bioconductor.org/packages/GEOexplorer git_branch: devel git_last_commit: 6e19a44 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GEOexplorer_1.13.0.tar.gz vignettes: vignettes/GEOexplorer/inst/doc/GEOexplorer.html vignetteTitles: GEOexplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOexplorer/inst/doc/GEOexplorer.R dependencyCount: 225 Package: GEOfastq Version: 1.15.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 44f73f06eeda9fba0e833cf40e82335c NeedsCompilation: no Title: Downloads ENA Fastqs With GEO Accessions Description: GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA. biocViews: RNASeq, DataImport Author: Alex Pickering [cre, aut] (ORCID: ) Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: devel git_last_commit: 7ac929a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/GEOfastq_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOfastq_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOfastq_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOfastq_1.15.0.tgz vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html vignetteTitles: Using the GEOfastq Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R dependencyCount: 38 Package: GEOmetadb Version: 1.69.2 Depends: R.utils,RSQLite Suggests: knitr, rmarkdown, dplyr, dbplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 2159a90b33aa4b133e4f3c8c8964331f NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: devel git_last_commit: 9a07aa7 git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-10 source.ver: src/contrib/GEOmetadb_1.69.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOmetadb_1.69.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOmetadb_1.69.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOmetadb_1.69.2.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R suggestsMe: antiProfilesData, maGUI dependencyCount: 23 Package: geomeTriD Version: 1.1.1 Depends: R (>= 4.4.0) Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, graphics, grDevices, grid, htmlwidgets, igraph, InteractionSet, IRanges, MASS, Matrix, methods, plotrix, rgl, rjson, S4Vectors, scales, stats, trackViewer Suggests: RUnit, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, manipulateWidget, shiny, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 58c3d7493478524ac14b39e28c6a522b NeedsCompilation: no Title: A R/Bioconductor package for interactive 3D plot of epigenetic data or single cell data Description: geomeTriD (Three Dimensional Geometry Package) create interactive 3D plots using the GL library with the 'three.js' visualization library (https://threejs.org) or the rgl library. In addition to creating interactive 3D plots, the application also generates simplified models in 2D. These 2D models provide a more straightforward visual representation, making it easier to analyze and interpret the data quickly. This functionality ensures that users have access to both detailed three-dimensional visualizations and more accessible two-dimensional views, catering to various analytical needs. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/geomeTriD VignetteBuilder: knitr BugReports: https://github.com/jianhong/geomeTriD/issues git_url: https://git.bioconductor.org/packages/geomeTriD git_branch: devel git_last_commit: 803e9b0 git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-12 source.ver: src/contrib/geomeTriD_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/geomeTriD_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geomeTriD_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geomeTriD_1.1.1.tgz vignettes: vignettes/geomeTriD/inst/doc/geomeTriD.html vignetteTitles: geomeTriD Vignette: Plot data in 3D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/geomeTriD/inst/doc/geomeTriD.R dependencyCount: 168 Package: GeomxTools Version: 3.11.0 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, lmerTest, dplyr, stringr, grDevices, graphics, GGally, rlang, ggplot2, SeuratObject Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph, Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork License: MIT Archs: x64 MD5sum: fee07998ac5baedd5b7d8afe3df55efe NeedsCompilation: no Title: NanoString GeoMx Tools Description: Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign, Normalization, Spatial Author: Maddy Griswold [cre, aut], Nicole Ortogero [aut], Zhi Yang [aut], Ronalyn Vitancol [aut], David Henderson [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: devel git_last_commit: d685805 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeomxTools_3.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeomxTools_3.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeomxTools_3.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeomxTools_3.11.0.tgz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet, Coercion of GeoMxSet to Seurat and SpatialExperiment Objects, Protein data using GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon, SpatialOmicsOverlay dependencyCount: 128 Package: GEOquery Version: 2.75.0 Depends: methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, rentrez, R.utils, stringr, SummarizedExperiment, S4Vectors, rvest, httr2 Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown License: MIT + file LICENSE MD5sum: c2fe537f5a4f392721ca06b02d72a3fb NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis [aut, cre] (ORCID: ) Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery, http://seandavi.github.io/GEOquery, http://seandavi.github.io/GEOquery/ VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: devel git_last_commit: 2a416df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GEOquery_2.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOquery_2.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOquery_2.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOquery_2.75.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, ChIPXpress, DExMA, EGAD, GEOexplorer, minfi, Moonlight2R, MoonlightR, phantasus, recount, BeadArrayUseCases, BioPlex, GSE13015, healthyControlsPresenceChecker, easyDifferentialGeneCoexpression, geneExpressionFromGEO, RCPA, seeker suggestsMe: AUCell, autonomics, COTAN, ctsGE, dearseq, debCAM, diffcoexp, dyebias, EpiDISH, EpiMix, fgsea, FLAMES, GeneExpressionSignature, GenomicOZone, GeoTcgaData, methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools, phantasusLite, quantiseqr, RegEnrich, RGSEA, Rnits, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, ath1121501frmavecs, airway, antiProfilesData, muscData, parathyroidSE, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, easybio, fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray dependencyCount: 78 Package: GEOsubmission Version: 1.59.0 Imports: affy, Biobase, utils License: GPL (>= 2) Archs: x64 MD5sum: 71681b7ff7748e885a7d2bf4526dca72 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: devel git_last_commit: d655fdc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GEOsubmission_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEOsubmission_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEOsubmission_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEOsubmission_1.59.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 13 Package: GeoTcgaData Version: 2.7.0 Depends: R (>= 4.2.0) Imports: utils, data.table, plyr, cqn, topconfects, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, DESeq2, S4Vectors, ChAMP, impute, tidyr, clusterProfiler, org.Hs.eg.db, edgeR, limma, quantreg, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, dearseq, NOISeq, testthat (>= 3.0.0), CATT, TCGAbiolinks, enrichplot, GEOquery, BiocGenerics License: Artistic-2.0 MD5sum: 41861e6be3d1fb356ec3be8bacf916b8 NeedsCompilation: no Title: Processing Various Types of Data on GEO and TCGA Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data. biocViews: GeneExpression, DifferentialExpression, RNASeq, CopyNumberVariation, Microarray, Software, DNAMethylation, DifferentialMethylation, SNP, ATACSeq, MethylationArray Author: Erqiang Hu [aut, cre] (ORCID: ) Maintainer: Erqiang Hu <13766876214@163.com> URL: https://github.com/YuLab-SMU/GeoTcgaData VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GeoTcgaData/issues git_url: https://git.bioconductor.org/packages/GeoTcgaData git_branch: devel git_last_commit: 49ac154 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GeoTcgaData_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GeoTcgaData_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GeoTcgaData_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GeoTcgaData_2.7.0.tgz vignettes: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html vignetteTitles: GeoTcgaData hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.R dependencyCount: 75 Package: gep2pep Version: 1.27.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: b410ffe8297f221ea3c80ed9ccf1ac8d NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: devel git_last_commit: 98fa3a0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gep2pep_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gep2pep_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gep2pep_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gep2pep_1.27.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 59 Package: getDEE2 Version: 1.17.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 6e156117bca4bed170f29df8df24c305 NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: devel git_last_commit: a598524 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/getDEE2_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/getDEE2_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/getDEE2_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/getDEE2_1.17.0.tgz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R importsMe: homosapienDEE2CellScore dependencyCount: 38 Package: geva Version: 1.15.0 Depends: R (>= 4.1) Imports: grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 94faaabc99a34bdbe92afda8d61f2b83 NeedsCompilation: no Title: Gene Expression Variation Analysis (GEVA) Description: Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors. biocViews: Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, SystemsBiology, Transcriptomics Author: Itamar José Guimarães Nunes [aut, cre] (ORCID: ), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (ORCID: ), Marcio Dorn [ctb] (ORCID: ) Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: devel git_last_commit: d89cefb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/geva_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/geva_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/geva_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/geva_1.15.0.tgz vignettes: vignettes/geva/inst/doc/geva.pdf vignetteTitles: GEVA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geva/inst/doc/geva.R dependencyCount: 10 Package: GEWIST Version: 1.51.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: a5923199545cab4f38f2b32125172ec4 NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: devel git_last_commit: f80228c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GEWIST_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GEWIST_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GEWIST_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GEWIST_1.51.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 69 Package: gg4way Version: 1.5.0 Depends: R (>= 4.3.0), ggplot2 Imports: DESeq2, dplyr, edgeR, ggrepel, glue, janitor, limma, magrittr, methods, purrr, rlang, scales, stats, stringr, tibble, tidyr Suggests: airway, BiocStyle, knitr, org.Hs.eg.db, rmarkdown, testthat, vdiffr License: MIT + file LICENSE MD5sum: f01db7c0264c1f61bb3ef2fe23fba14e NeedsCompilation: no Title: 4way Plots of Differential Expression Description: 4way plots enable a comparison of the logFC values from two contrasts of differential gene expression. The gg4way package creates 4way plots using the ggplot2 framework and supports popular Bioconductor objects. The package also provides information about the correlation between contrasts and significant genes of interest. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/gg4way VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/gg4way/issues git_url: https://git.bioconductor.org/packages/gg4way git_branch: devel git_last_commit: b67ad3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gg4way_1.5.0.tar.gz vignettes: vignettes/gg4way/inst/doc/gg4way.html vignetteTitles: gg4way hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gg4way/inst/doc/gg4way.R dependencyCount: 92 Package: ggbio Version: 1.55.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: 5bd9bce4f66ee9564c6071d78bde3303 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: https://lawremi.github.io/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/lawremi/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: devel git_last_commit: 289a36c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggbio_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggbio_1.55.0.zip vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: BOBaFIT, cageminer, Damsel, derfinderPlot, FLAMES, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures, OHCA, MOCHA suggestsMe: bambu, beadarray, ensembldb, FRASER, gwascat, interactiveDisplay, NanoStringNCTools, OUTRIDER, RnBeads, shiny.gosling, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 162 Package: ggcyto Version: 1.35.0 Depends: methods, ggplot2(>= 3.5.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 4.3.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: file LICENSE MD5sum: e7bbdb05371ed796964828177de690fe NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: devel git_last_commit: 21e5576 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggcyto_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggcyto_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggcyto_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggcyto_1.35.0.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R dependsOnMe: flowGate importsMe: CytoML, CytoPipeline suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace, openCyto dependencyCount: 70 Package: ggkegg Version: 1.5.0 Depends: R (>= 4.3.0), ggplot2, ggraph, XML, igraph, tidygraph Imports: BiocFileCache, GetoptLong, data.table, dplyr, magick, patchwork, shadowtext, stringr, tibble, methods, utils, stats, grDevices, gtable Suggests: knitr, clusterProfiler, bnlearn, rmarkdown, BiocStyle, AnnotationDbi, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 84531c180ac0fbddcd484d47ec276ead NeedsCompilation: no Title: Analyzing and visualizing KEGG information using the grammar of graphics Description: This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. biocViews: Pathways, DataImport, KEGG Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/ggkegg VignetteBuilder: knitr BugReports: https://github.com/noriakis/ggkegg/issues git_url: https://git.bioconductor.org/packages/ggkegg git_branch: devel git_last_commit: 5ce3db0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggkegg_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggkegg_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggkegg_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggkegg_1.5.0.tgz vignettes: vignettes/ggkegg/inst/doc/usage_of_ggkegg.html vignetteTitles: ggkegg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggkegg/inst/doc/usage_of_ggkegg.R importsMe: pathfindR dependencyCount: 86 Package: ggmanh Version: 1.11.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, paletteer, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats, tidyr, dplyr, pals, magrittr Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), GenomicRanges License: MIT + file LICENSE MD5sum: 96897f0e2e4f5a8b4a9011a200a04030 NeedsCompilation: no Title: Visualization Tool for GWAS Result Description: Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints. biocViews: Visualization, GenomeWideAssociation, Genetics Author: John Lee [aut, cre], John Lee [aut] (AbbVie), Xiuwen Zheng [ctb, dtc] Maintainer: John Lee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: devel git_last_commit: c8acd63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggmanh_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggmanh_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggmanh_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggmanh_1.11.0.tgz vignettes: vignettes/ggmanh/inst/doc/ggmanh.html vignetteTitles: Guide to ggmanh Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R suggestsMe: SAIGEgds dependencyCount: 76 Package: ggmsa Version: 1.13.1 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggalt, ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods, statebins, ggtree (>= 1.17.1) Suggests: ggtreeExtra, ape, cowplot, knitr, BiocStyle, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3897916d10f6c061d8e0a776a62ae56b NeedsCompilation: no Title: Plot Multiple Sequence Alignment using 'ggplot2' Description: A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. biocViews: Software, Visualization, Alignment, Annotation, MultipleSequenceAlignment Author: Guangchuang Yu [aut, cre, ths] (ORCID: ), Lang Zhou [aut], Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Guangchuang Yu URL: https://doi.org/10.1093/bib/bbac222(paper), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: devel git_last_commit: b90d4b9 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/ggmsa_1.13.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggmsa_1.13.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggmsa_1.13.1.tgz vignettes: vignettes/ggmsa/inst/doc/ggmsa.html vignetteTitles: ggmsa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R importsMe: ggaligner, SeedMatchR dependencyCount: 116 Package: GGPA Version: 1.19.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: fb6053eb870f7dbd3e5a79709ab02d6c NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: devel git_last_commit: 872cd05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GGPA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GGPA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GGPA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GGPA_1.19.0.tgz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 61 Package: ggsc Version: 1.5.0 Imports: Rcpp, RcppParallel, cli, dplyr, ggfun (>= 0.1.5), ggplot2, grDevices, grid, methods, rlang, scattermore, stats, Seurat, SingleCellExperiment, SummarizedExperiment, tidydr, tidyr, tibble, utils, RColorBrewer, yulab.utils, scales LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: aplot, BiocParallel, forcats, ggforce, ggnewscale, igraph, knitr, ks, Matrix, prettydoc, rmarkdown, scran, scater, scatterpie (>= 0.2.4), scuttle, shadowtext, sf, SeuratObject, SpatialExperiment, STexampleData, testthat (>= 3.0.0), MASS License: Artistic-2.0 MD5sum: 1faa602e3dbcc94518b181cb5c3d35d0 NeedsCompilation: yes Title: Visualizing Single Cell and Spatial Transcriptomics Description: Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'. biocViews: DimensionReduction, GeneExpression, SingleCell, Software, Spatial, Transcriptomics,Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [aut] (ORCID: ), Noriaki Sato [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggsc (devel), https://yulab-smu.top/ggsc/ (docs) SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggsc/issues git_url: https://git.bioconductor.org/packages/ggsc git_branch: devel git_last_commit: 95664cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggsc_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggsc_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggsc_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggsc_1.5.0.tgz vignettes: vignettes/ggsc/inst/doc/ggsc.html vignetteTitles: Visualizing single cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggsc/inst/doc/ggsc.R dependencyCount: 176 Package: ggseqalign Version: 1.1.0 Depends: R (>= 4.4.0) Imports: pwalign, dplyr, ggplot2 Suggests: Biostrings, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: f19bb91fcda69d877ee890e6b1e85a8c NeedsCompilation: no Title: Minimal Visualization of Sequence Alignments Description: Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme(). biocViews: Alignment, MultipleSequenceAlignment, Software, Visualization Author: Simeon Lim Rossmann [aut, cre] (ORCID: ) Maintainer: Simeon Lim Rossmann URL: https://github.com/simeross/ggseqalign VignetteBuilder: knitr BugReports: https://github.com/simeross/ggseqalign/issues git_url: https://git.bioconductor.org/packages/ggseqalign git_branch: devel git_last_commit: 5e75de0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggseqalign_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggseqalign_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggseqalign_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggseqalign_1.1.0.tgz vignettes: vignettes/ggseqalign/inst/doc/ggseqalign.html vignetteTitles: Quickstart Guide to ggseqalign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggseqalign/inst/doc/ggseqalign.R dependencyCount: 58 Package: ggspavis Version: 1.13.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, ggrepel, RColorBrewer, scales, grDevices, methods, stats Suggests: BiocStyle, rmarkdown, knitr, STexampleData, BumpyMatrix, scater, scran, uwot, testthat, patchwork License: MIT + file LICENSE MD5sum: 6a55a885cccd2399e9c02fdd8dc1ee02 NeedsCompilation: no Title: Visualization functions for spatial transcriptomics data Description: Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, QualityControl, DimensionReduction Author: Lukas M. Weber [aut, cre] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Yixing E. Dong [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: devel git_last_commit: 479bf59 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggspavis_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggspavis_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggspavis_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggspavis_1.13.0.tgz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R suggestsMe: smoothclust, HCATonsilData dependencyCount: 89 Package: ggtree Version: 3.15.0 Depends: R (>= 3.5.0) Imports: ape, aplot, dplyr, ggplot2 (> 3.3.6), grid, magrittr, methods, purrr, rlang, ggfun (>= 0.1.7), yulab.utils (>= 0.1.6), tidyr, tidytree (>= 0.4.5), treeio (>= 1.8.0), utils, scales, stats, cli Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr, prettydoc, rmarkdown, testthat, tibble, glue License: Artistic-2.0 MD5sum: e6e370294b42f85c77689f426ea6caa6 NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (ORCID: ), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: devel git_last_commit: ac91900 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggtree_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtree_3.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtree_3.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtree_3.15.0.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R dependsOnMe: ggtreeDendro, tanggle importsMe: cardelino, cogeqc, enrichplot, ggmsa, ggtreeExtra, ggtreeSpace, gINTomics, lefser, LinTInd, LymphoSeq, miaViz, MicrobiotaProcess, orthogene, philr, scBubbletree, scDotPlot, singleCellTK, sitePath, systemPipeTools, treeclimbR, treekoR, BioVizSeq, DAISIEprep, ddtlcm, dowser, EvoPhylo, FossilSim, genBaRcode, harrietr, numbat, Platypus, RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon, TransProR suggestsMe: compcodeR, syntenet, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, aplotExtra, CoOL, DAISIE, deeptime, gggenomes, ggimage, ggtangle, idiogramFISH, MetaNet, nosoi, oppr, PCMBase, pctax, RAINBOWR, rhierbaps, rphylopic dependencyCount: 59 Package: ggtreeDendro Version: 1.9.0 Depends: ggtree (>= 3.5.3) Imports: ggplot2, stats, tidytree, utils Suggests: aplot, cluster, knitr, MASS, mdendro, prettydoc, pvclust, rmarkdown, testthat (>= 3.0.0), treeio, yulab.utils License: Artistic-2.0 MD5sum: df5a67737c1d7da95c7a70710108023c NeedsCompilation: no Title: Drawing 'dendrogram' using 'ggtree' Description: Offers a set of 'autoplot' methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using 'ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree. biocViews: Clustering, Classification, DecisionTree, Phylogenetics, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [ctb] (ORCID: ), Chuanjie Zhang [ctb] Maintainer: Guangchuang Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggtreeDendro git_branch: devel git_last_commit: ecbc889 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggtreeDendro_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeDendro_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeDendro_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeDendro_1.9.0.tgz vignettes: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.html vignetteTitles: Visualizing Dendrogram using ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.R dependencyCount: 60 Package: ggtreeExtra Version: 1.17.0 Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree, tidytree (>= 0.3.9), cli, magrittr Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar License: GPL (>= 3) Archs: x64 MD5sum: 52ec85f98a6658c924536ffefaffc7d0 NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: devel git_last_commit: 5620083 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggtreeExtra_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeExtra_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeExtra_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeExtra_1.17.0.tgz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R importsMe: MicrobiotaProcess, TransProR suggestsMe: enrichplot, ggmsa, pctax dependencyCount: 61 Package: ggtreeSpace Version: 1.3.0 Imports: interp, ape, dplyr, GGally, ggplot2, grid, ggtree, phytools, rlang, tibble, tidyr, tidyselect, stats Suggests: knitr, prettydoc, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 07cb1d853ce0917e0e70df9beb38c82a NeedsCompilation: no Title: Visualizing Phylomorphospaces using 'ggtree' Description: This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns. biocViews: Annotation, Visualization, Phylogenetics, Software Author: Guangchuang Yu [aut, cre, ths, cph] (ORCID: ), Li Lin [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggtreeSpace VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeSpace/issues git_url: https://git.bioconductor.org/packages/ggtreeSpace git_branch: devel git_last_commit: 4e39526 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ggtreeSpace_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ggtreeSpace_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeSpace_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ggtreeSpace_1.3.0.tgz vignettes: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.html vignetteTitles: Introduction to ggtreeSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.R dependencyCount: 90 Package: GIGSEA Version: 1.25.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 970533d0cc85ca2b6827c4147bf69dfd NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: devel git_last_commit: af5e89c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GIGSEA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GIGSEA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GIGSEA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GIGSEA_1.25.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: ginmappeR Version: 1.3.3 Imports: KEGGREST, UniProt.ws, XML, rentrez, httr, utils, memoise, cachem, jsonlite, rvest Suggests: RUnit, BiocGenerics, markdown, knitr License: GPL-3 + file LICENSE MD5sum: de5acaf2c36e873785a151442ea210f3 NeedsCompilation: no Title: Gene Identifier Mapper Description: Provides functionalities to translate gene or protein identifiers between state-of-art biological databases: CARD (), NCBI Protein, Nucleotide and Gene (), UniProt () and KEGG (). Also offers complementary functionality like NCBI identical proteins or UniProt similar genes clusters retrieval. biocViews: Annotation, KEGG, Genetics, ThirdPartyClient, Software Author: Fernando Sola [aut, cre] (ORCID: ), Daniel Ayala [aut] (ORCID: ), Marina Pulido [aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Lorena López-Cerero [aut] (ORCID: ), Inma Hernández [aut] (ORCID: ), David Ruiz [aut] (ORCID: ) Maintainer: Fernando Sola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ginmappeR git_branch: devel git_last_commit: 2c546ae git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-02 source.ver: src/contrib/ginmappeR_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/ginmappeR_1.3.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ginmappeR_1.3.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ginmappeR_1.3.3.tgz vignettes: vignettes/ginmappeR/inst/doc/ginmappeR.html vignetteTitles: ginmappeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ginmappeR/inst/doc/ginmappeR.R dependencyCount: 74 Package: gINTomics Version: 1.3.0 Depends: R (>= 4.4.0) Imports: BiocParallel, biomaRt, OmnipathR, edgeR, ggplot2, ggridges, gtools, MultiAssayExperiment, plyr, stringi, stringr, SummarizedExperiment, methods, stats, reshape2, randomForest, limma, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, GenomicFeatures, ReactomePA, clusterProfiler, dplyr, AnnotationDbi, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, shiny, GenomicRanges, ggtree, shinydashboard, plotly, DT, MASS, InteractiveComplexHeatmap, ComplexHeatmap, visNetwork, shiny.gosling, ggvenn, RColorBrewer, utils, grDevices, callr, circlize Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 MD5sum: f44ab279a1201e3e292d834875f95320 NeedsCompilation: no Title: Multi-Omics data integration Description: gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app. biocViews: GeneExpression, RNASeq, Microarray, Visualization, CopyNumberVariation, GeneTarget Author: Angelo Velle [cre, aut] (ORCID: ), Francesco Patane' [aut] (ORCID: ), Chiara Romualdi [aut] (ORCID: ) Maintainer: Angelo Velle URL: https://github.com/angelovelle96/gINTomics VignetteBuilder: knitr BugReports: https://github.com/angelovelle96/gINTomics/issues git_url: https://git.bioconductor.org/packages/gINTomics git_branch: devel git_last_commit: 3ba788e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gINTomics_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gINTomics_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gINTomics_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gINTomics_1.3.0.tgz vignettes: vignettes/gINTomics/inst/doc/gINTomics.html vignetteTitles: gINTomics vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gINTomics/inst/doc/gINTomics.R dependencyCount: 239 Package: girafe Version: 1.59.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), pwalign, graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 MD5sum: 8035a2033daf769697cb69dba29b6fb5 NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/girafe git_branch: devel git_last_commit: 595754b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/girafe_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/girafe_1.59.1.zip vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R dependencyCount: 66 Package: GLAD Version: 2.71.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: 8f7b39144bdaad24d240809cb1dcb3ce NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: devel git_last_commit: 1c16508 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GLAD_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GLAD_2.71.0.zip vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS importsMe: ITALICS, MANOR suggestsMe: aroma.cn, aroma.core dependencyCount: 4 Package: GladiaTOX Version: 1.23.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMariaDB, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: 9fa799d4bc2d1ed96b918962bf1a374c NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: devel git_last_commit: c12ce38 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GladiaTOX_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GladiaTOX_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GladiaTOX_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GladiaTOX_1.23.0.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 68 Package: Glimma Version: 2.17.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran License: GPL-3 MD5sum: 16d7ab6e174541eb7dcf0098e5d621e1 NeedsCompilation: no Title: Interactive visualizations for gene expression analysis Description: This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: devel git_last_commit: 895d8b3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Glimma_2.17.0.tar.gz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html, vignettes/Glimma/inst/doc/single_cell_edger.html vignetteTitles: DESeq2, Introduction using limma or edgeR, Single Cells with edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R, vignettes/Glimma/inst/doc/single_cell_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools dependencyCount: 100 Package: glmGamPoi Version: 1.19.2 Imports: Rcpp, beachmat, DelayedMatrixStats, matrixStats, MatrixGenerics, SparseArray (>= 1.5.21), DelayedArray, HDF5Array, Matrix, SummarizedExperiment, SingleCellExperiment, BiocGenerics, methods, stats, utils, splines, rlang, vctrs LinkingTo: Rcpp, RcppArmadillo, beachmat, assorthead Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran, dplyr License: GPL-3 MD5sum: 7a8bf882ca1f3eb1417dd4769c28d2d5 NeedsCompilation: yes Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Nathan Lubock [ctb] (ORCID: ), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: devel git_last_commit: e8799cb git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-10 source.ver: src/contrib/glmGamPoi_1.19.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/glmGamPoi_1.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/glmGamPoi_1.19.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/glmGamPoi_1.19.2.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html, vignettes/glmGamPoi/inst/doc/pseudobulk.html vignetteTitles: glmGamPoi Quickstart, Pseudobulk and differential expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R, vignettes/glmGamPoi/inst/doc/pseudobulk.R importsMe: BASiCStan, lemur, transformGamPoi, SCdeconR suggestsMe: DESeq2, scregclust dependencyCount: 54 Package: glmSparseNet Version: 1.25.0 Depends: R (>= 4.3.0) Imports: biomaRt, checkmate, dplyr, forcats, futile.logger, ggplot2, glue, httr, lifecycle, methods, parallel, readr, rlang, glmnet, Matrix, MultiAssayExperiment, SummarizedExperiment, survminer, TCGAutils, utils Suggests: BiocStyle, curatedTCGAData, knitr, magrittr, reshape2, pROC, rmarkdown, survival, testthat, VennDiagram, withr License: GPL-3 MD5sum: 1674da34f781b5a36088759e54528683 NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre] (ORCID: ), Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: devel git_last_commit: 91851c6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/glmSparseNet_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/glmSparseNet_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/glmSparseNet_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/glmSparseNet_1.25.0.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma, Separate 2 groups in Cox regression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R dependencyCount: 182 Package: GlobalAncova Version: 4.25.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: 95f934dc780d868a0edfa9a535b44cff NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: devel git_last_commit: 5090aa6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GlobalAncova_4.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GlobalAncova_4.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GlobalAncova_4.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GlobalAncova_4.25.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest suggestsMe: GiANT dependencyCount: 82 Package: globalSeq Version: 1.35.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 Archs: x64 MD5sum: 82503f8353883178ee5503ae12c8b245 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: devel git_last_commit: 68e448d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/globalSeq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/globalSeq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/globalSeq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/globalSeq_1.35.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.61.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) Archs: x64 MD5sum: 1b16186bde528acc0ce0766249f8984f NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: devel git_last_commit: d812738 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/globaltest_5.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/globaltest_5.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/globaltest_5.61.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest, SlaPMEG suggestsMe: topGO, GiANT, penalized dependencyCount: 54 Package: GloScope Version: 1.5.0 Depends: R (>= 4.4.0) Imports: utils, stats, MASS, mclust, ggplot2, RANN, FNN, BiocParallel, mvnfast, SingleCellExperiment, rlang Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown, zellkonverter License: Artistic-2.0 MD5sum: 9fded23ee3a3e47a413f0632ca535a59 NeedsCompilation: no Title: Population-level Representation on scRNA-Seq data Description: This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots. biocViews: DataRepresentation, QualityControl, RNASeq, Sequencing, Software, SingleCell Author: William Torous [aut, cre] (ORCID: ), Hao Wang [aut] (ORCID: ), Elizabeth Purdom [aut], Boying Gong [aut] Maintainer: William Torous VignetteBuilder: knitr BugReports: https://github.com/epurdom/GloScope/issues git_url: https://git.bioconductor.org/packages/GloScope git_branch: devel git_last_commit: 62fe7a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GloScope_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GloScope_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GloScope_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GloScope_1.5.0.tgz vignettes: vignettes/GloScope/inst/doc/GloScopeTutorial.html vignetteTitles: GloScope hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GloScope/inst/doc/GloScopeTutorial.R dependencyCount: 80 Package: gmapR Version: 1.49.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: b42d9c94e51a80c60b0aac79a8bf42f3 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: devel git_last_commit: fbb42ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gmapR_1.49.0.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R suggestsMe: VariantTools, VariantToolsData dependencyCount: 80 Package: GmicR Version: 1.21.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE Archs: x64 MD5sum: 319ed322a3c55d2a74d8f64a59eba19c NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: devel git_last_commit: 84bfd05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GmicR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GmicR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GmicR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GmicR_1.21.0.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 155 Package: gmoviz Version: 1.19.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, GenomeInfoDb, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager License: GPL-3 MD5sum: c4a8540b1a0fa3fd8183ebf07dd150a5 NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (ORCID: ), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: devel git_last_commit: f73a067 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gmoviz_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gmoviz_1.19.0.zip vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 93 Package: GMRP Version: 1.35.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: b83e883e39526efa3aacdf3d25c6af25 NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: devel git_last_commit: 261aa5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GMRP_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GMRP_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GMRP_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GMRP_1.35.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 29 Package: GNET2 Version: 1.23.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: 75259c0f570ea6ae1e968f8fe552f392 NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: devel git_last_commit: fb558f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GNET2_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GNET2_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GNET2_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GNET2_1.23.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 108 Package: GNOSIS Version: 1.5.0 Depends: R (>= 4.3.0), shiny, shinydashboard, shinydashboardPlus, dashboardthemes, shinyWidgets, shinymeta, tidyverse, operator.tools, maftools Imports: DT, fontawesome, shinycssloaders, cBioPortalData, shinyjs, reshape2, RColorBrewer, survival, survminer, stats, compareGroups, rpart, partykit, DescTools, car, rstatix, fabricatr, shinylogs, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 8122dc73fb552a75eccf80fb0d28b747 NeedsCompilation: no Title: Genomics explorer using statistical and survival analysis in R Description: GNOSIS incorporates a range of R packages enabling users to efficiently explore and visualise clinical and genomic data obtained from cBioPortal. GNOSIS uses an intuitive GUI and multiple tab panels supporting a range of functionalities. These include data upload and initial exploration, data recoding and subsetting, multiple visualisations, survival analysis, statistical analysis and mutation analysis, in addition to facilitating reproducible research. biocViews: Software, ShinyApps, Survival, GUI Author: Lydia King [aut, cre] (ORCID: ), Marcel Ramos [ctb] Maintainer: Lydia King URL: https://github.com/Lydia-King/GNOSIS/ VignetteBuilder: knitr Video: https://doi.org/10.5281/zenodo.5788544 BugReports: https://github.com/Lydia-King/GNOSIS/issues git_url: https://git.bioconductor.org/packages/GNOSIS git_branch: devel git_last_commit: 45d7820 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GNOSIS_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GNOSIS_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GNOSIS_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GNOSIS_1.5.0.tgz vignettes: vignettes/GNOSIS/inst/doc/GNOSIS.html vignetteTitles: GNOSIS Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GNOSIS/inst/doc/GNOSIS.R dependencyCount: 297 Package: GOexpress Version: 1.41.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: 3b17ab255807ac9aa93b5591162c1fd7 NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: devel git_last_commit: 015022e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOexpress_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOexpress_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOexpress_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOexpress_1.41.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 93 Package: GOfuncR Version: 1.27.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat License: GPL (>= 2) MD5sum: 4c14aa224d718feb0cb350454fd12723 NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (01-May-2021). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: devel git_last_commit: 872b8cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOfuncR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOfuncR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOfuncR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOfuncR_1.27.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R dependencyCount: 55 Package: GOpro Version: 1.33.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 MD5sum: 4d5cb94a8683f861d5a3d43f1834ad58 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: devel git_last_commit: 5a8d050 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOpro_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOpro_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOpro_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOpro_1.33.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 98 Package: goProfiles Version: 1.69.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: 9ce163968fed115b0476a65dae740117 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: devel git_last_commit: 9437379 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/goProfiles_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goProfiles_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goProfiles_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goProfiles_1.69.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R importsMe: goSorensen dependencyCount: 51 Package: GOSemSim Version: 2.33.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, DBI, digest, GO.db, methods, rlang, R.utils, stats, utils, yulab.utils (>= 0.1.6) LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, rappdirs, readr, rmarkdown, testthat, tidyr, tidyselect, ROCR License: Artistic-2.0 Archs: x64 MD5sum: 413ad21f835b8b411ccf3cef1f8c7158 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: devel git_last_commit: 5f25326 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOSemSim_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOSemSim_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOSemSim_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOSemSim_2.33.0.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, enrichplot, GeDi, meshes, Rcpi, rrvgo, ViSEAGO, BiSEp suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, scDiffCom dependencyCount: 54 Package: goseq Version: 1.59.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db, BiocGenerics, methods, rtracklayer, GenomicFeatures, GenomeInfoDb Suggests: edgeR, org.Hs.eg.db License: LGPL (>= 2) MD5sum: dd8bb1ce8a3a2ad7b5d72b5b498a1249 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data. biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq, DifferentialExpression, Annotation, GeneSetEnrichment, KEGG, Pathways, Software Author: Matthew Young [aut], Nadia Davidson [aut], Federico Marini [ctb, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/goseq BugReports: https://github.com/federicomarini/goseq/issues git_url: https://git.bioconductor.org/packages/goseq git_branch: devel git_last_commit: 87e9f39 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/goseq_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goseq_1.59.0.zip vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: Damsel, ideal, mosdef, SMITE suggestsMe: sparrow dependencyCount: 108 Package: goSorensen Version: 1.9.0 Depends: R (>= 4.3) Imports: clusterProfiler, goProfiles, org.Hs.eg.db, parallel, stats, stringr Suggests: BiocManager, BiocStyle, knitr, rmarkdown, org.At.tair.db, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcSakai.eg.db, org.EcK12.eg.db, org.Gg.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Pt.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: 26d4c4e929aca5f1f4d36f78b1c9c434 NeedsCompilation: no Title: Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO) Description: This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items. biocViews: Annotation, GO, GeneSetEnrichment, Software, Microarray, Pathways, GeneExpression, MultipleComparison, GraphAndNetwork, Reactome, Clustering, KEGG Author: Pablo Flores [aut, cre] (ORCID: ), Jordi Ocana [aut, ctb] (ORCID: 0000-0002-4736-699), Alexandre Sanchez-Pla [ctb] (ORCID: ), Miquel Salicru [ctb] (ORCID: ) Maintainer: Pablo Flores VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSorensen git_branch: devel git_last_commit: 88ed371 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/goSorensen_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goSorensen_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goSorensen_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goSorensen_1.9.0.tgz vignettes: vignettes/goSorensen/inst/doc/goSorensen_Introduction.html, vignettes/goSorensen/inst/doc/irrelevance-threshold_Matrix_Dissimilarities.html, vignettes/goSorensen/inst/doc/README.html vignetteTitles: An introduction to equivalence test between feature lists using goSorensen., An Irrelevance Threshold Matrix of Dissimilarities., README hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSorensen/inst/doc/goSorensen_Introduction.R, vignettes/goSorensen/inst/doc/irrelevance-threshold_Matrix_Dissimilarities.R, vignettes/goSorensen/inst/doc/README.R dependencyCount: 127 Package: goSTAG Version: 1.31.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: a07925ede5f939382476258e53ad1444 NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: devel git_last_commit: fd8ca2d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/goSTAG_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goSTAG_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goSTAG_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goSTAG_1.31.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 70 Package: GOstats Version: 2.73.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 615bb6e4c02a678912df97e8d9a43d92 NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Sonali Kumari [ctb] (Converted vignettes from Sweave to R Markdown / HTML.), Dennis Ndubi [ctb] (Converted GOstatsHyperG vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOstats git_branch: devel git_last_commit: a8f7238 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOstats_2.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOstats_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GOstats_2.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GOstats_2.73.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.html, vignettes/GOstats/inst/doc/GOstatsHyperG.html, vignettes/GOstats/inst/doc/GOvis.html vignetteTitles: How To Use GOstats and Category to do Hypergeometric testing with unsupported model organisms, Hypergeometric Tests Using GOstats, Visualizing and Distances Using GO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, miRLAB, pcaExplorer, scTensor, SGCP, DNLC suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, maGUI, sand dependencyCount: 67 Package: GOTHiC Version: 1.43.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, GenomeInfoDb Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: c9f44f8f6298ef87708580cab72baea1 NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: devel git_last_commit: fdd7c2b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GOTHiC_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GOTHiC_1.43.0.zip vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R importsMe: OHCA dependencyCount: 98 Package: goTools Version: 1.81.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 Archs: x64 MD5sum: 3caefa49b01ca79d0242a4e9cdce7533 NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: devel git_last_commit: 6659a1b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/goTools_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/goTools_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/goTools_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/goTools_1.81.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 47 Package: GPA Version: 1.19.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: 5580d226b5700c1a5955c7f67b9f1617 NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: devel git_last_commit: adab3a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GPA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GPA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GPA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GPA_1.19.0.tgz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 77 Package: gpls Version: 1.79.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 0d7fd8d90ec0d098e47a44f74d32750d NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: devel git_last_commit: eb0ca4c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gpls_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gpls_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gpls_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gpls_1.79.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: gpuMagic Version: 1.23.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 2406c611a2c17a7834959ed57aea298f NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For Windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For Linux user (Except mac): ocl-icd-opencl-dev package is required. For Mac user, no action is needed for the system has installed the dependency. 4. GNU make VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: devel git_last_commit: 06a3340 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gpuMagic_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gpuMagic_1.23.0.zip vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html, vignettes/gpuMagic/inst/doc/Quick_start_guide.html vignetteTitles: Customized_opencl_code, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R, vignettes/gpuMagic/inst/doc/Quick_start_guide.R dependencyCount: 71 Package: GrafGen Version: 1.3.0 Depends: R (>= 4.3.0) Imports: stats, utils, graphics, ggplot2, plotly, zlibbioc, scales, RColorBrewer, dplyr, grDevices, GenomicRanges, shiny, cowplot, ggpubr, stringr, rlang Suggests: knitr, rmarkdown, RUnit, BiocManager, BiocGenerics, BiocStyle, devtools License: GPL-2 MD5sum: 2a4cb7a3d73f1589c211e73f8dd97278 NeedsCompilation: yes Title: Classification of Helicobacter Pylori Genomes Description: To classify Helicobacter pylori genomes according to genetic distance from nine reference populations. The nine reference populations are hpgpAfrica, hpgpAfrica-distant, hpgpAfroamerica, hpgpEuroamerica, hpgpMediterranea, hpgpEurope, hpgpEurasia, hpgpAsia, and hpgpAklavik86-like. The vertex populations are Africa, Europe and Asia. biocViews: Genetics, Software, GenomeAnnotation, Classification Author: William Wheeler [aut, cre], Difei Wang [aut], Isaac Zhao [aut], Yumi Jin [aut], Charles Rabkin [aut] Maintainer: William Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/GrafGen/issues git_url: https://git.bioconductor.org/packages/GrafGen git_branch: devel git_last_commit: bcc109a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GrafGen_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GrafGen_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GrafGen_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GrafGen_1.3.0.tgz vignettes: vignettes/GrafGen/inst/doc/vignette.html vignetteTitles: GrafGen: Classifying H. pylori genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GrafGen/inst/doc/vignette.R dependencyCount: 126 Package: GRaNIE Version: 1.11.0 Depends: R (>= 4.2.0) Imports: futile.logger, checkmate, patchwork (>= 1.2.0), reshape2, data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer, ComplexHeatmap, DESeq2, circlize, progress, utils, methods, stringr, tools, scales, igraph, S4Vectors, ggplot2, rlang, Biostrings, GenomeInfoDb (>= 1.34.8), SummarizedExperiment, forcats, gridExtra, limma, tidyselect, readr, grid, tidyr (>= 1.3.0), dplyr, stats, grDevices, graphics, magrittr, tibble, viridis, colorspace, biomaRt, topGO, AnnotationHub, ensembldb Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Mmulatta.UCSC.rheMac10, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dm.eg.db, org.Mmu.eg.db, IHW, clusterProfiler, ReactomePA, DOSE, BiocFileCache, ChIPseeker, testthat (>= 3.0.0), BiocStyle, csaw, BiocParallel, WGCNA, variancePartition, purrr, EDASeq, JASPAR2022, JASPAR2024, RSQLite, TFBSTools, motifmatchr, rbioapi, LDlinkR License: Artistic-2.0 MD5sum: ecde9ce0b2c0fe073791d85cd8ff06dd NeedsCompilation: no Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data Description: Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach. biocViews: Software, GeneExpression, GeneRegulation, NetworkInference, GeneSetEnrichment, BiomedicalInformatics, Genetics, Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression, Transcription, ChIPSeq Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb] Maintainer: Christian Arnold URL: https://grp-zaugg.embl-community.io/GRaNIE VignetteBuilder: knitr BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues git_url: https://git.bioconductor.org/packages/GRaNIE git_branch: devel git_last_commit: 7fdc850 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GRaNIE_1.11.0.tar.gz vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.html, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html vignetteTitles: Package Details, Single-cell eGRN inference, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.R, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R dependencyCount: 156 Package: granulator Version: 1.15.0 Depends: R (>= 4.1) Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify, grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap, purrr, rlang, stats, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 8280e2deaba8dd3a239cdb08b6e3e3b3 NeedsCompilation: no Title: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data Description: granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well. biocViews: RNASeq, GeneExpression, DifferentialExpression, Transcriptomics, SingleCell, StatisticalMethod, Regression Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico Ferrero [aut] Maintainer: Sabina Pfister URL: https://github.com/xanibas/granulator VignetteBuilder: knitr BugReports: https://github.com/xanibas/granulator/issues git_url: https://git.bioconductor.org/packages/granulator git_branch: devel git_last_commit: 61e04a0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/granulator_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/granulator_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/granulator_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/granulator_1.15.0.tgz vignettes: vignettes/granulator/inst/doc/granulator.html vignetteTitles: Deconvoluting bulk RNA-seq data with granulator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/granulator/inst/doc/granulator.R suggestsMe: deconvR dependencyCount: 111 Package: graper Version: 1.23.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: e141f1412fcae49e4dcdaa67872f5349 NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: devel git_last_commit: 91803f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/graper_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graper_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graper_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graper_1.23.0.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 40 Package: graph Version: 1.85.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr Enhances: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: 211b8dec75acffc640bd68864f58b739 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R Gentleman [aut], Elizabeth Whalen [aut], W Huber [aut], S Falcon [aut], Jeff Gentry [aut], Paul Shannon [aut], Halimat C. Atanda [ctb] (Converted 'MultiGraphClass' and 'GraphClass' vignettes from Sweave to RMarkdown / HTML.), Paul Villafuerte [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Aliyu Atiku Mustapha [ctb] (Converted 'Graph' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graph git_branch: devel git_last_commit: eff9ecd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/graph_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graph_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graph_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graph_1.85.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.html, vignettes/graph/inst/doc/graph.html, vignettes/graph/inst/doc/graphAttributes.html, vignettes/graph/inst/doc/GraphClass.html, vignettes/graph/inst/doc/MultiGraphClass.html vignetteTitles: clusterGraph and distGraph, How to use the graph package, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, GOstats, GraphAT, GSEABase, hypergraph, keggorthology, MineICA, pathRender, Pigengene, RbcBook1, RBGL, RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, DLBCL, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler, hasseDiagram, PairViz, PerfMeas, SubpathwayLNCE importsMe: AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA, CytoML, dce, DEGraph, DEsubs, EnrichDO, epiNEM, EventPointer, fgga, flowClust, flowWorkspace, gage, GeneNetworkBuilder, GenomicInteractionNodes, GraphAT, graphite, hyperdraw, KEGGgraph, MIRit, mnem, MOSClip, NCIgraph, netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi, pathview, PhenStat, qpgraph, RCy3, RGraph2js, rsbml, Rtreemix, SGCP, SplicingGraphs, Streamer, VariantFiltering, BioPlex, abn, BayesNetBP, BCDAG, BiDAG, BNrich, ceg, CePa, classGraph, clustNet, CodeDepends, cogmapr, eulerian, ggm, gridDebug, HEMDAG, kpcalg, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, RCPA, rsolr, rSpectral, SEMgraph, stablespec, topologyGSA, tpc, unifDAG, zenplots suggestsMe: AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, omXplore, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnlearn, bnstruct, bsub, ChoR, gbutils, GeneNet, gMCP, lava, loon, maGUI, psych, rEMM, rPref, sisal, textplot, tidygraph dependencyCount: 7 Package: GraphAlignment Version: 1.71.0 License: file LICENSE License_restricts_use: yes MD5sum: 9f0915a96c528eb4186f11b2a0235aeb NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: devel git_last_commit: 3faa9a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GraphAlignment_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GraphAlignment_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GraphAlignment_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GraphAlignment_1.71.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.79.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL Archs: x64 MD5sum: d5d435e9756b6b39c7375563fa535290 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: devel git_last_commit: 87c3fcb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GraphAT_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GraphAT_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GraphAT_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GraphAT_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 21 Package: graphite Version: 1.53.0 Depends: R (>= 4.2), methods Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics, rlang, purrr Suggests: checkmate, a4Preproc, ALL, BiocStyle, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 Archs: x64 MD5sum: 3cc81f42b9b9fcedb30731b9bd3a8c01 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, Metabolomics Author: Gabriele Sales [cre], Enrica Calura [aut], Chiara Romualdi [aut] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/graphite VignetteBuilder: R.rsp BugReports: https://github.com/sales-lab/graphite/issues git_url: https://git.bioconductor.org/packages/graphite git_branch: devel git_last_commit: b1e8a35 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/graphite_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/graphite_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/graphite_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/graphite_1.53.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R importsMe: CBNplot, dce, EnrichmentBrowser, MIRit, mogsa, MOSClip, multiGSEA, ReactomePA, sSNAPPY, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping dependencyCount: 50 Package: GRENITS Version: 1.59.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: a6c0acb85a8e5bd4fd86ee5c593eed48 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: devel git_last_commit: 021f1b5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GRENITS_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GRENITS_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GRENITS_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GRENITS_1.59.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 42 Package: GreyListChIP Version: 1.39.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: x64 MD5sum: 59d1f6e897dc1fad22568790039a7726 NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Matt Eldridge [cre], Gord Brown [aut] Maintainer: Matt Eldridge git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: devel git_last_commit: daa5333 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GreyListChIP_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GreyListChIP_1.39.0.zip vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: DiffBind, epigraHMM dependencyCount: 61 Package: GRmetrics Version: 1.33.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 Archs: x64 MD5sum: 5475419908cb6438495231ab376f2c06 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: devel git_last_commit: cdb20a0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GRmetrics_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GRmetrics_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GRmetrics_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GRmetrics_1.33.0.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 129 Package: GSALightning Version: 1.35.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 0119a7f58c43b4863dce710c42126208 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: devel git_last_commit: 7414410 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSALightning_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSALightning_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSALightning_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSALightning_1.35.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.41.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 4d989333329a423a1ea3de72a0748942 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: devel git_last_commit: 04f1246 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSAR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSAR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSAR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSAR_1.41.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 17 Package: GSCA Version: 2.37.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 638626be275c7ce865b2c384608d6a37 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: devel git_last_commit: b2008f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSCA_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSCA_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSCA_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSCA_2.37.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 72 Package: gscreend Version: 1.21.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: 840e3d21b837d7a97c068ea2fa2ccd61 NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: devel git_last_commit: 12b0796 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gscreend_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gscreend_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gscreend_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gscreend_1.21.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 61 Package: GSEABase Version: 1.69.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr, RUnit License: Artistic-2.0 Archs: x64 MD5sum: da00e58239d65a1a25122685963540d2 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan [aut], Seth Falcon [aut], Robert Gentleman [aut], Paul Villafuerte [ctb] ('GSEABase' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: devel git_last_commit: 9f00f81 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSEABase_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEABase_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEABase_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEABase_1.69.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.html vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cosmosR, dreamlet, EnrichmentBrowser, escape, gep2pep, GlobalAncova, GmicR, GSRI, GSVA, mastR, miRSM, mogsa, oppar, PanomiR, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, sparrow, TFutils, TMSig, vissE, zenith, msigdb, SingscoreAMLMutations, clustermole, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, BaseSet dependencyCount: 50 Package: GSEABenchmarkeR Version: 1.27.0 Depends: R (>= 3.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown License: Artistic-2.0 MD5sum: 8d4bbd34303acf44f0614054cdd1aa23 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: devel git_last_commit: 7fc44fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSEABenchmarkeR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEABenchmarkeR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEABenchmarkeR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEABenchmarkeR_1.27.0.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R suggestsMe: roastgsa dependencyCount: 111 Package: GSEAlm Version: 1.67.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 2486c505d9f23474c702e814cef676cf NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: devel git_last_commit: dc2aaa7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSEAlm_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEAlm_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEAlm_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEAlm_1.67.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 7 Package: GSEAmining Version: 1.17.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat, tm License: GPL-3 | file LICENSE Archs: x64 MD5sum: 687e214c785f18de67516123de3206ea NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: devel git_last_commit: 775d0d7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSEAmining_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSEAmining_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSEAmining_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSEAmining_1.17.0.tgz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 60 Package: gsean Version: 1.27.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: e04286a690b56f7042c21ae4c08c183d NeedsCompilation: yes Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: devel git_last_commit: 2515344 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gsean_1.27.0.tar.gz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 118 Package: GSgalgoR Version: 1.17.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE MD5sum: 32f43d5754160bf6fb5b6352e92a314c NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: devel git_last_commit: 5111e23 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSgalgoR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSgalgoR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSgalgoR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSgalgoR_1.17.0.tgz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.41.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: f29c09b7f03d4bda2b4e1eff2ac7a0a3 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: devel git_last_commit: 2723c81 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSReg_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSReg_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSReg_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSReg_1.41.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 110 Package: GSRI Version: 2.55.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: d0c5140cb37bdbfa082741f95884dd5a NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: devel git_last_commit: 5b1a284 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GSRI_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSRI_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSRI_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSRI_2.55.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 68 Package: GSVA Version: 2.1.4 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel, BiocParallel, SingleCellExperiment, SpatialExperiment, sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array, BiocSingular, cli LinkingTo: cli Suggests: BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs License: GPL (>= 2) MD5sum: aa179e6252e9b9000c9c2b9db137fe10 NeedsCompilation: yes Title: Gene Set Variation Analysis for Microarray and RNA-Seq Data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways, GeneSetEnrichment Author: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GSVA VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: devel git_last_commit: c5e276e git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/GSVA_2.1.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/GSVA_2.1.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GSVA_2.1.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GSVA_2.1.4.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.html vignetteTitles: Gene set variation analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: SMDIC importsMe: consensusOV, EGSEA, escape, octad, oppar, scFeatures, singleCellTK, TBSignatureProfiler, clustermole, DRviaSPCN, GSEMA, psSubpathway, scMappR, SIGN, sigQC, ssdGSA suggestsMe: decoupleR, MCbiclust, sparrow, SPONGE, ReporterScore dependencyCount: 103 Package: gtrellis Version: 1.39.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: b9045b6bb4acfbed12419baa8768681d NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: devel git_last_commit: d40aa01 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gtrellis_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gtrellis_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gtrellis_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gtrellis_1.39.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 33 Package: GUIDEseq Version: 1.37.0 Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics Imports: Biostrings, pwalign, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), stringr, multtest, GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures, rio, tidyr, tools, methods, purrr, ggplot2, openxlsx, patchwork, rlang Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 7ea8ae85c95700b0244a6bdc547235d6 NeedsCompilation: no Title: GUIDE-seq and PEtag-seq analysis pipeline Description: The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: devel git_last_commit: f947d90 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GUIDEseq_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GUIDEseq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GUIDEseq_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GUIDEseq_1.37.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R dependencyCount: 181 Package: Guitar Version: 2.23.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: 8885c8ad55a7639303addac39369712e NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: devel git_last_commit: 4f9853b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Guitar_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Guitar_2.23.0.zip vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 104 Package: Gviz Version: 1.51.0 Depends: R (>= 4.3), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb (>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 7d5393d2029b0e09f2ad52adb60abc4e NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (ORCID: ), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb], Mike Smith [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: devel git_last_commit: 60a0f4a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Gviz_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Gviz_1.51.0.zip vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, cummeRbund, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpli, CAGEfightR, comapr, crisprViz, DMRcate, DuplexDiscovereR, ELMER, epimutacions, GenomicInteractions, maser, mCSEA, MEAL, methylPipe, motifbreakR, OGRE, PING, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, tadar, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata, GRIN2 suggestsMe: annmap, BindingSiteFinder, cellbaseR, CNEr, CNVRanger, ensembldb, extraChIPs, fishpond, GenomicRanges, gwascat, interactiveDisplay, MIRit, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 156 Package: GWAS.BAYES Version: 1.17.0 Depends: R (>= 4.3.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), limma (>= 3.54.0), stats (>= 4.2.2), MASS (>= 7.3-58.1) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP License: GPL-3 + file LICENSE MD5sum: 8240beed7ffee9b583e661db57e4b2c1 NeedsCompilation: no Title: Bayesian analysis of Gaussian GWAS data Description: This package is built to perform GWAS analysis using Bayesian techniques. Currently, GWAS.BAYES has functionality for the implementation of BICOSS (Williams, J., Ferreira, M. A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics), BGWAS (Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies." BMC Bioinformatics), and GINA. All methods currently are for the analysis of Gaussian phenotypes The research related to this package was supported in part by National Science Foundation awards DMS 1853549, DMS 1853556, and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Marco Ferreira [aut] (ORCID: ), Tieming Ji [aut] Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: devel git_last_commit: 645ae38 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GWAS.BAYES_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWAS.BAYES_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWAS.BAYES_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWAS.BAYES_1.17.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.html, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.html vignetteTitles: BICOSS, GINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.R, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.R dependencyCount: 93 Package: gwascat Version: 2.39.0 Depends: R (>= 3.5.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, dplyr, Gviz, Rsamtools, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 Archs: x64 MD5sum: 15f505e9dc87b627305eb2214c09a938 NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: devel git_last_commit: a659d47 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gwascat_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gwascat_2.39.0.zip vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 109 Package: GWASTools Version: 1.53.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel, BiocStyle, knitr License: Artistic-2.0 MD5sum: 35e335f32b53b5ead5085039a768010a NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten [aut], Cathy Laurie [aut], Tushar Bhangale [aut], Matthew P. Conomos [aut], Cecelia Laurie [aut], Michael Lawrence [aut], Caitlin McHugh [aut], Ian Painter [aut], Xiuwen Zheng [aut], Jess Shen [aut], Rohit Swarnkar [aut], Adrienne Stilp [aut], Sarah Nelson [aut], David Levine [aut], Sonali Kumari [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Stephanie M. Gogarten [cre] Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWASTools git_branch: devel git_last_commit: 804422b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GWASTools_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWASTools_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWASTools_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWASTools_1.53.0.tgz vignettes: vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf, vignettes/GWASTools/inst/doc/Affymetrix.html vignetteTitles: GWAS Data Cleaning, Data formats in GWASTools, Preparing Affymetrix Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata, snplinkage importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 93 Package: gwasurvivr Version: 1.25.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 1d5f28130207c5065e692cc7545e2201 NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: devel git_last_commit: dd7257d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gwasurvivr_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gwasurvivr_1.25.0.zip vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 143 Package: GWENA Version: 1.17.0 Depends: R (>= 4.1) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>= 4.0.4), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: 9b214ac4abefdd1b5bd7f7223358d60b NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] (ORCID: ), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: devel git_last_commit: 19342a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/GWENA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/GWENA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/GWENA_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GWENA_1.17.0.tgz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 142 Package: gypsum Version: 1.3.0 Imports: utils, httr2, jsonlite, parallel, filelock, rappdirs Suggests: knitr, rmarkdown, testthat, BiocStyle, digest, jsonvalidate, DBI, RSQLite, S4Vectors, methods License: MIT + file LICENSE MD5sum: e21eed74adb2850fa6a06ec9576a9ed9 NeedsCompilation: no Title: Interface to the gypsum REST API Description: Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details. biocViews: DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/gypsum-R VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/gypsum-R/issues git_url: https://git.bioconductor.org/packages/gypsum git_branch: devel git_last_commit: c2d990c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/gypsum_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/gypsum_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/gypsum_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/gypsum_1.3.0.tgz vignettes: vignettes/gypsum/inst/doc/userguide.html vignetteTitles: Hitting the gypsum API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gypsum/inst/doc/userguide.R importsMe: celldex, scRNAseq dependencyCount: 21 Package: h5vc Version: 2.41.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 2.13.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.99.1) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: d88661a6b6fe40b63aea1890cb2a449d NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: devel git_last_commit: 2b58d09 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/h5vc_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/h5vc_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/h5vc_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/h5vc_2.41.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 97 Package: hapFabia Version: 1.49.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 43558a996af670e27850187db87c8b5f NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: devel git_last_commit: 469b361 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hapFabia_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hapFabia_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hapFabia_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hapFabia_1.49.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 9 Package: Harman Version: 1.35.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel, methods, matrixStats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE MD5sum: a70de6b8a59093b02e4b45f06cec0369 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: devel git_last_commit: e1dee17 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Harman_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Harman_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Harman_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Harman_1.35.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser suggestsMe: HarmanData dependencyCount: 11 Package: HarmonizR Version: 1.5.0 Depends: R (>= 4.2.0) Imports: doParallel (>= 1.0.16), foreach (>= 1.5.1), janitor (>= 2.1.0), plyr (>= 1.8.6), sva (>= 3.36.0), seriation (>= 1.3.5), limma (>= 3.46.0), SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 9bd022c25b3fd617ca6d04246cd2603d NeedsCompilation: no Title: Handles missing values and makes more data available Description: An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss. biocViews: BatchEffect Author: Simon Schlumbohm [aut, cre], Julia Neumann [aut], Philipp Neumann [aut] Maintainer: Simon Schlumbohm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HarmonizR git_branch: devel git_last_commit: 024ca72 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HarmonizR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HarmonizR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HarmonizR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HarmonizR_1.5.0.tgz vignettes: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html vignetteTitles: HarmonizR_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.R dependencyCount: 112 Package: Harshlight Version: 1.79.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) MD5sum: 6ef2f45f8f9ccedcff290c7641f37f9c NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: devel git_last_commit: ed62604 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Harshlight_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Harshlight_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Harshlight_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Harshlight_1.79.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R dependencyCount: 35 Package: hca Version: 1.15.0 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache, tools, utils, digest, shiny, miniUI, DT Suggests: LoomExperiment, SummarizedExperiment, SingleCellExperiment, S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 2def23df0224ef4f6685746b5c0f1b3e NeedsCompilation: no Title: Exploring the Human Cell Atlas Data Coordinating Platform Description: This package provides users with the ability to query the Human Cell Atlas data repository for single-cell experiment data. The `projects()`, `files()`, `samples()` and `bundles()` functions retrieve summary information on each of these indexes; corresponding `*_details()` are available for individual entries of each index. File-based resources can be downloaded using `files_download()`. Advanced use of the package allows the user to page through large result sets, and to flexibly query the 'list-of-lists' structure representing query responses. biocViews: Software, SingleCell Author: Maya McDaniel [aut], Martin Morgan [aut, cre] (ORCID: ), Kayla Interdonato [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: devel git_last_commit: 2bb75df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hca_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hca_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hca_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hca_1.15.0.tgz vignettes: vignettes/hca/inst/doc/hca_manifest_vignette.html, vignettes/hca/inst/doc/hca_vignette.html vignetteTitles: Working With Human Cell Atlas Manifests, Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hca/inst/doc/hca_manifest_vignette.R, vignettes/hca/inst/doc/hca_vignette.R dependencyCount: 83 Package: HDF5Array Version: 1.35.2 Depends: R (>= 3.4), methods, SparseArray (>= 1.5.42), DelayedArray (>= 0.33.1), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>= 0.51.2), S4Vectors, IRanges, S4Arrays (>= 1.1.1) LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, RUnit, SingleCellExperiment, DelayedMatrixStats, genefilter License: Artistic-2.0 MD5sum: 9057b29160ae789b3081d939ffd4c110 NeedsCompilation: yes Title: HDF5 datasets as array-like objects in R Description: The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array SystemRequirements: GNU make BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: devel git_last_commit: 86705e1 git_last_commit_date: 2024-11-29 Date/Publication: 2024-12-01 source.ver: src/contrib/HDF5Array_1.35.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/HDF5Array_1.35.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HDF5Array_1.35.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HDF5Array_1.35.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: MAGAR, TENxBrainData, TENxPBMCData importsMe: alabaster.matrix, beachmat.hdf5, BgeeDB, biscuiteer, bsseq, Cepo, chihaya, clusterExperiment, CuratedAtlasQueryR, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, lemur, LoomExperiment, mariner, methrix, minfi, MOFA2, netSmooth, orthos, recountmethylation, scmeth, signatureSearch, SpliceWiz, TENxIO, transformGamPoi, xenLite, MafH5.gnomAD.v4.0.GRCh38, curatedTCGAData, HCAData, HCATonsilData, imcdatasets, MerfishData, MethylSeqData, orthosData, scMultiome, SingleCellMultiModal, TabulaMurisSenisData, ebvcube suggestsMe: beachmat, BiocGenerics, BiocSklearn, cellxgenedp, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MuData, MultiAssayExperiment, PDATK, QFeatures, SCArray, scMerge, scran, scry, SparseArray, spatialHeatmap, SummarizedExperiment, zellkonverter, STexampleData, spicyWorkflow, SeuratObject, SpatialDDLS dependencyCount: 26 Package: HDTD Version: 1.41.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 217f915f9f0d33aa9042cae66100d4dd NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (ORCID: ), John C. Marioni [aut] (ORCID: ), Simon Tavar\'{e} [aut] (ORCID: ) Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: devel git_last_commit: f05e068 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HDTD_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HDTD_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HDTD_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HDTD_1.41.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: hdxmsqc Version: 1.3.0 Depends: R(>= 4.3), QFeatures, S4Vectors, Spectra Imports: dplyr, tidyr, ggplot2, BiocStyle, knitr, methods, grDevices, stats, MsCoreUtils Suggests: RColorBrewer, pheatmap, MASS, patchwork, testthat License: file LICENSE Archs: x64 MD5sum: d3435039c6516e3674560cebe524fc18 NeedsCompilation: no Title: An R package for quality Control for hydrogen deuterium exchange mass spectrometry experiments Description: The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data. biocViews: QualityControl,DataImport, Proteomics, MassSpectrometry, Metabolomics Author: Oliver M. Crook [aut, cre] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/hdxmsqc VignetteBuilder: knitr BugReports: https://github.com/ococrook/hdxmsqc/issues git_url: https://git.bioconductor.org/packages/hdxmsqc git_branch: devel git_last_commit: 75e2815 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hdxmsqc_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hdxmsqc_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hdxmsqc_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hdxmsqc_1.3.0.tgz vignettes: vignettes/hdxmsqc/inst/doc/qc-streamlined.html vignetteTitles: Qualityt control for differential hydrogen deuterium exchange mass spectrometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hdxmsqc/inst/doc/qc-streamlined.R dependencyCount: 122 Package: heatmaps Version: 1.31.0 Depends: R (>= 3.5.0) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 64872a90e60068f12c49744a64153dfc NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: devel git_last_commit: 2ebde80 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/heatmaps_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/heatmaps_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/heatmaps_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/heatmaps_1.31.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 66 Package: Heatplus Version: 3.15.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) Archs: x64 MD5sum: d63b8115d5202e58f7c7d0d74c96ec0e NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: devel git_last_commit: dc4ccf1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Heatplus_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Heatplus_3.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Heatplus_3.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Heatplus_3.15.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics dependencyCount: 4 Package: HelloRanges Version: 1.33.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment, BiocIO Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 0505cfd72189a08447387c4f2344edef NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: devel git_last_commit: ee9547e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HelloRanges_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HelloRanges_1.33.0.zip vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 81 Package: HELP Version: 1.65.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) Archs: x64 MD5sum: d6ac978c90b9ccda8940d9083b42c8d5 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: devel git_last_commit: 1379b76 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HELP_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HELP_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HELP_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HELP_1.65.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 8 Package: HEM Version: 1.79.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: c2dca411ab25432c54b3b57d5d3c26c3 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: devel git_last_commit: 47fa135 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HEM_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HEM_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HEM_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HEM_1.79.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: hermes Version: 1.11.0 Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16) Imports: assertthat, Biobase, BiocGenerics, biomaRt, checkmate (>= 2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats, forcats (>= 1.0.0), GenomicRanges, ggplot2, ggrepel (>= 0.9), IRanges, lifecycle, limma, magrittr, matrixStats, methods, MultiAssayExperiment, purrr, R6, Rdpack, rlang, S4Vectors, stats, tidyr, utils Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown, statmod, testthat (>= 2.0), vdiffr License: Apache License 2.0 MD5sum: c8f8b92195ef5810566a0ae77a0d3237 NeedsCompilation: no Title: Preprocessing, analyzing, and reporting of RNA-seq data Description: Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available. biocViews: RNASeq, DifferentialExpression, Normalization, Preprocessing, QualityControl Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Alex Richardson [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut], F. Hoffmann-La Roche AG [cph, fnd] Maintainer: Daniel Sabanés Bové URL: https://github.com/insightsengineering/hermes/ VignetteBuilder: knitr BugReports: https://github.com/insightsengineering/hermes/issues git_url: https://git.bioconductor.org/packages/hermes git_branch: devel git_last_commit: 2f1b4a7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hermes_1.11.0.tar.gz vignettes: vignettes/hermes/inst/doc/hermes.html vignetteTitles: Introduction to `hermes` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hermes/inst/doc/hermes.R dependencyCount: 134 Package: HERON Version: 1.5.0 Depends: R (>= 4.4.0), SummarizedExperiment (>= 1.1.6), GenomicRanges, IRanges, S4Vectors Imports: matrixStats, stats, data.table, harmonicmeanp, metap, cluster, spdep, Matrix, limma, methods Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) Archs: x64 MD5sum: ae204cb642f6466762c668d62efd237c NeedsCompilation: no Title: Hierarchical Epitope pROtein biNding Description: HERON is a software package for analyzing peptide binding array data. In addition to identifying significant binding probes, HERON also provides functions for finding epitopes (string of consecutive peptides within a protein). HERON also calculates significance on the probe, epitope, and protein level by employing meta p-value methods. HERON is designed for obtaining calls on the sample level and calculates fractions of hits for different conditions. biocViews: Microarray, Software Author: Sean McIlwain [aut, cre] (ORCID: ), Irene Ong [aut] (ORCID: ) Maintainer: Sean McIlwain URL: https://github.com/Ong-Research/HERON VignetteBuilder: knitr BugReports: https://github.com/Ong-Research/HERON/issues git_url: https://git.bioconductor.org/packages/HERON git_branch: devel git_last_commit: a355e34 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HERON_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HERON_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HERON_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HERON_1.5.0.tgz vignettes: vignettes/HERON/inst/doc/full_analysis.html vignetteTitles: Analyzing High Density Peptide Array Data using HERON hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HERON/inst/doc/full_analysis.R dependencyCount: 86 Package: Herper Version: 1.17.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 02eff3bb7ec941338edb44b7643560f1 NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (ORCID: ), Thomas Carroll [aut, cre] (ORCID: ), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: devel git_last_commit: a708ac5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Herper_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Herper_1.17.0.tgz vignettes: vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 19 Package: HGC Version: 1.15.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: be69dfb4c0e1d29aae8a203a3be549f5 NeedsCompilation: yes Title: A fast hierarchical graph-based clustering method Description: HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer: XGlab SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: devel git_last_commit: c8f04e4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HGC_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HGC_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HGC_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HGC_1.15.0.tgz vignettes: vignettes/HGC/inst/doc/HGC.html vignetteTitles: HGC package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HGC/inst/doc/HGC.R dependencyCount: 50 Package: hiAnnotator Version: 1.41.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) MD5sum: f4c84ccc8e20355a1d22040fc9b47650 NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: devel git_last_commit: e4def22 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hiAnnotator_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hiAnnotator_1.41.0.zip vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor dependencyCount: 90 Package: HIBAG Version: 1.43.1 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown, Rsamtools License: GPL-3 MD5sum: 7bab737223a3cced04a11debeb95d0ed NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: devel git_last_commit: 4d1767d git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/HIBAG_1.43.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIBAG_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIBAG_1.43.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIBAG_1.43.1.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HicAggR Version: 1.3.0 Depends: R (>= 4.2.0) Imports: InteractionSet, BiocGenerics, BiocParallel, dplyr, GenomeInfoDb, GenomicRanges, ggplot2, grDevices, IRanges, Matrix, methods, rhdf5, rlang, rtracklayer, S4Vectors, stats, utils, strawr, tibble, stringr, tidyr, gridExtra, data.table, reshape, checkmate, purrr, withr Suggests: covr, tools, kableExtra (>= 1.3.4), knitr (>= 1.45), rmarkdown, testthat (>= 3.0.0), BiocFileCache (>= 2.6.1) License: MIT + file LICENSE MD5sum: c7aff2e8cc6294b28fdb05f649e4835a NeedsCompilation: no Title: Set of 3D genomic interaction analysis tools Description: This package provides a set of functions useful in the analysis of 3D genomic interactions. It includes the import of standard HiC data formats into R and HiC normalisation procedures. The main objective of this package is to improve the visualization and quantification of the analysis of HiC contacts through aggregation. The package allows to import 1D genomics data, such as peaks from ATACSeq, ChIPSeq, to create potential couples between features of interest under user-defined parameters such as distance between pairs of features of interest. It allows then the extraction of contact values from the HiC data for these couples and to perform Aggregated Peak Analysis (APA) for visualization, but also to compare normalized contact values between conditions. Overall the package allows to integrate 1D genomics data with 3D genomics data, providing an easy access to HiC contact values. biocViews: Software, HiC, DataImport, DataRepresentation, Normalization, Visualization, DNA3DStructure, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Robel Tesfaye [aut, ctb] (ORCID: ), David Depierre [aut], Naomi Schickele [ctb], Nicolas Chanard [aut], Refka Askri [ctb], Stéphane Schaak [aut, ctb], Pascal Martin [ctb], Olivier Cuvier [cre, ctb] (ORCID: ) Maintainer: Olivier Cuvier URL: https://bioconductor.org/packages/HicAggR, https://cuvierlab.github.io/HicAggR/, https://github.com/CuvierLab/HicAggR VignetteBuilder: knitr BugReports: https://github.com/CuvierLab/HicAggR/issues git_url: https://git.bioconductor.org/packages/HicAggR git_branch: devel git_last_commit: 1905b6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HicAggR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HicAggR_1.3.0.zip vignettes: vignettes/HicAggR/inst/doc/HicAggR.html vignetteTitles: HicAggR - In depth tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HicAggR/inst/doc/HicAggR.R dependencyCount: 103 Package: HiCBricks Version: 1.25.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, GenomeInfoDb, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: b60ff97b743516c921e2951ac87b5649 NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: devel git_last_commit: 51b1cc8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiCBricks_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCBricks_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCBricks_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCBricks_1.25.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R importsMe: bnbc dependencyCount: 90 Package: HiCcompare Version: 1.29.0 Depends: R (>= 3.5.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: e0630fb56464ecd9ca04fdbfc2d2fd76 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: devel git_last_commit: 07988eb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiCcompare_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCcompare_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCcompare_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCcompare_1.29.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, SpectralTAD, TADCompare dependencyCount: 85 Package: HiCDCPlus Version: 1.15.0 Imports: Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors LinkingTo: Rcpp Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix, BiocFileCache, rappdirs Enhances: parallel License: GPL-3 Archs: x64 MD5sum: 2003eaa7ca546056ee12e1723c122f44 NeedsCompilation: yes Title: Hi-C Direct Caller Plus Description: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation. biocViews: HiC, DNA3DStructure, Software, Normalization Author: Merve Sahin [cre, aut] (ORCID: ) Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: devel git_last_commit: cd71b78 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiCDCPlus_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCDCPlus_1.15.0.zip vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R dependencyCount: 169 Package: HiCDOC Version: 1.9.2 Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>= 4.1.0) Imports: methods, ggplot2, Rcpp (>= 0.12.8), stats, S4Vectors, gtools, pbapply, BiocParallel, BiocGenerics, grid, cowplot, gridExtra, data.table, multiHiCcompare, GenomeInfoDb LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, BiocManager, rhdf5 License: LGPL-3 MD5sum: 3791e422ff3875616f1914a8b69a0960 NeedsCompilation: yes Title: A/B compartment detection and differential analysis Description: HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. biocViews: HiC, DNA3DStructure, Normalization, Sequencing, Software, Clustering Author: Kurylo Cyril [aut], Zytnicki Matthias [aut], Foissac Sylvain [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/mzytnicki/HiCDOC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/mzytnicki/HiCDOC/issues git_url: https://git.bioconductor.org/packages/HiCDOC git_branch: devel git_last_commit: 43dadba git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-12 source.ver: src/contrib/HiCDOC_1.9.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCDOC_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCDOC_1.9.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCDOC_1.9.2.tgz vignettes: vignettes/HiCDOC/inst/doc/HiCDOC.html vignetteTitles: HiCDOC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDOC/inst/doc/HiCDOC.R importsMe: treediff dependencyCount: 96 Package: HiCExperiment Version: 1.7.0 Depends: R (>= 4.2) Imports: InteractionSet, strawr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, BiocGenerics, BiocIO, BiocParallel, methods, rhdf5, Matrix, vroom, dplyr, stats Suggests: HiContacts, HiContactsData, BiocFileCache, rtracklayer, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 87df9494d039ac01483da24ef8045797 NeedsCompilation: no Title: Bioconductor class for interacting with Hi-C files in R Description: R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCExperiment VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCExperiment/issues git_url: https://git.bioconductor.org/packages/HiCExperiment git_branch: devel git_last_commit: 5db289f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiCExperiment_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiCExperiment_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCExperiment_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiCExperiment_1.7.0.tgz vignettes: vignettes/HiCExperiment/inst/doc/HiCExperiment.html vignetteTitles: Introduction to HiCExperiment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCExperiment/inst/doc/HiCExperiment.R dependsOnMe: HiContacts, HiCool, DNAZooData importsMe: fourDNData, OHCA dependencyCount: 75 Package: HiContacts Version: 1.9.0 Depends: R (>= 4.2), HiCExperiment Imports: InteractionSet, SummarizedExperiment, GenomicRanges, IRanges, GenomeInfoDb, S4Vectors, methods, BiocGenerics, BiocIO, BiocParallel, RSpectra, Matrix, tibble, tidyr, dplyr, readr, stringr, ggplot2, ggrastr, scales, stats, utils Suggests: HiContactsData, rtracklayer, GenomicFeatures, Biostrings, BSgenome.Scerevisiae.UCSC.sacCer3, WGCNA, Rfast, terra, patchwork, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 0b1df6d0a448ae54d63d9f6f767279e2 NeedsCompilation: no Title: Analysing cool files in R with HiContacts Description: HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment. biocViews: HiC, DNA3DStructure Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiContacts VignetteBuilder: knitr BugReports: https://github.com/js2264/HiContacts/issues git_url: https://git.bioconductor.org/packages/HiContacts git_branch: devel git_last_commit: 8328e67 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiContacts_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiContacts_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiContacts_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiContacts_1.9.0.tgz vignettes: vignettes/HiContacts/inst/doc/HiContacts.html vignetteTitles: Introduction to HiContacts hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiContacts/inst/doc/HiContacts.R importsMe: OHCA suggestsMe: HiCExperiment, HiCool dependencyCount: 107 Package: HiCool Version: 1.7.0 Depends: R (>= 4.2), HiCExperiment Imports: BiocIO, S4Vectors, GenomicRanges, IRanges, InteractionSet, vroom, basilisk, reticulate, rmarkdown, rmdformats, plotly, dplyr, stringr, sessioninfo, utils Suggests: HiContacts, HiContactsData, AnnotationHub, BiocFileCache, BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 80bbd38dd71838be0a0a254b04160170 NeedsCompilation: no Title: HiCool Description: HiCool provides an R interface to process and normalize Hi-C paired-end fastq reads into .(m)cool files. .(m)cool is a compact, indexed HDF5 file format specifically tailored for efficiently storing HiC-based data. On top of processing fastq reads, HiCool provides a convenient reporting function to generate shareable reports summarizing Hi-C experiments and including quality controls. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCool VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCool/issues git_url: https://git.bioconductor.org/packages/HiCool git_branch: devel git_last_commit: 639be54 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiCool_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiCool_1.7.0.tgz vignettes: vignettes/HiCool/inst/doc/HiCool.html vignetteTitles: HiCool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCool/inst/doc/HiCool.R importsMe: OHCA dependencyCount: 132 Package: hicVennDiagram Version: 1.5.0 Depends: R (>= 4.3.0) Imports: GenomeInfoDb, GenomicRanges, IRanges, InteractionSet, rtracklayer, ggplot2, ComplexUpset, reshape2, eulerr, S4Vectors, methods, utils, htmlwidgets, svglite Suggests: BiocStyle, knitr, rmarkdown, testthat, ChIPpeakAnno, grid, TxDb.Hsapiens.UCSC.hg38.knownGene License: GPL-3 Archs: x64 MD5sum: 14c2cd47d3a3cf813f37e7d12b34cdce NeedsCompilation: no Title: Venn Diagram for genomic interaction data Description: A package to generate high-resolution Venn and Upset plots for genomic interaction data from HiC, ChIA-PET, HiChIP, PLAC-Seq, Hi-TrAC, HiCAR and etc. The package generates plots specifically crafted to eliminate the deceptive visual representation caused by the counts method. biocViews: DNA3DStructure, HiC, Visualization Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/hicVennDiagram VignetteBuilder: knitr BugReports: https://github.com/jianhong/hicVennDiagram/issues git_url: https://git.bioconductor.org/packages/hicVennDiagram git_branch: devel git_last_commit: f6b6c77 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hicVennDiagram_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hicVennDiagram_1.5.0.zip vignettes: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.html vignetteTitles: hicVennDiagram Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.R dependencyCount: 119 Package: hierGWAS Version: 1.37.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: f34e1edb03571790caa113a908f2ec28 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: devel git_last_commit: 06911bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hierGWAS_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hierGWAS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hierGWAS_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hierGWAS_1.37.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 19 Package: hierinf Version: 1.25.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: b698b6f920b3cc2e9d23e8df219e1dff NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: devel git_last_commit: 0124639 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hierinf_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hierinf_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hierinf_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hierinf_1.25.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 19 Package: HilbertCurve Version: 2.1.0 Depends: R (>= 4.0.0), grid Imports: methods, utils, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr, Rcpp LinkingTo: Rcpp Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE MD5sum: f74cd989521a322b403c568cf4c9caed NeedsCompilation: yes Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve, https://jokergoo.github.io/HilbertCurve/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: devel git_last_commit: 32b4f09 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HilbertCurve_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HilbertCurve_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HilbertCurve_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HilbertCurve_2.1.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: The HilbertCurve package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap dependencyCount: 33 Package: HilbertVis Version: 1.65.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: x64 MD5sum: 344126afb55ca55662be7c7d40c14ab7 NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: devel git_last_commit: 4c32450 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HilbertVis_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HilbertVis_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HilbertVis_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HilbertVis_1.65.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR dependencyCount: 6 Package: HilbertVisGUI Version: 1.65.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 20533513836de7a80b49aefaa62c01e8 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: devel git_last_commit: 42a8573 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HilbertVisGUI_1.65.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.21.0 Depends: R(>= 4.1), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: df84f2f52c9d85330421b431794f29e1 NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.0.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: devel git_last_commit: 09c4796 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiLDA_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiLDA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HiLDA_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HiLDA_1.21.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 115 Package: hipathia Version: 3.7.0 Depends: R (>= 4.1), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors, ggplot2, ggpubr, dplyr, tibble, visNetwork, reshape2, MetBrewer Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 07d352e397f623fe73e6a6c7e4f2951d NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: devel git_last_commit: eda829a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hipathia_3.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hipathia_3.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hipathia_3.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hipathia_3.7.0.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 161 Package: HIPPO Version: 1.19.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 0e70e9fd11abdc4e3828e969e64c2a69 NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: devel git_last_commit: 3720af6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HIPPO_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIPPO_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIPPO_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIPPO_1.19.0.tgz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 84 Package: hiReadsProcessor Version: 1.43.0 Depends: R (>= 3.5.0), Biostrings, pwalign, GenomicAlignments, BiocParallel, hiAnnotator Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat, markdown License: GPL-3 MD5sum: b80d2d7b95e06d07082c8b9f448a340c NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiReadsProcessor git_branch: devel git_last_commit: b964203 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hiReadsProcessor_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hiReadsProcessor_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hiReadsProcessor_1.43.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R dependencyCount: 99 Package: HIREewas Version: 1.25.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: 4aabd8e5e4fb80bd69508c2a7faac4c5 NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: devel git_last_commit: bf30fc5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HIREewas_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HIREewas_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HIREewas_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HIREewas_1.25.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiTC Version: 1.51.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90, BSgenome.Hsapiens.UCSC.hg18 License: Artistic-2.0 MD5sum: a1ca38bd3c9534d76f952528e4dec185 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: devel git_last_commit: 1718634 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HiTC_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HiTC_1.51.0.zip vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust dependencyCount: 60 Package: hmdbQuery Version: 1.27.0 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 MD5sum: 79a222938f6a8b2587d58a0f7c6df2be NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: devel git_last_commit: cca0a20 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hmdbQuery_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hmdbQuery_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hmdbQuery_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hmdbQuery_1.27.0.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 9 Package: HMMcopy Version: 1.49.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 Archs: x64 MD5sum: 1e18b2621b069075ea77d334344211c0 NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: devel git_last_commit: 9dbb51b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HMMcopy_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HMMcopy_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HMMcopy_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HMMcopy_1.49.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: HoloFoodR Version: 1.1.0 Depends: R(>= 4.4.0), TreeSummarizedExperiment, MultiAssayExperiment Imports: dplyr, httr2, jsonlite, S4Vectors, stats, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 097d6c3d4fbc03d2782c6a5f5d0eb8a4 NeedsCompilation: no Title: R interface to EBI HoloFood resource Description: Utility package to facilitate integration and analysis of EBI HoloFood data in R. This package streamlines access to the resource, allowing for direct loading of data into formats optimized for downstream analytics. biocViews: Software, Infrastructure, DataImport Author: Tuomas Borman [aut, cre] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/HoloFoodR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/HoloFoodR/issues git_url: https://git.bioconductor.org/packages/HoloFoodR git_branch: devel git_last_commit: 5175106 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HoloFoodR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HoloFoodR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HoloFoodR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HoloFoodR_1.1.0.tgz vignettes: vignettes/HoloFoodR/inst/doc/HoloFoodR.html vignetteTitles: HoloFoodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HoloFoodR/inst/doc/HoloFoodR.R dependencyCount: 81 Package: hoodscanR Version: 1.5.1 Depends: R (>= 4.3) Imports: knitr, rmarkdown, SpatialExperiment, SummarizedExperiment, circlize, ComplexHeatmap, scico, rlang, utils, ggplot2, grid, methods, stats, RANN, Rcpp (>= 1.0.9) LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 + file LICENSE Archs: x64 MD5sum: 50060296c051f2e81d581ed8d8666c9f NeedsCompilation: yes Title: Spatial cellular neighbourhood scanning in R Description: hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering. biocViews: Spatial, Transcriptomics, SingleCell, Clustering Author: Ning Liu [aut, cre] (ORCID: ), Jarryd Martin [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/hoodscanR, https://davislaboratory.github.io/hoodscanR/ VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/hoodscanR/issues git_url: https://git.bioconductor.org/packages/hoodscanR git_branch: devel git_last_commit: 8600f1c git_last_commit_date: 2024-12-04 Date/Publication: 2024-12-05 source.ver: src/contrib/hoodscanR_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/hoodscanR_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hoodscanR_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hoodscanR_1.5.1.tgz vignettes: vignettes/hoodscanR/inst/doc/Quick_start.html vignetteTitles: hoodscanR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hoodscanR/inst/doc/Quick_start.R dependencyCount: 118 Package: hopach Version: 2.67.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 55279eba3e6c6e7b9819a5dbd7926105 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: devel git_last_commit: 1e51949 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hopach_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hopach_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hopach_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hopach_2.67.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess dependencyCount: 9 Package: HPAanalyze Version: 1.25.0 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, markdown, devtools, BiocStyle License: GPL-3 + file LICENSE MD5sum: 2876647d9806621ec1e5b0f030a9c921 NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran URL: https://github.com/anhtr/HPAanalyze VignetteBuilder: knitr BugReports: https://github.com/anhtr/HPAanalyze/issues git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: devel git_last_commit: d90d2c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HPAanalyze_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HPAanalyze_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HPAanalyze_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HPAanalyze_1.25.0.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function with HPAanalyze", "2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human Protein Atlas (HPA) queries", "4. Tutorial: Working with Human Protein Atlas (HPA) xml files offline", "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 45 Package: hpar Version: 1.49.0 Depends: R (>= 3.5.0) Imports: utils, ExperimentHub Suggests: org.Hs.eg.db, GO.db, AnnotationDbi, knitr, BiocStyle, testthat, rmarkdown, dplyr, DT License: Artistic-2.0 Archs: x64 MD5sum: a36c8779696ea3bc0b5ee98c3cc39cc6 NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, CellBiology, DataImport, FunctionalGenomics, SystemsBiology, ExperimentHubSoftware Author: Laurent Gatto [cre, aut] (ORCID: ), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: devel git_last_commit: 1c9e5d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hpar_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hpar_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hpar_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hpar_1.49.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: pRoloc, RforProteomics dependencyCount: 67 Package: HPiP Version: 1.13.0 Depends: R (>= 4.1) Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE MD5sum: 6b72504483f6b537a67f4f2373feefd2 NeedsCompilation: no Title: Host-Pathogen Interaction Prediction Description: HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations. biocViews: Proteomics, SystemsBiology, NetworkInference, StructuralPrediction, GenePrediction, Network Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/HPiP VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/HPiP/issues git_url: https://git.bioconductor.org/packages/HPiP git_branch: devel git_last_commit: 65e718f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HPiP_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HPiP_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HPiP_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HPiP_1.13.0.tgz vignettes: vignettes/HPiP/inst/doc/HPiP_tutorial.html vignetteTitles: Introduction to HPiP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPiP/inst/doc/HPiP_tutorial.R dependencyCount: 107 Package: HTSFilter Version: 1.47.0 Depends: R (>= 4.0.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 0500aad07d4ed2ec5fc62eaa4341fca3 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: devel git_last_commit: e83b4d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HTSFilter_1.47.0.tar.gz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster dependencyCount: 80 Package: HuBMAPR Version: 1.1.4 Depends: R (>= 4.4.0) Imports: httr2, dplyr, tidyr, tibble, rjsoncons, rlang, utils, stringr, whisker, purrr Suggests: testthat (>= 3.0.0), knitr, ggplot2, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 7297e1500f7242ad25e8594d930568d8 NeedsCompilation: no Title: Interface to 'HuBMAP' Description: 'HuBMAP' provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`. biocViews: Software, SingleCell, DataImport, ThirdPartyClient, Spatial, Infrastructure Author: Christine Hou [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Christine Hou URL: https://christinehou11.github.io/HuBMAPR/, https://github.com/christinehou11/HuBMAPR VignetteBuilder: knitr BugReports: https://github.com/christinehou11/HuBMAPR/issues git_url: https://git.bioconductor.org/packages/HuBMAPR git_branch: devel git_last_commit: 33fd90f git_last_commit_date: 2024-11-03 Date/Publication: 2024-11-03 source.ver: src/contrib/HuBMAPR_1.1.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/HuBMAPR_1.1.4.zip vignettes: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.html vignetteTitles: Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.R dependencyCount: 35 Package: HubPub Version: 1.15.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 Archs: x64 MD5sum: a3b2193393df55ccb8e912ca16a35b80 NeedsCompilation: no Title: Utilities to create and use Bioconductor Hubs Description: HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket. biocViews: DataImport, Infrastructure, Software, ThirdPartyClient Author: Kayla Interdonato [aut, cre], Martin Morgan [aut] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: devel git_last_commit: df312ca git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/HubPub_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HubPub_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HubPub_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HubPub_1.15.0.tgz vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html, vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub, HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R, vignettes/HubPub/inst/doc/HubPub.R suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub, ExperimentHubData dependencyCount: 77 Package: hummingbird Version: 1.17.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: 3f1a24c568f16a0400df56ce42ba123b NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: devel git_last_commit: 8a0488f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hummingbird_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hummingbird_1.17.0.zip vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 38 Package: HybridExpress Version: 1.3.0 Depends: R (>= 4.3.0) Imports: ggplot2, patchwork, rlang, DESeq2, SummarizedExperiment, stats, methods, RColorBrewer, ComplexHeatmap, grDevices, BiocParallel Suggests: BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: 10597b654d2f90371966642513caec12 NeedsCompilation: no Title: Comparative analysis of RNA-seq data for hybrids and their progenitors Description: HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature. biocViews: Software, FunctionalGenomics, GeneExpression, Transcriptomics, RNASeq, Classification, DifferentialExpression Author: Fabricio Almeida-Silva [aut, cre] (ORCID: ), Lucas Prost-Boxoen [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/HybridExpress VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/HybridExpress git_url: https://git.bioconductor.org/packages/HybridExpress git_branch: devel git_last_commit: 7c5952c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HybridExpress_1.3.0.tar.gz vignettes: vignettes/HybridExpress/inst/doc/HybridExpress.html vignetteTitles: Comparative transcriptomic analysis of hybrids and their progenitors hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridExpress/inst/doc/HybridExpress.R dependencyCount: 91 Package: HybridMTest Version: 1.51.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later Archs: x64 MD5sum: 5d39e4e60a51d08bd4d258c9c985f504 NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: devel git_last_commit: a4de83e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/HybridMTest_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/HybridMTest_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/HybridMTest_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/HybridMTest_1.51.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R importsMe: APAlyzer dependencyCount: 15 Package: hypeR Version: 2.5.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny, BiocStyle Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9dc7205d966b3e13d0125b904fd54253 NeedsCompilation: no Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Andrew Chen [aut], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: devel git_last_commit: 2152271 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hypeR_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hypeR_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hypeR_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hypeR_2.5.0.tgz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 101 Package: hyperdraw Version: 1.59.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: e0d43591b53642a3702a9378e10bd2fe NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: devel git_last_commit: b1686fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hyperdraw_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hyperdraw_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hyperdraw_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hyperdraw_1.59.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR dependencyCount: 12 Package: hypergraph Version: 1.79.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: ee7fd25509e00cb6d04ba93fd9f4b220 NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: devel git_last_commit: d88b9b3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/hypergraph_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/hypergraph_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/hypergraph_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/hypergraph_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw dependencyCount: 8 Package: iASeq Version: 1.51.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 983607606a0d80f499f84f43d72e6a92 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: devel git_last_commit: 5d875d8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iASeq_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iASeq_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iASeq_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iASeq_1.51.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.25.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 9b3019881850ade3b2193b62c2552e73 NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: devel git_last_commit: f02ee47 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iasva_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iasva_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iasva_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iasva_1.25.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 49 Package: iBBiG Version: 1.51.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: 0905aee1dcd57fe89f9283c868b10e79 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: devel git_last_commit: 370a007 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iBBiG_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iBBiG_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iBBiG_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iBBiG_1.51.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 57 Package: ibh Version: 1.55.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) Archs: x64 MD5sum: 3a81381c6440acf97eadf13924a4aed4 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: devel git_last_commit: 946a7be git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ibh_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ibh_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ibh_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ibh_1.55.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.47.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: a545e1d14e55fa3853dff76aa871b347 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: devel git_last_commit: f5a261f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iBMQ_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iBMQ_1.47.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 38 Package: iCARE Version: 1.35.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE MD5sum: 0e9725db4a47d7c5fefb5677c55b6266 NeedsCompilation: yes Title: Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to build, validate and apply absolute risk models biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Parichoy Pal Choudhury, Paige Maas, William Wheeler, Nilanjan Chatterjee Maintainer: Parichoy Pal Choudhury git_url: https://git.bioconductor.org/packages/iCARE git_branch: devel git_last_commit: 6ae8860 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iCARE_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCARE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCARE_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCARE_1.35.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 75 Package: Icens Version: 1.79.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: b83efacf90773a1b038a5cee3524cfdd NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: devel git_last_commit: 19e9542 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Icens_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Icens_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Icens_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Icens_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.25.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE MD5sum: 7817515f9ddaf10ead9fb7ca79cddb78 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: devel git_last_commit: 795af55 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/icetea_1.25.0.tar.gz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 118 Package: iCheck Version: 1.37.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: b0e307c80db1be2d39dcad2d96c8221c NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: devel git_last_commit: 3a391bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iCheck_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCheck_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCheck_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCheck_1.37.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 189 Package: iChip Version: 1.61.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: 602222977534c27e9e2f41cbc8cb2853 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: devel git_last_commit: c3b6d48 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iChip_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iChip_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iChip_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iChip_1.61.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 7 Package: iClusterPlus Version: 1.43.1 Depends: R (>= 4.1.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: ccf67c3c2c26187a450952c38516f268 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Multi-omics, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: devel git_last_commit: 7a3601f git_last_commit_date: 2024-11-22 Date/Publication: 2024-11-24 source.ver: src/contrib/iClusterPlus_1.43.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/iClusterPlus_1.43.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iClusterPlus_1.43.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iClusterPlus_1.43.1.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.27.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 Archs: x64 MD5sum: b18c0c5d596240fb921148bfb6a0cf5f NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: devel git_last_commit: 3beaddb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iCNV_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCNV_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCNV_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCNV_1.27.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 101 Package: iCOBRA Version: 1.35.0 Depends: R (>= 4.4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 3.4.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR, markdown, utils, rlang Suggests: knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: a0b7b797a78e8384ed3ad67af75359ce NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results. biocViews: Classification, Visualization Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/iCOBRA VignetteBuilder: knitr BugReports: https://github.com/csoneson/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: devel git_last_commit: fe3014f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iCOBRA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iCOBRA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iCOBRA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iCOBRA_1.35.0.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat dependencyCount: 90 Package: ideal Version: 2.1.1 Depends: topGO Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, graphics, base64enc, methods, utils, stats Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, htmltools, edgeR License: MIT + file LICENSE MD5sum: 2f10c9b0e75410951802b60c3b078c4a NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: devel git_last_commit: 38ec392 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/ideal_2.1.1.tar.gz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 236 Package: IdeoViz Version: 1.43.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 Archs: x64 MD5sum: a1c336c595cdf718138ed8ee5ea0265d NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: devel git_last_commit: b8543cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IdeoViz_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IdeoViz_1.43.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 60 Package: idiogram Version: 1.83.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 Archs: x64 MD5sum: ea355153a09177abbc5c02212cd0ca46 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: devel git_last_commit: fca554d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/idiogram_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idiogram_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idiogram_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idiogram_1.83.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 50 Package: idpr Version: 1.17.0 Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, pwalign, msa, ape, testthat, seqinr License: LGPL (>= 3) MD5sum: 631de9c69b10fafc0cacbe655cc451a1 NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. Described in McFadden WM & Yanowitz JL (2022). "idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R." PloS one, 17(4), e0266929. . biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: devel git_last_commit: 91e32e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/idpr_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idpr_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idpr_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idpr_1.17.0.tgz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 59 Package: idr2d Version: 1.21.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 488ed81955108ab84b743ce346960849 NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), David Gifford [ths, cph] (ORCID: ) Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: devel git_last_commit: 43c8e8a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/idr2d_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/idr2d_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/idr2d_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/idr2d_1.21.0.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 70 Package: IFAA Version: 1.9.0 Depends: R (>= 4.2.0), Imports: mathjaxr, doRNG, foreach (>= 1.4.3), Matrix (>= 1.4-0), HDCI (>= 1.0-2), parallel (>= 3.3.0), doParallel (>= 1.0.11), parallelly , glmnet, stats, utils, SummarizedExperiment, stringr, S4Vectors, DescTools, MatrixExtra, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: x64 MD5sum: bdf772320600f2cad0e79b084be9d9c9 NeedsCompilation: no Title: Robust Inference for Absolute Abundance in Microbiome Analysis Description: This package offers a robust approach to make inference on the association of covariates with the absolute abundance (AA) of microbiome in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA because the ratio of two RA is equal to the ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential confounders. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. False discovery rate is automatically controlled by this approach. Zeros do not need to be imputed by a positive value for the analysis. The IFAA package also offers the 'MZILN' function for estimating and testing associations of abundance ratios with covariates. biocViews: Software, Technology, Sequencing, Microbiome, Regression Author: Quran Wu [aut], Zhigang Li [aut, cre] Maintainer: Zhigang Li URL: https://pubmed.ncbi.nlm.nih.gov/35241863/, https://pubmed.ncbi.nlm.nih.gov/30923584/, https://github.com/quranwu/IFAA VignetteBuilder: knitr BugReports: https://github.com/quranwu/IFAA/issues git_url: https://git.bioconductor.org/packages/IFAA git_branch: devel git_last_commit: aaff157 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IFAA_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IFAA_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IFAA_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IFAA_1.9.0.tgz vignettes: vignettes/IFAA/inst/doc/IFAA.pdf vignetteTitles: IFAA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IFAA/inst/doc/IFAA.R dependencyCount: 103 Package: iGC Version: 1.37.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 Archs: x64 MD5sum: 02a13bebc0c5ff6d64c10304cd96b2d6 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: devel git_last_commit: 9bbfe28 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iGC_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iGC_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iGC_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iGC_1.37.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.21.0 Depends: R (>= 4.2.0) Imports: methods, reshape2 (>= 1.4.3), Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.2.0), SummarizedExperiment, tidyr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, ggrepel, patchwork License: MIT + file LICENSE MD5sum: eabf6a2295c976be0bce0973e18b99fe NeedsCompilation: yes Title: Differential gene usage in immune repertoires Description: Detection of biases in the usage of immunoglobulin (Ig) genes is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage between biological conditions using a probabilistic model which is analyzed computationally by Bayes inference. With this IgGeneUsage also avoids some common problems related to the current practice of null-hypothesis significance testing. biocViews: DifferentialExpression, Regression, Genetics, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/IgGeneUsage SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: devel git_last_commit: 63e09a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IgGeneUsage_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IgGeneUsage_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IgGeneUsage_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IgGeneUsage_1.21.0.tgz vignettes: vignettes/IgGeneUsage/inst/doc/User_Manual.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/User_Manual.R dependencyCount: 95 Package: igvR Version: 1.27.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.17.1) Imports: methods, BiocGenerics, httpuv, utils, rtracklayer, VariantAnnotation, RColorBrewer, httr Suggests: RUnit, BiocStyle, knitr, rmarkdown, MotifDb, seqLogo License: MIT + file LICENSE MD5sum: 2b627e18c27e4966e97c358d1919b115 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: devel git_last_commit: e490c15 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/igvR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/igvR_1.27.0.zip vignettes: vignettes/igvR/inst/doc/v00.basicIntro.html, vignettes/igvR/inst/doc/v01.stockGenome.html, vignettes/igvR/inst/doc/v02.customGenome.html, vignettes/igvR/inst/doc/v03.ctcfChIP.html, vignettes/igvR/inst/doc/v04.pairedEnd.html, vignettes/igvR/inst/doc/v05.ucscTableBrowser.html, vignettes/igvR/inst/doc/v06.annotationHub.html, vignettes/igvR/inst/doc/v07.gwas.html vignetteTitles: "Introduction: a simple demo", "Use a Stock Genome", "Use a Custom Genome", "Explore CTCF ChIP-seq alignments,, MACS2 narrowPeaks,, Motif Matching and H3K4me3 methylation", "Paired-end Interaction Tracks", "Obtain and Display H3K4Me3 K562 track from UCSC table browser", "Obtain and Display H3K27ac K562 track from the AnnotationHub", "GWAS Tracks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/v00.basicIntro.R, vignettes/igvR/inst/doc/v01.stockGenome.R, vignettes/igvR/inst/doc/v02.customGenome.R, vignettes/igvR/inst/doc/v03.ctcfChIP.R, vignettes/igvR/inst/doc/v04.pairedEnd.R, vignettes/igvR/inst/doc/v05.ucscTableBrowser.R, vignettes/igvR/inst/doc/v06.annotationHub.R, vignettes/igvR/inst/doc/v07.gwas.R dependencyCount: 87 Package: igvShiny Version: 1.3.0 Depends: R (>= 3.5.0), GenomicRanges, methods, shiny Imports: BiocGenerics, checkmate, futile.logger, GenomeInfoDbData, htmlwidgets, httr, jsonlite, randomcoloR, utils Suggests: BiocStyle, GenomicAlignments, knitr, Rsamtools, rtracklayer, RUnit, shinytest2, VariantAnnotation License: MIT + file LICENSE MD5sum: 59482c9300e3063e708675e921b0e7b2 NeedsCompilation: no Title: igvShiny: a wrapper of Integrative Genomics Viewer (IGV - an interactive tool for visualization and exploration integrated genomic data) Description: This package is a wrapper of Integrative Genomics Viewer (IGV). It comprises an htmlwidget version of IGV. It can be used as a module in Shiny apps. biocViews: Software, ShinyApps, Sequencing, Coverage Author: Paul Shannon [aut], Arkadiusz Gladki [aut, cre] (ORCID: ), Karolina Scigocka [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gladkia/igvShiny, https://gladkia.github.io/igvShiny/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/igvShiny/issues git_url: https://git.bioconductor.org/packages/igvShiny git_branch: devel git_last_commit: 9a89fc1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/igvShiny_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/igvShiny_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/igvShiny_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/igvShiny_1.3.0.tgz vignettes: vignettes/igvShiny/inst/doc/igvShiny.html vignetteTitles: igvShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvShiny/inst/doc/igvShiny.R dependencyCount: 80 Package: IHW Version: 1.35.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: 057a314663cb054ed6a6bc49ffe59f4c NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: devel git_last_commit: a1c2d79 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IHW_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IHW_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IHW_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IHW_1.35.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal, scp suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, BloodCancerMultiOmics2017, BisRNA, DGEobj.utils dependencyCount: 10 Package: illuminaio Version: 0.49.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: 4fc6b066a9ab10c6d1250aaecbee2bba NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: devel git_last_commit: 6a685ff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/illuminaio_0.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/illuminaio_0.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/illuminaio_0.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/illuminaio_0.49.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: beadarray, bigmelon, crlmm, methylumi, minfi suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.17.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 222c77945ffae592bc514f9d41f64e8e NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: devel git_last_commit: 8522e63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ILoReg_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ILoReg_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ILoReg_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ILoReg_1.17.0.tgz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 133 Package: IMAS Version: 1.31.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 MD5sum: befcd5c3a470d97ab382e5e75823553a NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: devel git_last_commit: 807fef2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IMAS_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IMAS_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IMAS_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IMAS_1.31.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R dependencyCount: 117 Package: imcRtools Version: 1.13.0 Depends: R (>= 4.1), SpatialExperiment Imports: S4Vectors, stats, utils, SummarizedExperiment, methods, pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind, BiocParallel, viridis, dplyr, magrittr, DT, igraph, SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph, tidygraph, ggplot2, data.table, sf, concaveman, tidyselect, distances, MatrixGenerics, rlang, grDevices Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: ec87981b33f4860013871cea3d6eee7f NeedsCompilation: no Title: Methods for imaging mass cytometry data analysis Description: This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected. biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering Author: Nils Eling [aut], Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb, cre] (ORCID: ), Lasse Meyer [ctb] Maintainer: Daniel Schulz URL: https://github.com/BodenmillerGroup/imcRtools VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/imcRtools/issues git_url: https://git.bioconductor.org/packages/imcRtools git_branch: devel git_last_commit: 19f5a4b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/imcRtools_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/imcRtools_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/imcRtools_1.13.0.tgz vignettes: vignettes/imcRtools/inst/doc/imcRtools.html vignetteTitles: "Tools for IMC data analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R suggestsMe: spicyR dependencyCount: 185 Package: IMMAN Version: 1.27.0 Imports: STRINGdb, pwalign, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 9e11c73ae21aa245f81bfbdfe39c15ed NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: devel git_last_commit: b80f1c2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IMMAN_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IMMAN_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IMMAN_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IMMAN_1.27.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 72 Package: immunoClust Version: 1.39.3 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: 3c632394dce4643b168206bce4ced837 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen [aut, cre] Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: devel git_last_commit: 17b4bb8 git_last_commit_date: 2024-11-25 Date/Publication: 2024-11-25 source.ver: src/contrib/immunoClust_1.39.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/immunoClust_1.39.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immunoClust_1.39.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immunoClust_1.39.3.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 20 Package: immunogenViewer Version: 1.1.0 Depends: R (>= 4.0) Imports: ggplot2, httr, jsonlite, patchwork, UniProt.ws Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), DT License: Apache License (>= 2) MD5sum: 7623f596eb29430dc0b81622aaa669a0 NeedsCompilation: no Title: Visualization and evaluation of protein immunogens Description: Plots protein properties and visualizes position of peptide immunogens within protein sequence. Allows evaluation of immunogens based on structural and functional annotations to infer suitability for antibody-based methods aiming to detect native proteins. biocViews: FeatureExtraction, Proteomics, Software, Visualization Author: Katharina Waury [aut, cre] (ORCID: ) Maintainer: Katharina Waury URL: https://github.com/kathiwaury/immunogenViewer VignetteBuilder: knitr BugReports: https://github.com/kathiwaury/immunogenViewer/issues git_url: https://git.bioconductor.org/packages/immunogenViewer git_branch: devel git_last_commit: a57d054 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/immunogenViewer_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/immunogenViewer_1.1.0.zip vignettes: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.html vignetteTitles: Using immunogenViewer to evaluate and choose antibodies hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.R dependencyCount: 87 Package: immunotation Version: 1.15.0 Depends: R (>= 4.1) Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr, xml2, maps, rlang Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT License: GPL-3 MD5sum: 7761beb3d3f07f2747930b2cf3717e78 NeedsCompilation: no Title: Tools for working with diverse immune genes Description: MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database. biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics, Annotation Author: Katharina Imkeller [cre, aut] Maintainer: Katharina Imkeller VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: devel git_last_commit: 6821974 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/immunotation_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/immunotation_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immunotation_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immunotation_1.15.0.tgz vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html vignetteTitles: User guide immunotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R dependencyCount: 66 Package: IMPCdata Version: 1.43.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE Archs: x64 MD5sum: 9f67c6aaef588f549f4c176f254b8088 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: devel git_last_commit: 85978f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IMPCdata_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IMPCdata_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IMPCdata_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IMPCdata_1.43.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.81.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 6c0c74800705cf0a04f7500770f571c4 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: devel git_last_commit: bd0db5e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/impute_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/impute_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/impute_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/impute_1.81.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, cola, DExMA, doppelgangR, EGAD, EpiMix, fastLiquidAssociation, genefu, genomation, GEOexplorer, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, DIscBIO, ePCR, FAMT, GSEMA, iC10, lilikoi, mi4p, PCAPAM50, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, DAPAR, GeoTcgaData, graphite, MethPed, MsCoreUtils, QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler, TCGAutils, DDPNA, GSA, maGUI, MetChem, romic dependencyCount: 0 Package: INDEED Version: 2.21.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: 1682548f1f12fb64757be840192ecd6b NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: devel git_last_commit: c505f83 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/INDEED_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INDEED_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INDEED_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INDEED_2.21.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 106 Package: iNETgrate Version: 1.5.0 Depends: R (>= 4.3.0), BiocStyle (>= 2.18.1) Imports: SummarizedExperiment, GenomicRanges (>= 1.24.1), stats, WGCNA, grDevices, graphics, survival, igraph, Pigengene (>= 1.19.26), Homo.sapiens, glmnet, caret, gplots, minfi, matrixStats, Rfast, tidyr, tidyselect, utils Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, IlluminaHumanMethylation450kanno.ilmn12.hg19, AnnotationDbi, sesameData, TCGAbiolinks (>= 2.29.4) License: GPL-3 MD5sum: 7fa52af0838f5006b9d03b80bd11c75e NeedsCompilation: no Title: Integrates DNA methylation data with gene expression in a single gene network Description: The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes. biocViews: GeneExpression, RNASeq, DNAMethylation, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DimensionReduction, PrincipalComponent, mRNAMicroarray, Normalization, GenePrediction, KEGG, Survival Author: Isha Mehta [aut] (), Ghazal Ebrahimi [aut], Hanie Samimi [aut], Habil Zare [aut, cre] () Maintainer: Habil Zare VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocManager/issues git_url: https://git.bioconductor.org/packages/iNETgrate git_branch: devel git_last_commit: 4216302 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iNETgrate_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iNETgrate_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iNETgrate_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iNETgrate_1.5.0.tgz vignettes: vignettes/iNETgrate/inst/doc/iNETgrate_inference.pdf vignetteTitles: iNETgrate: Integrating gene expression and DNA methylation data in a gene network hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iNETgrate/inst/doc/iNETgrate_inference.R dependencyCount: 287 Package: infercnv Version: 1.23.0 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, igraph, reshape2, rjags, fitdistrplus, future, foreach, doParallel, Seurat, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: df24c96b78e40c1ead33e83fe98a65d6 NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: devel git_last_commit: ec059e2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/infercnv_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/infercnv_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/infercnv_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/infercnv_1.23.0.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R suggestsMe: SCpubr dependencyCount: 200 Package: infinityFlow Version: 1.17.0 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: 0d8e8d1de4ce1552077863bf0d149d0e NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: devel git_last_commit: ac4a2be git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/infinityFlow_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/infinityFlow_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/infinityFlow_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/infinityFlow_1.17.0.tgz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 41 Package: Informeasure Version: 1.17.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), SummarizedExperiment License: Artistic-2.0 Archs: x64 MD5sum: 184a35c135751226cec3341714e599bb NeedsCompilation: no Title: R implementation of information measures Description: This package consolidates a comprehensive set of information measurements, encompassing mutual information, conditional mutual information, interaction information, partial information decomposition, and part mutual information. biocViews: GeneExpression, NetworkInference, Network, Software Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: devel git_last_commit: b93e002 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Informeasure_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Informeasure_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Informeasure_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Informeasure_1.17.0.tgz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify nonlinear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 1 Package: InPAS Version: 2.15.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq, depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges, GenomicFeatures, ggplot2, IRanges, limma, magrittr,methods,parallelly, plyranges, preprocessCore, readr,reshape2, RSQLite, stats,S4Vectors, utils Suggests: BiocGenerics,BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown, rmarkdown, rtracklayer, RUnit, grDevices, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: e680923d19d9422deb4571fcbbd9cbeb NeedsCompilation: no Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites. biocViews: Alternative Polyadenylation, Differential Polyadenylation Site Usage, RNA-seq, Gene Regulation, Transcription Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: devel git_last_commit: db66cda git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/InPAS_2.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/InPAS_2.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InPAS_2.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InPAS_2.15.1.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 147 Package: INPower Version: 1.43.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: b2f4eff27b4ae59e30ae717033d44855 NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: devel git_last_commit: bf23655 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/INPower_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INPower_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INPower_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INPower_1.43.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 2 Package: INSPEcT Version: 1.37.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, readxl, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 3beb5f79b402e70a485ee2a59d46b376 NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: devel git_last_commit: 7d70bac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/INSPEcT_1.37.0.tar.gz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 131 Package: INTACT Version: 1.7.0 Depends: R (>= 4.3.0) Imports: SQUAREM, bdsmatrix, numDeriv, stats, tidyr, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 59928e5db440a961a83ee923b038a4e2 NeedsCompilation: no Title: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis Description: This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step. biocViews: Bayesian, GeneSetEnrichment Author: Jeffrey Okamoto [aut, cre] (ORCID: ), Xiaoquan Wen [aut] (ORCID: ) Maintainer: Jeffrey Okamoto URL: https://github.com/jokamoto97/INTACT VignetteBuilder: knitr BugReports: https://github.com/jokamoto97/INTACT/issues git_url: https://git.bioconductor.org/packages/INTACT git_branch: devel git_last_commit: 597e401 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/INTACT_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/INTACT_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/INTACT_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/INTACT_1.7.0.tgz vignettes: vignettes/INTACT/inst/doc/INTACT.html vignetteTitles: INTACT: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INTACT/inst/doc/INTACT.R dependencyCount: 47 Package: InTAD Version: 1.27.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 8f7867dcde0a211838cbb540d217dba0 NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: devel git_last_commit: 4f6fc18 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/InTAD_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InTAD_1.27.0.zip vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 128 Package: intansv Version: 1.47.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: d427bf514a9afc046119b89042930038 NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: devel git_last_commit: 69fa786 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/intansv_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/intansv_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/intansv_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/intansv_1.47.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 163 Package: interacCircos Version: 1.17.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 2dfa499eaccdf6ee4d62fc3e4e83db72 NeedsCompilation: no Title: The Generation of Interactive Circos Plot Description: Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework. biocViews: Visualization Author: Zhe Cui [aut, cre] Maintainer: Zhe Cui VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: devel git_last_commit: 147c9a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/interacCircos_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/interacCircos_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interacCircos_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interacCircos_1.17.0.tgz vignettes: vignettes/interacCircos/inst/doc/interacCircos.html vignetteTitles: interacCircos hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R dependencyCount: 35 Package: InteractionSet Version: 1.35.0 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, GenomeInfoDb LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 64888b2e7bb30e1b4f22a5d6ae664eea NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: devel git_last_commit: 8c47d4b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/InteractionSet_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InteractionSet_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InteractionSet_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InteractionSet_1.35.0.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, DuplexDiscovereR, GenomicInteractions, HiCDOC, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, DegCre, EDIRquery, extraChIPs, geomeTriD, HicAggR, HiCcompare, HiCExperiment, HiContacts, HiCool, hicVennDiagram, mariner, nullranges, plyinteractions, trackViewer, treediff suggestsMe: plotgardener, transmogR, updateObject, CAGEWorkflow dependencyCount: 38 Package: InteractiveComplexHeatmap Version: 1.15.0 Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0) Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer, fontawesome Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges, data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap, cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3, GOexpress, SingleCellExperiment, scater, gplots, pheatmap, airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve (>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks License: MIT + file LICENSE MD5sum: 61cd9b90186c941db580101bb03093d8 NeedsCompilation: no Title: Make Interactive Complex Heatmaps Description: This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/InteractiveComplexHeatmap VignetteBuilder: knitr BugReports: https://github.com/jokergoo/InteractiveComplexHeatmap/issues git_url: https://git.bioconductor.org/packages/InteractiveComplexHeatmap git_branch: devel git_last_commit: 4548478 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/InteractiveComplexHeatmap_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InteractiveComplexHeatmap_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InteractiveComplexHeatmap_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InteractiveComplexHeatmap_1.15.0.tgz vignettes: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html, vignettes/InteractiveComplexHeatmap/inst/doc/share.html, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for visualizing DESeq2 results, 7. Implement interactive heatmap from scratch, 2. How interactive complex heatmap is implemented, 5. Interactivate heatmaps indirectly generated by pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize heatmaps interactively, 8. Share interactive heatmaps to collaborators, 3. Functions for Shiny app development hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R, vignettes/InteractiveComplexHeatmap/inst/doc/share.R, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R importsMe: CRISPRball, gINTomics, mineSweepR suggestsMe: CTexploreR, simona, simplifyEnrichment, metasnf dependencyCount: 81 Package: interactiveDisplay Version: 1.45.1 Depends: R (>= 3.5.0), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: 1e61e523f704c37ecda456de75b0bb67 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: devel git_last_commit: d6784e2 git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/interactiveDisplay_1.45.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/interactiveDisplay_1.45.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplay_1.45.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplay_1.45.1.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 114 Package: interactiveDisplayBase Version: 1.45.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: 0013248584852a215739490f3432a998 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: devel git_last_commit: 5218b82 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/interactiveDisplayBase_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/interactiveDisplayBase_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplayBase_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplayBase_1.45.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: interactiveDisplay suggestsMe: recount3 dependencyCount: 49 Package: InterCellar Version: 2.13.0 Depends: R (>= 4.1) Imports: config, golem, shiny, DT, shinydashboard, shinyFiles, shinycssloaders, data.table, fs, dplyr, tidyr, circlize, colourpicker, dendextend, factoextra, ggplot2, plotly, plyr, shinyFeedback, shinyalert, tibble, umap, visNetwork, wordcloud2, readxl, htmlwidgets, colorspace, signal, scales, htmltools, ComplexHeatmap, grDevices, stats, tools, utils, biomaRt, rlang, fmsb, igraph Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, httr License: MIT + file LICENSE MD5sum: d1b9955aa5cf68a7918d4e0c98fc98b5 NeedsCompilation: no Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions. biocViews: Software, SingleCell, Visualization, GO, Transcriptomics Author: Marta Interlandi [cre, aut] (ORCID: ) Maintainer: Marta Interlandi URL: https://github.com/martaint/InterCellar VignetteBuilder: knitr BugReports: https://github.com/martaint/InterCellar/issues git_url: https://git.bioconductor.org/packages/InterCellar git_branch: devel git_last_commit: e86c244 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/InterCellar_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/InterCellar_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/InterCellar_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/InterCellar_2.13.0.tgz vignettes: vignettes/InterCellar/inst/doc/user_guide.html vignetteTitles: InterCellar User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterCellar/inst/doc/user_guide.R dependencyCount: 200 Package: IntramiRExploreR Version: 1.29.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: fac728779bc94f8f14de8e647d0a6ca9 NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/IntramiRExploreR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/IntramiRExploreR git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: devel git_last_commit: a02de99 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IntramiRExploreR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IntramiRExploreR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IntramiRExploreR_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IntramiRExploreR_1.29.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf, vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 37 Package: IONiseR Version: 2.31.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: c30f2f6420db22002e52c8f8693f4c47 NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: devel git_last_commit: 5d8cf1e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IONiseR_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IONiseR_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IONiseR_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IONiseR_2.31.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 99 Package: iPath Version: 1.13.0 Depends: R (>= 4.1), mclust, BiocParallel, survival Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer, stats LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, BiocStyle, knitr License: GPL-2 MD5sum: f48067f9f3bb44ccbf7156c60d61d205 NeedsCompilation: yes Title: iPath pipeline for detecting perturbed pathways at individual level Description: iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes. biocViews: Pathways, Software, GeneExpression, Survival Author: Kenong Su [aut, cre], Zhaohui Qin [aut] Maintainer: Kenong Su SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/suke18/iPath/issues git_url: https://git.bioconductor.org/packages/iPath git_branch: devel git_last_commit: 8c1cca1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iPath_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iPath_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iPath_1.13.0.tgz vignettes: vignettes/iPath/inst/doc/iPath.html vignetteTitles: The iPath User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPath/inst/doc/iPath.R dependencyCount: 111 Package: ipdDb Version: 1.25.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 946179ec16dd2e6f25e63a45b078c3c9 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation,AnnotationHubSoftware Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: devel git_last_commit: db4516e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ipdDb_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ipdDb_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ipdDb_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ipdDb_1.25.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 68 Package: IPO Version: 1.33.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: c0744f28c11a64976a9d8e3823430abb NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Lieb Maintainer: Thomas Lieb URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: devel git_last_commit: 6da5785 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IPO_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IPO_1.33.0.zip vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 163 Package: IRanges Version: 2.41.2 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.45.2) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: b493af234823db286ace8748d98659f0 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Michael Lawrence [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: devel git_last_commit: 4eff83a git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-03 source.ver: src/contrib/IRanges_2.41.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/IRanges_2.41.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IRanges_2.41.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IRanges_2.41.2.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE 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data.table, Rcapture License: CC BY 4.0 MD5sum: f2ea0fe3781775d78651e5dfa321a745 NeedsCompilation: no Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell Author: Francesco Gazzo [cre], Giulia Pais [aut] (ORCID: ), Andrea Calabria [aut], Giulio Spinozzi [aut] Maintainer: Francesco Gazzo URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics, https://calabrialab.github.io/ISAnalytics/ VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: devel git_last_commit: 3b0f63a git_last_commit_date: 2024-12-05 Date/Publication: 2024-12-05 source.ver: src/contrib/ISAnalytics_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISAnalytics_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISAnalytics_1.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISAnalytics_1.17.1.tgz vignettes: vignettes/ISAnalytics/inst/doc/ISAnalytics.html, vignettes/ISAnalytics/inst/doc/workflow_start.html vignetteTitles: ISAnalytics, workflow_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/ISAnalytics.R, vignettes/ISAnalytics/inst/doc/workflow_start.R dependencyCount: 115 Package: iSEE Version: 2.19.2 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, listviewer, ComplexHeatmap, circlize, grid Suggests: testthat, covr, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools, GenomicRanges License: MIT + file LICENSE MD5sum: 3d7969d2bfff368d660271cf1e7e10d9 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GeneExpression, GUI, ImmunoOncology, ShinyApps, SingleCell, Transcription, Transcriptomics, Visualization Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://isee.github.io/iSEE/ VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: devel git_last_commit: 2d462c8 git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-10 source.ver: src/contrib/iSEE_2.19.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEE_2.19.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEE_2.19.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEE_2.19.2.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEde, iSEEhex, iSEEpathways, iSEEtree, iSEEu importsMe: iSEEfier, iSEEhub, iSEEindex suggestsMe: schex, DuoClustering2018, HCAData, HCATonsilData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 121 Package: iSEEde Version: 1.5.0 Depends: iSEE Imports: DESeq2, edgeR, methods, S4Vectors, shiny, SummarizedExperiment Suggests: airway, BiocStyle, covr, knitr, limma, org.Hs.eg.db, RefManageR, rmarkdown, scuttle, sessioninfo, statmod, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 66cee6294aaafe1527e971b0c9232239 NeedsCompilation: no Title: iSEE extension for panels related to differential expression analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of differential expression results. This package does not perform differential expression. Instead, it provides methods to embed precomputed differential expression results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEde VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEde git_url: https://git.bioconductor.org/packages/iSEEde git_branch: devel git_last_commit: 3899f44 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEde_1.5.0.tar.gz vignettes: vignettes/iSEEde/inst/doc/annotations.html, vignettes/iSEEde/inst/doc/iSEEde.html, vignettes/iSEEde/inst/doc/methods.html, vignettes/iSEEde/inst/doc/rounding.html vignetteTitles: Using annotations to facilitate interactive exploration, Introduction to iSEEde, Supported differential expression methods, Rounding numeric values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEde/inst/doc/annotations.R, vignettes/iSEEde/inst/doc/iSEEde.R, vignettes/iSEEde/inst/doc/methods.R, vignettes/iSEEde/inst/doc/rounding.R suggestsMe: iSEEpathways dependencyCount: 135 Package: iSEEfier Version: 1.3.0 Imports: iSEE, iSEEu, methods, ggplot2, igraph, rlang, stats, SummarizedExperiment, SingleCellExperiment, visNetwork, BiocBaseUtils Suggests: knitr, rmarkdown, scater, scRNAseq, org.Mm.eg.db, scuttle, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 55dbead1277f722f21985e5a08459c95 NeedsCompilation: no Title: Streamlining the creation of initial states for starting an iSEE instance Description: iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user). biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GUI, GeneExpression, ImmunoOncology, ShinyApps, SingleCell, Software, Transcription, Transcriptomics, Visualization Author: Najla Abassi [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Najla Abassi URL: https://github.com/NajlaAbassi/iSEEfier VignetteBuilder: knitr BugReports: https://github.com/NajlaAbassi/iSEEfier/issues git_url: https://git.bioconductor.org/packages/iSEEfier git_branch: devel git_last_commit: 94647b3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEfier_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEfier_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEfier_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEfier_1.3.0.tgz vignettes: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.html vignetteTitles: iSEEfier_userguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.R dependencyCount: 127 Package: iSEEhex Version: 1.9.0 Depends: SummarizedExperiment, iSEE Imports: ggplot2, hexbin, methods, shiny Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), scRNAseq, scater License: Artistic-2.0 MD5sum: 4b3c07bbe65dd5c8e15f35fb1731b025 NeedsCompilation: no Title: iSEE extension for summarising data points in hexagonal bins Description: This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhex git_url: https://git.bioconductor.org/packages/iSEEhex git_branch: devel git_last_commit: 7dd9c2b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEhex_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEhex_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhex_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEhex_1.9.0.tgz vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html vignetteTitles: The iSEEhex package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R dependsOnMe: iSEEu dependencyCount: 123 Package: iSEEhub Version: 1.9.0 Depends: SummarizedExperiment, SingleCellExperiment, ExperimentHub Imports: AnnotationHub, BiocManager, DT, iSEE, methods, rintrojs, S4Vectors, shiny, shinydashboard, shinyjs, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), nullrangesData Enhances: BioPlex, biscuiteerData, bodymapRat, CLLmethylation, CopyNeutralIMA, curatedAdipoArray, curatedAdipoChIP, curatedMetagenomicData, curatedTCGAData, DMRcatedata, DuoClustering2018, easierData, emtdata, epimutacionsData, FieldEffectCrc, GenomicDistributionsData, GSE103322, GSE13015, GSE62944, HDCytoData, HMP16SData, HumanAffyData, imcdatasets, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, MethylSeqData, muscData, NxtIRFdata, ObMiTi, quantiseqr, restfulSEData, RLHub, sesameData, SimBenchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialDmelxsim, STexampleData, TabulaMurisData, TabulaMurisSenisData, TENxVisiumData, tissueTreg, VectraPolarisData, xcoredata License: Artistic-2.0 MD5sum: c461ac4a2973209e404ef88a386011a3 NeedsCompilation: no Title: iSEE for the Bioconductor ExperimentHub Description: This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app. biocViews: DataImport, ImmunoOncology Infrastructure, ShinyApps, SingleCell, Software Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhub VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhub git_url: https://git.bioconductor.org/packages/iSEEhub git_branch: devel git_last_commit: bd09a3b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/iSEEhub_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEhub_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhub_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEhub_1.9.0.tgz vignettes: vignettes/iSEEhub/inst/doc/contributing.html, vignettes/iSEEhub/inst/doc/iSEEhub.html vignetteTitles: Contributing to iSEEhub, Introduction to iSEEhub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhub/inst/doc/contributing.R, vignettes/iSEEhub/inst/doc/iSEEhub.R dependencyCount: 144 Package: iSEEindex Version: 1.5.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: BiocFileCache, DT, iSEE, methods, paws.storage, rintrojs, shiny, shinydashboard, shinyjs, stringr, urltools, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, markdown, scRNAseq, sessioninfo, testthat (>= 3.0.0), yaml License: Artistic-2.0 MD5sum: e70a6d368fc3018695a5187490867821 NeedsCompilation: no Title: iSEE extension for a landing page to a custom collection of data sets Description: This package provides an interface to any collection of data sets within a single iSEE web-application. The main functionality of this package is to define a custom landing page allowing app maintainers to list a custom collection of data sets that users can selected from and directly load objects into an iSEE web-application. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Federico Marini [aut] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEindex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEindex git_url: https://git.bioconductor.org/packages/iSEEindex git_branch: devel git_last_commit: 7211e8a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEindex_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEindex_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEindex_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEindex_1.5.0.tgz vignettes: vignettes/iSEEindex/inst/doc/header.html, vignettes/iSEEindex/inst/doc/iSEEindex.html, vignettes/iSEEindex/inst/doc/resources.html vignetteTitles: Adding custom header and footer to the landing page, Introduction to iSEEindex, Implementing custom iSEEindex resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEindex/inst/doc/header.R, vignettes/iSEEindex/inst/doc/iSEEindex.R, vignettes/iSEEindex/inst/doc/resources.R dependencyCount: 142 Package: iSEEpathways Version: 1.5.0 Depends: iSEE Imports: ggplot2, methods, S4Vectors, shiny, shinyWidgets, stats, SummarizedExperiment Suggests: airway, BiocStyle, covr, edgeR, fgsea, GO.db, iSEEde, knitr, org.Hs.eg.db, RefManageR, rmarkdown, scater, scuttle, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 018a57ab767784c0d002743f4d50cb3b NeedsCompilation: no Title: iSEE extension for panels related to pathway analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of pathway analysis results. This package does not perform pathway analysis. Instead, it provides methods to embed precomputed pathway analysis results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression, GeneExpression, GUI, Visualization, Pathways, GeneSetEnrichment, GO, ShinyApps Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEpathways VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEpathways git_url: https://git.bioconductor.org/packages/iSEEpathways git_branch: devel git_last_commit: d58f5a5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEpathways_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEpathways_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEpathways_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEpathways_1.5.0.tgz vignettes: vignettes/iSEEpathways/inst/doc/gene-ontology.html, vignettes/iSEEpathways/inst/doc/integration.html, vignettes/iSEEpathways/inst/doc/iSEEpathways.html vignetteTitles: Working with the Gene Ontology, Integration with other panels, Introduction to iSEEpathways hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEpathways/inst/doc/gene-ontology.R, vignettes/iSEEpathways/inst/doc/integration.R, vignettes/iSEEpathways/inst/doc/iSEEpathways.R dependencyCount: 122 Package: iSEEtree Version: 1.1.2 Depends: R (>= 4.4.0), iSEE (>= 2.19.2) Imports: grDevices, methods, miaViz, S4Vectors, shiny, mia, shinyWidgets, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, utils Suggests: biomformat, BiocStyle, knitr, MGnifyR, RefManageR, remotes, rmarkdown, scater, testthat (>= 3.0.0), vegan License: Artistic-2.0 MD5sum: 4c548321a9207f99cf3ac6c148483da2 NeedsCompilation: no Title: Interactive visualisation for microbiome data Description: iSEEtree is an extension of iSEE for the TreeSummarizedExperiment. It leverages the functionality from the miaViz package for microbiome data visualisation to create panels that are specific for TreeSummarizedExperiment objects. Not surprisingly, it also depends on the generic panels from iSEE. biocViews: Microbiome, Software, Visualization, GUI, ShinyApps, DataImport Author: Giulio Benedetti [aut, cre] (ORCID: ), Ely Seraidarian [ctb] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Giulio Benedetti URL: https://github.com/microbiome/iSEEtree VignetteBuilder: knitr BugReports: https://github.com/microbiome/iSEEtree/issues git_url: https://git.bioconductor.org/packages/iSEEtree git_branch: devel git_last_commit: 628462c git_last_commit_date: 2024-12-14 Date/Publication: 2024-12-15 source.ver: src/contrib/iSEEtree_1.1.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEtree_1.1.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEtree_1.1.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEtree_1.1.2.tgz vignettes: vignettes/iSEEtree/inst/doc/iSEEtree.html vignetteTitles: iSEEtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEtree/inst/doc/iSEEtree.R dependencyCount: 230 Package: iSEEu Version: 1.19.0 Depends: iSEE, iSEEhex Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2 (>= 3.4.0), DT, stats, colourpicker, shinyAce Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: d25d71ac493e83b19077ba094cda55fc NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: devel git_last_commit: 4869642 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSEEu_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSEEu_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSEEu_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSEEu_1.19.0.tgz vignettes: vignettes/iSEEu/inst/doc/iSEEu.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/iSEEu.R importsMe: iSEEfier dependencyCount: 124 Package: iSeq Version: 1.59.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: x64 MD5sum: 6382a55c713215430267e85be3d118ed NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: devel git_last_commit: f08fc7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iSeq_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iSeq_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iSeq_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iSeq_1.59.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: ISLET Version: 1.9.0 Depends: R(>= 4.2.0), Matrix, parallel, BiocParallel, SummarizedExperiment, BiocGenerics, lme4, nnls Imports: stats, methods, purrr, abind Suggests: BiocStyle, knitr, rmarkdown, htmltools, RUnit, dplyr License: GPL-2 MD5sum: 24951acce6e5ca7450f04378f8cdeea7 NeedsCompilation: no Title: Individual-Specific ceLl typE referencing Tool Description: ISLET is a method to conduct signal deconvolution for general -omics data. It can estimate the individual-specific and cell-type-specific reference panels, when there are multiple samples observed from each subject. It takes the input of the observed mixture data (feature by sample matrix), and the cell type mixture proportions (sample by cell type matrix), and the sample-to-subject information. It can solve for the reference panel on the individual-basis and conduct test to identify cell-type-specific differential expression (csDE) genes. It also improves estimated cell type mixture proportions by integrating personalized reference panels. biocViews: Software, RNASeq, Transcriptomics, Transcription, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Hao Feng [aut, cre] (ORCID: ), Qian Li [aut], Guanqun Meng [aut] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ISLET git_branch: devel git_last_commit: 7137eb9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ISLET_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISLET_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISLET_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISLET_1.9.0.tgz vignettes: vignettes/ISLET/inst/doc/ISLET.html vignetteTitles: Individual-specific and cell-type-specific deconvolution using ISLET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISLET/inst/doc/ISLET.R dependencyCount: 64 Package: isobar Version: 1.53.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: 189dac6af3815bd6ff377963ad60aaa1 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: devel git_last_commit: 2a92392 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/isobar_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/isobar_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/isobar_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/isobar_1.53.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R dependencyCount: 91 Package: IsoBayes Version: 1.5.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, data.table, glue, stats, doParallel, parallel, doRNG, foreach, iterators, ggplot2, HDInterval, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: fad672779e9b38732bba0f108eafd75d NeedsCompilation: yes Title: IsoBayes: Single Isoform protein inference Method via Bayesian Analyses Description: IsoBayes is a Bayesian method to perform inference on single protein isoforms. Our approach infers the presence/absence of protein isoforms, and also estimates their abundance; additionally, it provides a measure of the uncertainty of these estimates, via: i) the posterior probability that a protein isoform is present in the sample; ii) a posterior credible interval of its abundance. IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance. We further identify isoforms where the relative abundance of proteins and transcripts significantly differ. We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data: i) peptides may be erroneously detected (even when absent); ii) many peptides are compatible with multiple protein isoforms. In the first layer, we sample the presence/absence of each peptide based on its estimated probability of being mistakenly detected, also known as PEP (i.e., posterior error probability). In the second layer, for peptides that were estimated as being present, we allocate their abundance across the protein isoforms they map to. These two steps allow us to recover the presence and abundance of each protein isoform. biocViews: StatisticalMethod, Bayesian, Proteomics, MassSpectrometry, AlternativeSplicing, Sequencing, RNASeq, GeneExpression, Genetics, Visualization, Software Author: Jordy Bollon [aut], Simone Tiberi [aut, cre] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/IsoBayes SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/IsoBayes/issues git_url: https://git.bioconductor.org/packages/IsoBayes git_branch: devel git_last_commit: 9b32421 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IsoBayes_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoBayes_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoBayes_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoBayes_1.5.0.tgz vignettes: vignettes/IsoBayes/inst/doc/IsoBayes.html vignetteTitles: IsoBayes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IsoBayes/inst/doc/IsoBayes.R dependencyCount: 75 Package: IsoCorrectoR Version: 1.25.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: b29c72535e281d6f39e3950f3bcd9b9b NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: devel git_last_commit: b3c775a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IsoCorrectoR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoR_1.25.0.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 41 Package: IsoCorrectoRGUI Version: 1.23.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: fb9907a431025b25bf919c456e446e31 NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: devel git_last_commit: 7bf3e50 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IsoCorrectoRGUI_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoRGUI_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoRGUI_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoRGUI_1.23.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 44 Package: ISoLDE Version: 1.35.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) MD5sum: fe15a189ea6f2fe0cfe1c3b9b6c02d28 NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: devel git_last_commit: e892789 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ISoLDE_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ISoLDE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ISoLDE_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ISoLDE_1.35.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.35.0 Depends: R (>= 4.4), SummarizedExperiment Imports: AnnotationDbi, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, rmarkdown, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: ef3a3f886ccd5842943be4027c7159db NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP - CIBER Epidemiologia y Salud Publica) Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: devel git_last_commit: b036eff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/isomiRs_1.35.0.tar.gz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 150 Package: ITALICS Version: 2.67.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 6da1e72b9ee3fcdb12b552fb277de3d6 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: devel git_last_commit: ae94ff4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ITALICS_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ITALICS_2.67.0.zip vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 71 Package: iterativeBMA Version: 1.65.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: a31b53ffecaf3451650f55a271457504 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: devel git_last_commit: 00a6f3f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iterativeBMA_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iterativeBMA_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMA_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMA_1.65.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 22 Package: iterativeBMAsurv Version: 1.65.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 5f0db4b43ab1c2fdf2f85c00aeb29c18 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: devel git_last_commit: 18ae47d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/iterativeBMAsurv_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/iterativeBMAsurv_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMAsurv_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMAsurv_1.65.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: IVAS Version: 2.27.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 Archs: x64 MD5sum: 7bdba9a5c95195792cc83e29fa991473 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: devel git_last_commit: 19b8da0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IVAS_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IVAS_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IVAS_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IVAS_2.27.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS dependencyCount: 115 Package: ivygapSE Version: 1.29.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat, rmarkdown, BiocStyle, magick, statmod, codetools License: Artistic-2.0 MD5sum: 4800b899c10d80f3c5458d2962c9965c NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: devel git_last_commit: 4df1812 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ivygapSE_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ivygapSE_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ivygapSE_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ivygapSE_1.29.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 150 Package: IWTomics Version: 1.31.0 Depends: R (>= 3.5.0), GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 54809a6925fc13ab8ec1a2ff26aef68c NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: devel git_last_commit: 87c60ab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/IWTomics_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/IWTomics_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/IWTomics_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/IWTomics_1.31.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 75 Package: karyoploteR Version: 1.33.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 Archs: x64 MD5sum: 575febb46f71f107e192ad9f57350d52 NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel [aut, cre] (ORCID: ) Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: devel git_last_commit: 4e6d89c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/karyoploteR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/karyoploteR_1.33.0.zip vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR, CNViz, multicrispr suggestsMe: Category, EpiMix, UPDhmm, MitoHEAR dependencyCount: 141 Package: katdetectr Version: 1.9.0 Depends: R (>= 4.2) Imports: Biobase (>= 2.54.0), BiocParallel (>= 1.26.2), BSgenome (>= 1.62.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.4), changepoint (>= 2.2.3), changepoint.np (>= 1.0.3), checkmate (>= 2.0.0), dplyr (>= 1.0.8), GenomeInfoDb (>= 1.28.4), GenomicRanges (>= 1.44.0), ggplot2 (>= 3.3.5), ggtext (>= 0.1.1), IRanges (>= 2.26.0), maftools (>= 2.10.5), methods (>= 4.1.3), plyranges (>= 1.17.0), Rdpack (>= 2.3.1), rlang (>= 1.0.2), S4Vectors (>= 0.30.2), scales (>= 1.2.0), tibble (>= 3.1.6), tidyr (>= 1.2.0), tools, utils, VariantAnnotation (>= 1.38.0) Suggests: BiocStyle (>= 2.26.0), knitr (>= 1.37), rmarkdown (>= 2.13), stats, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: a55f0455612abd5b40ddd5f7d3f9cde3 NeedsCompilation: no Title: Detection, Characterization and Visualization of Kataegis in Sequencing Data Description: Kataegis refers to the occurrence of regional hypermutation and is a phenomenon observed in a wide range of malignancies. Using changepoint detection katdetectr aims to identify putative kataegis foci from common data-formats housing genomic variants. Katdetectr has shown to be a robust package for the detection, characterization and visualization of kataegis. biocViews: WholeGenome, Software, SNP, Sequencing, Classification, VariantAnnotation Author: Daan Hazelaar [aut, cre] (ORCID: ), Job van Riet [aut] (ORCID: ), Harmen van de Werken [ths] (ORCID: ) Maintainer: Daan Hazelaar URL: https://doi.org/doi:10.18129/B9.bioc.katdetectr VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/katdetectr/issues git_url: https://git.bioconductor.org/packages/katdetectr git_branch: devel git_last_commit: 641e734 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/katdetectr_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/katdetectr_1.9.0.zip vignettes: vignettes/katdetectr/inst/doc/General_overview.html vignetteTitles: Overview_katdetectr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/katdetectr/inst/doc/General_overview.R dependencyCount: 129 Package: KBoost Version: 1.15.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 0c664c8502b9f68eca6628199ef0302a NeedsCompilation: no Title: Inference of gene regulatory networks from gene expression data Description: Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets. biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference, GeneRegulation, Transcriptomics, SystemsBiology, Transcription, GeneExpression, Regression, PrincipalComponent Author: Luis F. Iglesias-Martinez [aut, cre] (ORCID: ), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: devel git_last_commit: b4d1509 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KBoost_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KBoost_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KBoost_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KBoost_1.15.0.tgz vignettes: vignettes/KBoost/inst/doc/KBoost.html vignetteTitles: KBoost hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KBoost/inst/doc/KBoost.R dependencyCount: 2 Package: KCsmart Version: 2.65.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 Archs: x64 MD5sum: 5d4d18f1af1f0c74a494e7c04a161fae NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: devel git_last_commit: d71c2e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KCsmart_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KCsmart_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KCsmart_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KCsmart_2.65.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 19 Package: kebabs Version: 1.41.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix (>= 1.5-0), XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: 759859e1abad227e8c1b9976e4e1ef74 NeedsCompilation: yes Title: Kernel-Based Analysis of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme [aut], Ulrich Bodenhofer [aut,cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: devel git_last_commit: 56e9334 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/kebabs_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/kebabs_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/kebabs_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/kebabs_1.41.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: odseq dependencyCount: 37 Package: KEGGgraph Version: 1.67.0 Depends: R (>= 3.5.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 93ace09026b443db8cb39ae4d8e280b8 NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon and Hervé Pagès Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: devel git_last_commit: 522a8e8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KEGGgraph_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGgraph_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGgraph_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGgraph_1.67.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: lpNet, ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, iCARH suggestsMe: DEGraph, GenomicRanges, kangar00, maGUI, rags2ridges dependencyCount: 14 Package: KEGGlincs Version: 1.33.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: b529e8ac2b959d99c697318472f4513a NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: devel git_last_commit: ead0a33 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KEGGlincs_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGlincs_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGlincs_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGlincs_1.33.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 64 Package: keggorthology Version: 2.59.0 Depends: R (>= 2.5.0), hgu95av2.db, graph Imports: AnnotationDbi, DBI, grDevices, methods, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: d22e505ac9ef77b13947307b04ca5e22 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: devel git_last_commit: 1023f18 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/keggorthology_2.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/keggorthology_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/keggorthology_2.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/keggorthology_2.59.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 49 Package: KEGGREST Version: 1.47.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: 81419475dde18ef4164e7c26f9d23262 NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Bioconductor Package Maintainer [aut, cre], Martin Morgan [ctb], Kozo Nishida [ctb], Marcel Ramos [ctb], Kristina Riemer [ctb], Lori Shepherd [ctb], Jeremy Volkening [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/KEGGREST VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/KEGGREST/issues git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: devel git_last_commit: 388585c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KEGGREST_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KEGGREST_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KEGGREST_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KEGGREST_1.47.0.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, funOmics, gage, ginmappeR, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA suggestsMe: Category, categoryCompare, gatom, GenomicRanges, globaltest, iSEEu, MetMashR, MLP, padma, rGREAT, RTopper, SomaScan.db, CALANGO, maGUI, phoenics, ReporterScore, scDiffCom dependencyCount: 27 Package: KinSwingR Version: 1.25.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 8e706ae61faad1d3f42a9777dd39244f NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: devel git_last_commit: ccf9fe9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KinSwingR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KinSwingR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KinSwingR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KinSwingR_1.25.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 36 Package: kissDE Version: 1.27.0 Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel, shiny, shinycssloaders, ade4, factoextra, DT Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 700c37ebc0b227bb3e757b76696c792e NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot URL: https://github.com/lbbe-software/kissDE git_url: https://git.bioconductor.org/packages/kissDE git_branch: devel git_last_commit: f7cd154 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-06 source.ver: src/contrib/kissDE_1.27.0.tar.gz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 200 Package: kmcut Version: 1.1.0 Imports: survival, tools, methods, pracma, doParallel, foreach, parallel, SummarizedExperiment, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, License: Artistic-2.0 MD5sum: 8ea024181c4482d2390c5afb5c3a444e NeedsCompilation: no Title: Optimized Kaplan Meier analysis and identification and validation of prognostic biomarkers Description: The purpose of the package is to identify prognostic biomarkers and an optimal numeric cutoff for each biomarker that can be used to stratify a group of test subjects (samples) into two sub-groups with significantly different survival (better vs. worse). The package was developed for the analysis of gene expression data, such as RNA-seq. However, it can be used with any quantitative variable that has a sufficiently large proportion of unique values. biocViews: Software, StatisticalMethod, GeneExpression, Survival Author: Igor Kuznetsov [aut, cre], Javed Khan [aut] Maintainer: Igor Kuznetsov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kmcut git_branch: devel git_last_commit: da83cfd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/kmcut_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/kmcut_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/kmcut_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/kmcut_1.1.0.tgz vignettes: vignettes/kmcut/inst/doc/kmcut_intro.html vignetteTitles: kmcut_intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kmcut/inst/doc/kmcut_intro.R dependencyCount: 45 Package: KnowSeq Version: 1.21.0 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) MD5sum: 8ec747eefa20aef52a39f0aabd31d784 NeedsCompilation: no Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: devel git_last_commit: 5a60694 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/KnowSeq_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/KnowSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/KnowSeq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/KnowSeq_1.21.0.tgz vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html vignetteTitles: The KnowSeq users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R dependencyCount: 172 Package: knowYourCG Version: 1.3.3 Depends: R (>= 4.4.0) Imports: sesameData, dplyr, methods, rlang, GenomicRanges, IRanges, reshape2, S4Vectors, stats, stringr, utils, ggplot2, ggrepel, tibble, wheatmap, magrittr Suggests: testthat (>= 3.0.0), SummarizedExperiment, rmarkdown, knitr, sesame, gprofiler2 License: MIT + file LICENSE MD5sum: 76c8c77b307b5684698819c1fe4847cc NeedsCompilation: no Title: Functional analysis of DNA methylome datasets Description: knowYourCG automates the functional analysis of DNA methylation data. The package tests the enrichment of discrete CpG probes across thousands of curated biological and technical features. GSEA-like analysis can be performed on continuous methylation data query sets. knowYourCG can also take beta matrices as input to perform feature aggregation over the curated database sets. biocViews: Epigenetics, DNAMethylation, MethylationArray Author: Zhou Wanding [aut], Goldberg David [aut, cre] (ORCID: ), Fu Hongxiang [ctb], Moyer Ethan [ctb] Maintainer: Goldberg David URL: https://github.com/zhou-lab/knowYourCG VignetteBuilder: knitr BugReports: https://github.com/zhou-lab/knowYourCG/issues git_url: https://git.bioconductor.org/packages/knowYourCG git_branch: devel git_last_commit: 59ae733 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-19 source.ver: src/contrib/knowYourCG_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/knowYourCG_1.3.5.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/knowYourCG_1.3.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/knowYourCG_1.3.5.tgz vignettes: vignettes/knowYourCG/inst/doc/KYCG.html vignetteTitles: "knowYourCG" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/knowYourCG/inst/doc/KYCG.R dependencyCount: 98 Package: koinar Version: 1.1.1 Depends: R (>= 4.3) Imports: httr, jsonlite, methods, utils Suggests: BiocManager, BiocStyle (>= 2.26), httptest, knitr, lattice, msdata, OrgMassSpecR, protViz, S4Vectors, Spectra, testthat, mzR License: Apache License 2.0 MD5sum: 3a9eead84080851ffa0d43aab77122f0 NeedsCompilation: no Title: KoinaR - Remote machine learning inference using Koina Description: A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic. biocViews: MassSpectrometry, Proteomics, Infrastructure, Software Author: Ludwig Lautenbacher [aut, cre] (ORCID: ), Christian Panse [aut] (ORCID: ) Maintainer: Ludwig Lautenbacher URL: https://github.com/wilhelm-lab/koina VignetteBuilder: knitr BugReports: https://github.com/wilhelm-lab/koina/issues git_url: https://git.bioconductor.org/packages/koinar git_branch: devel git_last_commit: a54b95d git_last_commit_date: 2024-12-20 Date/Publication: 2024-12-20 source.ver: src/contrib/koinar_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/koinar_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/koinar_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/koinar_1.1.1.tgz vignettes: vignettes/koinar/inst/doc/koina.html vignetteTitles: On using the R lang client for koina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/koinar/inst/doc/koina.R dependencyCount: 11 Package: LACE Version: 2.11.0 Depends: R (>= 4.2.0) Imports: curl, igraph, foreach, doParallel, sortable, dplyr, forcats, data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils, purrr, stringi, stringr, Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs, data.table, htmltools, htmlwidgets, bsplus, shinyvalidate, shiny, shinythemes, shinyFiles, shinyjs, shinyBS, shinydashboard, biomaRt, callr, logr, ggplot2, svglite Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown License: file LICENSE Archs: x64 MD5sum: 877b352a6390e98aeb4cc998340f3dd4 NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ), Gianluca Ascolani [aut] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: devel git_last_commit: c6d296d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LACE_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LACE_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LACE_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LACE_2.11.0.tgz vignettes: vignettes/LACE/inst/doc/v1_introduction.html, vignettes/LACE/inst/doc/v2_running_LACE.html, vignettes/LACE/inst/doc/v3_LACE_interface.html vignetteTitles: Introduction, Running LACE, LACE-interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/v1_introduction.R, vignettes/LACE/inst/doc/v2_running_LACE.R, vignettes/LACE/inst/doc/v3_LACE_interface.R dependencyCount: 166 Package: LBE Version: 1.75.0 Depends: stats Imports: graphics, stats, utils Suggests: qvalue License: GPL-2 MD5sum: e6cb772c9dee7d4cc767b52139dc5f2a NeedsCompilation: no Title: Estimation of the false discovery rate Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: devel git_last_commit: 4a18e0f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LBE_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LBE_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LBE_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LBE_1.75.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependencyCount: 3 Package: ldblock Version: 1.37.0 Depends: R (>= 3.5), methods, rlang Imports: BiocGenerics (>= 0.25.1), httr, Matrix Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown, snpStats, VariantAnnotation, GenomeInfoDb, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6) License: Artistic-2.0 MD5sum: 8cf0d38bbf6d8891bad058896297f2ca NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: devel git_last_commit: 6555373 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ldblock_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ldblock_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ldblock_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ldblock_1.37.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R dependencyCount: 20 Package: LEA Version: 3.19.1 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: 636d9b698d3d398f77a93249b44d74f8 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois , Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: devel git_last_commit: a37ac6c git_last_commit_date: 2024-11-08 Date/Publication: 2024-11-08 source.ver: src/contrib/LEA_3.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/LEA_3.19.1.zip vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R dependencyCount: 4 Package: LedPred Version: 1.41.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 85f140e27d3223e7392b8cc324e33c98 NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: devel git_last_commit: ae330cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LedPred_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LedPred_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LedPred_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LedPred_1.41.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 73 Package: lefser Version: 1.17.2 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, S4Vectors, stats, methods, utils, dplyr, testthat, tibble, tidyr, forcats, stringr, ggtree Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, phyloseq, pkgdown, covr, withr License: Artistic-2.0 MD5sum: c734c72901df96905df9bad4fbef0448 NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes). biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Sehyun Oh [cre, ctb] (ORCID: ), Asya Khleborodova [aut], Samuel Gamboa-Tuz [ctb], Marcel Ramos [ctb] (ORCID: ), Ludwig Geistlinger [ctb] (ORCID: ), Levi Waldron [ctb] (ORCID: ) Maintainer: Sehyun Oh URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: devel git_last_commit: c39ec62 git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-18 source.ver: src/contrib/lefser_1.17.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/lefser_1.17.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lefser_1.17.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lefser_1.17.2.tgz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R dependencyCount: 109 Package: lemur Version: 1.5.0 Depends: R (>= 4.1) Imports: stats, utils, irlba, methods, SingleCellExperiment, SummarizedExperiment, rlang (>= 1.1.0), vctrs (>= 0.6.0), glmGamPoi (>= 1.12.0), BiocGenerics, S4Vectors, Matrix, DelayedMatrixStats, HDF5Array, MatrixGenerics, matrixStats, Rcpp, harmony (>= 1.2.0), limma, BiocNeighbors LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), tidyverse, uwot, dplyr, edgeR, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 5dcc004463111d8c134e2bb79c2e2465 NeedsCompilation: yes Title: Latent Embedding Multivariate Regression Description: Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed. biocViews: Transcriptomics, DifferentialExpression, SingleCell, DimensionReduction, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/lemur VignetteBuilder: knitr BugReports: https://github.com/const-ae/lemur/issues git_url: https://git.bioconductor.org/packages/lemur git_branch: devel git_last_commit: 6e2e508 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lemur_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lemur_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lemur_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lemur_1.5.0.tgz vignettes: vignettes/lemur/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lemur/inst/doc/Introduction.R dependencyCount: 85 Package: les Version: 1.57.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 6a64fbc99dd458a4ffd328adc5b5bb19 NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: devel git_last_commit: 9008cb7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/les_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/les_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/les_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/les_1.57.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.25.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods, rmarkdown LinkingTo: Rcpp Suggests: rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 789edb284541cc686c1e617d3e17e1c2 NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: devel git_last_commit: ccbbbd4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/levi_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/levi_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/levi_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/levi_1.25.0.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 99 Package: lfa Version: 2.7.0 Depends: R (>= 4.0) Imports: utils, methods, corpcor, RSpectra Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) MD5sum: bc372f60a806789e8ad9d370a00f2eaa NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). biocViews: SNP, DimensionReduction, PrincipalComponent, Regression Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: devel git_last_commit: bf41026 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lfa_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lfa_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lfa_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lfa_2.7.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest suggestsMe: jackstraw dependencyCount: 12 Package: limma Version: 3.63.2 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods, statmod Suggests: BiasedUrn, ellipse, gplots, knitr, locfit, MASS, splines, affy, AnnotationDbi, Biobase, BiocStyle, GO.db, illuminaio, org.Hs.eg.db, vsn License: GPL (>=2) MD5sum: dc4562775761127ee367f5ddc9e00c59 NeedsCompilation: yes Title: Linear Models for Microarray and Omics Data Description: Data analysis, linear models and differential expression for omics data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Goknur Giner [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Charity Law [ctb], Mengbo Li [ctb], Lizhong Chen [ctb] Maintainer: Gordon Smyth URL: https://bioinf.wehi.edu.au/limma/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limma git_branch: devel git_last_commit: 39aac52 git_last_commit_date: 2024-11-10 Date/Publication: 2024-11-10 source.ver: src/contrib/limma_3.63.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/limma_3.63.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limma_3.63.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limma_3.63.2.tgz vignettes: vignettes/limma/inst/doc/usersguide.pdf, vignettes/limma/inst/doc/intro.html vignetteTitles: limma User's Guide, A brief introduction to limma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limma/inst/doc/intro.R dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, GEOexplorer, marray, metagenomeSeq, metaseqR2, mpra, NanoTube, octad, protGear, qpcrNorm, qusage, RBM, RnBeads, Rnits, splineTimeR, TMSig, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, zenith, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123, OSCA.workflows, BALLI, BioInsight, CEDA, countTransformers, cp4p, DAAGbio, DRomics, fmt, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ATACseqQC, ATACseqTFEA, attract, autonomics, AWFisher, ballgown, BatchQC, beadarray, BERT, biotmle, BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, casper, ChAMP, CleanUpRNAseq, clusterExperiment, CNVRanger, compcodeR, consensusDE, consensusOV, crlmm, csaw, cTRAP, ctsGE, DAMEfinder, DaMiRseq, debrowser, DELocal, DEP, derfinderPlot, DESpace, DEsubs, DExMA, DiffBind, diffcyt, diffHic, distinct, DMRcate, Doscheda, dreamlet, DRIMSeq, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, EpiMix, erccdashboard, EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, gg4way, gINTomics, Glimma, GRaNIE, GWAS.BAYES, HarmonizR, hermes, HERON, hipathia, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, lemur, limmaGUI, LimROTS, Linnorm, lipidr, lmdme, mastR, MatrixQCvis, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, miloR, minfi, MIRit, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, NanoMethViz, nethet, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PanomiR, pcaExplorer, PECA, PepSetTest, pepStat, phantasus, phenomis, phenoTest, PhosR, PolySTest, POMA, POWSC, projectR, PRONE, psichomics, qmtools, qPLEXanalyzer, qsea, RegEnrich, regsplice, RNAseqCovarImpute, roastgsa, ROSeq, RTN, RTopper, satuRn, scClassify, scone, scran, ScreenR, scviR, seqsetvis, shinyepico, SingleCellSignalR, singleCellTK, sparrow, speckle, standR, STATegRa, Statial, sva, timecourse, TOP, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, unifiedWMWqPCR, vsclust, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, signatureSearchData, spatialLIBD, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, cinaR, DiPALM, dsb, easybio, easyDifferentialGeneCoexpression, eLNNpairedCov, Grouphmap, GSEMA, GWASbyCluster, INCATome, lfproQC, lilikoi, limorhyde2, lipidomeR, MetAlyzer, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, MSclassifR, newIMVC, nlcv, OncoSubtype, Patterns, plfMA, promor, RANKS, RCPA, RPPanalyzer, scBio, scGOclust, scRNAtools, scROSHI, ssizeRNA, statVisual, tinyarray, TransProR, treediff, wrProteo suggestsMe: ABarray, ADaCGH2, Biobase, biobroom, BiocSet, BioNet, BioQC, broadSeq, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, Damsel, DAPAR, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, EnMCB, extraChIPs, fgsea, fishpond, gage, GeoTcgaData, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, iSEEde, isobar, ivygapSE, les, lumi, lute, MAST, methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, qsvaR, raer, randRotation, recountmethylation, ribosomeProfilingQC, rtracklayer, Rvisdiff, spatialHeatmap, SpliceWiz, stageR, subSeq, systemPipeR, tadar, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, bugphyzz, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, DGEobj.utils, GiANT, hexbin, limorhyde, LPS, maGUI, NACHO, pctax, Platypus, pmartR, protti, RepeatedHighDim, SCdeconR, seqgendiff, Seurat, simphony, st, volcano3D, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 6 Package: limmaGUI Version: 1.83.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: d52b811ebb3b4565ecb47d7c79b387d7 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: devel git_last_commit: 39141a0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/limmaGUI_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limmaGUI_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limmaGUI_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limmaGUI_1.83.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 11 Package: limpca Version: 1.3.0 Depends: R (>= 3.5.0) Imports: ggplot2, stringr, plyr, ggrepel, reshape2, grDevices, graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci, tidyverse, methods, stats, SummarizedExperiment, S4Vectors Suggests: BiocStyle, pander, rmarkdown, car, gridExtra, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 89a075372214446c17a8c5368826f71d NeedsCompilation: no Title: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods Description: This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design. biocViews: StatisticalMethod, PrincipalComponent, Regression, Visualization, ExperimentalDesign, MultipleComparison, GeneExpression, Metabolomics Author: Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb], Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio [dtc], Manon Martin [aut, cre] (ORCID: ), Nadia Benaiche [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/limpca, https://manonmartin.github.io/limpca/ VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/limpca/issues git_url: https://git.bioconductor.org/packages/limpca git_branch: devel git_last_commit: 8e4f4ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/limpca_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/limpca_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/limpca_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/limpca_1.3.0.tgz vignettes: vignettes/limpca/inst/doc/limpca.html, vignettes/limpca/inst/doc/Trout.html, vignettes/limpca/inst/doc/UCH.html vignetteTitles: Get started with limpca, Analysis of the Trout dataset with limpca, Analysis of the UCH dataset with limpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limpca/inst/doc/limpca.R, vignettes/limpca/inst/doc/Trout.R, vignettes/limpca/inst/doc/UCH.R dependencyCount: 142 Package: LimROTS Version: 0.99.12 Depends: R (>= 4.5.0), SummarizedExperiment Imports: limma, stringr, qvalue, utils, stats, BiocParallel, basilisk.utils, S4Vectors, dplyr Suggests: BiocStyle, ggplot2, magick, testthat (>= 3.0.0), knitr, rmarkdown, caret, ROTS License: Artistic-2.0 Archs: x64 MD5sum: c693cd89a4cd223be257d3fb5370d677 NeedsCompilation: no Title: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics and Metabolomics Data Description: Differential expression analysis is a prevalent method utilised in the examination of diverse biological data. The reproducibility-optimized test statistic (ROTS) modifies a t-statistic based on the data's intrinsic characteristics and ranks features according to their statistical significance for differential expression between two or more groups (f-statistic). Focussing on proteomics and metabolomics, the current ROTS implementation cannot account for technical or biological covariates such as MS batches or gender differences among the samples. Consequently, we developed LimROTS, which employs a reproducibility-optimized test statistic utilising the limma methodology to simulate complex experimental designs. LimROTS is a hybrid method integrating empirical bayes and reproducibility-optimized statistics for robust analysis of proteomics and metabolomics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology, Metabolomics, mRNAMicroarray Author: Ali Mostafa Anwar [aut, cre] (ORCID: ), Leo Lahti [aut, ths] (ORCID: ), Akewak Jeba [aut, ctb] (ORCID: ), Eleanor Coffey [aut, ths] (ORCID: ) Maintainer: Ali Mostafa Anwar URL: https://github.com/AliYoussef96/LimROTS, https://aliyoussef96.github.io/LimROTS/ VignetteBuilder: knitr BugReports: https://github.com/AliYoussef96/LimROTS/issues git_url: https://git.bioconductor.org/packages/LimROTS git_branch: devel git_last_commit: aac524a git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/LimROTS_0.99.12.tar.gz win.binary.ver: bin/windows/contrib/4.5/LimROTS_0.99.12.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LimROTS_0.99.12.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LimROTS_0.99.12.tgz vignettes: vignettes/LimROTS/inst/doc/LimROTS.html vignetteTitles: LimROTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LimROTS/inst/doc/LimROTS.R dependencyCount: 86 Package: lineagespot Version: 1.11.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 42c31ddd6813956bc966b40280df08d9 NeedsCompilation: no Title: Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing Description: Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages. biocViews: VariantDetection, VariantAnnotation, Sequencing Author: Nikolaos Pechlivanis [aut, cre] (ORCID: ), Maria Tsagiopoulou [aut], Maria Christina Maniou [aut], Anastasis Togkousidis [aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris [aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut], Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris Karapantsios [aut], Stamatia Laidou [aut], Elisavet Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E. Psomopoulos [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/lineagespot VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues git_url: https://git.bioconductor.org/packages/lineagespot git_branch: devel git_last_commit: 9ae0440 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lineagespot_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lineagespot_1.11.0.zip vignettes: vignettes/lineagespot/inst/doc/lineagespot.html vignetteTitles: lineagespot User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R dependencyCount: 84 Package: LinkHD Version: 1.21.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: e4a3547ac5521befec0bdaf0db8288d4 NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: devel git_last_commit: bdab22c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LinkHD_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LinkHD_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LinkHD_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LinkHD_1.21.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 133 Package: Linnorm Version: 2.31.0 Depends: R(>= 4.1.0) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer, moments, testthat, matrixStats License: MIT + file LICENSE Archs: x64 MD5sum: 24193fd4aef073ee40073d0b40b6f515 NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip Maintainer: Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: devel git_last_commit: 5c4a716 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Linnorm_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Linnorm_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Linnorm_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Linnorm_2.31.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem suggestsMe: SCdeconR dependencyCount: 67 Package: LinTInd Version: 1.11.0 Depends: R (>= 4.0), ggplot2, parallel, stats, S4Vectors Imports: data.tree, reshape2, networkD3, stringdist, purrr, ape, cowplot, ggnewscale, stringr, dplyr, rlist, pheatmap, Biostrings, pwalign, IRanges, BiocGenerics(>= 0.36.1), ggtree Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: ae20498a4e1bee1d7f6a7f71e7e0159e NeedsCompilation: no Title: Lineage tracing by indels Description: When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagProcess() function will help you to extract indels for each cell or read. TagDist() function will help you to calculate the similarity between each pair of groups across the indwells they contain. BuildTree() function will help you to reconstruct a tree. PlotTree() function will help you to visualize the tree. biocViews: SingleCell, CRISPR, Alignment Author: Luyue Wang [aut, cre], Bin Xiang [ctb], Hengxin Liu [ctb], Wu Wei [ths] Maintainer: Luyue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinTInd git_branch: devel git_last_commit: 7c091a5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LinTInd_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LinTInd_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LinTInd_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LinTInd_1.11.0.tgz vignettes: vignettes/LinTInd/inst/doc/tutorial.html vignetteTitles: LinTInd - tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LinTInd/inst/doc/tutorial.R dependencyCount: 108 Package: lionessR Version: 1.21.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE Archs: x64 MD5sum: 9ff331891af088bca45c13ffb94715fb NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (ORCID: ), Ping-Han Hsieh [cre] (ORCID: ) Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: devel git_last_commit: 26ea400 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lionessR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lionessR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lionessR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lionessR_1.21.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 37 Package: lipidr Version: 2.21.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, spelling, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 9caab0a213c881642f9881b0db1148e3 NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (ORCID: ), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: devel git_last_commit: ce50d70 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lipidr_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lipidr_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lipidr_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lipidr_2.21.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R suggestsMe: rgoslin dependencyCount: 128 Package: LiquidAssociation Version: 1.61.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) Archs: x64 MD5sum: 31ae064cfd3b7fe7b81c7cdb6c1ec44b NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: devel git_last_commit: 55fef47 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LiquidAssociation_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LiquidAssociation_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LiquidAssociation_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LiquidAssociation_1.61.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 64 Package: lisaClust Version: 1.15.6 Depends: R (>= 4.0) Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.explore, spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr, stats, data.table, dplyr, tidyr, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, pheatmap, spatstat.random, lifecycle, simpleSeg, rlang, Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>=2) MD5sum: 5d179b9a2588b550af1ba82492671969 NeedsCompilation: no Title: lisaClust: Clustering of Local Indicators of Spatial Association Description: lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution. biocViews: SingleCell, CellBasedAssays, Spatial Author: Ellis Patrick [aut, cre], Nicolas Canete [aut], Nicholas Robertson [ctb], Alex Qin [ctb], Shreya shreya.rajeshrao@sydney.edu.au Rao [ctb] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/lisaClust/, https://github.com/ellispatrick/lisaClust VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/lisaClust/issues git_url: https://git.bioconductor.org/packages/lisaClust git_branch: devel git_last_commit: a5842b8 git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/lisaClust_1.15.6.tar.gz win.binary.ver: bin/windows/contrib/4.5/lisaClust_1.15.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lisaClust_1.15.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lisaClust_1.15.6.tgz vignettes: vignettes/lisaClust/inst/doc/lisaClust.html vignetteTitles: "Inroduction to lisaClust" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R suggestsMe: Statial, spicyWorkflow dependencyCount: 223 Package: lmdme Version: 1.49.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 44aac4b2d23bd9bbc6c1515c6d011fa1 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: devel git_last_commit: 2d18be3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lmdme_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lmdme_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lmdme_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lmdme_1.49.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 9 Package: LOBSTAHS Version: 1.33.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: fd54d0828739d76f7b1932d52ac57282 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut], Daniel Lowenstein [aut] Maintainer: Henry Holm , Daniel Lowenstein , James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: devel git_last_commit: 39dadf2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LOBSTAHS_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LOBSTAHS_1.33.0.zip vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependsOnMe: PtH2O2lipids dependencyCount: 161 Package: loci2path Version: 1.27.0 Depends: R (>= 3.5.0) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: e0dae695c17ecda22c76323f2b3f8c8a NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: devel git_last_commit: 1995675 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/loci2path_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/loci2path_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/loci2path_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/loci2path_1.27.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 52 Package: logicFS Version: 2.27.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 7623c570de1e33c11c74c9352947d5b0 NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: devel git_last_commit: 235b3ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/logicFS_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/logicFS_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/logicFS_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/logicFS_2.27.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: LOLA Version: 1.37.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 Archs: x64 MD5sum: 363de05d168ba6d92a1b8a609fbb6a05 NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: devel git_last_commit: 9642694 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LOLA_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LOLA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LOLA_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LOLA_1.37.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, MAGAR, MIRA, ramr dependencyCount: 36 Package: LoomExperiment Version: 1.25.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, BiocIO Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate License: Artistic-2.0 MD5sum: ba633778f94d20f7cfc04c6fde6dd70d NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: devel git_last_commit: 0f3982f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LoomExperiment_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LoomExperiment_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LoomExperiment_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LoomExperiment_1.25.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R dependsOnMe: OSCA.intro suggestsMe: adverSCarial, hca dependencyCount: 51 Package: LPE Version: 1.81.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 5b7b5b701904d47f44302eef1b30a295 NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: devel git_last_commit: 8bda3fa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LPE_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LPE_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LPE_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LPE_1.81.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: PLPE suggestsMe: ABarray dependencyCount: 1 Package: lpNet Version: 2.39.0 Depends: lpSolve, KEGGgraph License: Artistic License 2.0 MD5sum: f9819edb87c43ea5d390033b60269c6b NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: devel git_last_commit: b90bc0c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lpNet_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lpNet_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lpNet_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lpNet_2.39.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 16 Package: lpsymphony Version: 1.35.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: e893d4b6265274a8dad484928a11dcfc NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb], Mike Smith [ctb] (ORCID: ) Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/lpsymphony/issues git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: devel git_last_commit: 464901c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lpsymphony_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lpsymphony_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lpsymphony_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lpsymphony_1.35.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW suggestsMe: oppr, prioritizr dependencyCount: 0 Package: LRBaseDbi Version: 2.17.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle, AnnotationHub License: Artistic-2.0 MD5sum: 63da5e711d8adbd89aaa7e98ebeac09c NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: devel git_last_commit: caa85c6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LRBaseDbi_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LRBaseDbi_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LRBaseDbi_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LRBaseDbi_2.17.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 46 Package: LRcell Version: 1.15.0 Depends: R (>= 4.1), ExperimentHub, AnnotationHub Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2, testthat License: MIT + file LICENSE MD5sum: ce4351b489a5c27c0995f3f38eae0afa NeedsCompilation: no Title: Differential cell type change analysis using Logistic/linear Regression Description: The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus). biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression, GeneExpression, DifferentialExpression Author: Wenjing Ma [cre, aut] (ORCID: ) Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: devel git_last_commit: 0eb2d9f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/LRcell_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LRcell_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LRcell_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LRcell_1.15.0.tgz vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html vignetteTitles: LRcell Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R suggestsMe: LRcellTypeMarkers dependencyCount: 95 Package: lumi Version: 2.59.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 28e1ca205848f036da048212f25e96e4 NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/lumi git_branch: devel git_last_commit: 130abf1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lumi_2.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lumi_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lumi_2.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/lumi_2.59.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, mvoutData importsMe: arrayMvout, ffpe, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, maGUI dependencyCount: 165 Package: lute Version: 1.3.0 Depends: R (>= 4.3.0), stats, methods, utils, SummarizedExperiment, SingleCellExperiment, BiocGenerics Imports: S4Vectors, Biobase, scran, dplyr, ggplot2 Suggests: nnls, knitr, testthat, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub, DelayedMatrixStats, BisqueRNA, DelayedArray License: Artistic-2.0 MD5sum: b8687466aff61cc3bbb5ba2857027de6 NeedsCompilation: no Title: Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments Description: Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms. biocViews: RNASeq, Sequencing, SingleCell, Coverage, Transcriptomics, Normalization Author: Sean K Maden [cre, aut] (ORCID: ), Stephanie Hicks [aut] (ORCID: ) Maintainer: Sean K Maden URL: https://github.com/metamaden/lute VignetteBuilder: knitr BugReports: https://github.com/metamaden/lute/issues git_url: https://git.bioconductor.org/packages/lute git_branch: devel git_last_commit: 449b0aa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/lute_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/lute_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/lute_1.3.0.tgz vignettes: vignettes/lute/inst/doc/lute_algorithm_classes.html, vignettes/lute/inst/doc/lute_pseudobulk_example.html, vignettes/lute/inst/doc/lute_users_guide.html vignetteTitles: lute algorithm classes, Pseudobulk cell size rescaling example, The lute user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lute/inst/doc/lute_algorithm_classes.R, vignettes/lute/inst/doc/lute_pseudobulk_example.R, vignettes/lute/inst/doc/lute_users_guide.R dependencyCount: 96 Package: LymphoSeq Version: 1.35.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: 6588aae39db741b0243ef7851eaf756f NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: devel git_last_commit: 1b80972 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/LymphoSeq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/LymphoSeq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/LymphoSeq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/LymphoSeq_1.35.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 98 Package: M3C Version: 1.29.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: e29d84f459a19b7d3660b91a851de577 NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: devel git_last_commit: cbbb878 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/M3C_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/M3C_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/M3C_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/M3C_1.29.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: lilikoi suggestsMe: parameters dependencyCount: 60 Package: M3Drop Version: 1.33.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods, scater Suggests: ROCR, knitr, M3DExampleData, SingleCellExperiment, Seurat, Biobase License: GPL (>=2) MD5sum: cc80f385313821a8002a83a847d7350d NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: devel git_last_commit: a036ab7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/M3Drop_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/M3Drop_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/M3Drop_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/M3Drop_1.33.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 168 Package: m6Aboost Version: 1.13.0 Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1) Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods, IRanges, ExperimentHub Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: fae8b724965d4ea206ac7ef7e375415e NeedsCompilation: no Title: m6Aboost Description: This package can help user to run the m6Aboost model on their own miCLIP2 data. The package includes functions to assign the read counts and get the features to run the m6Aboost model. The miCLIP2 data should be stored in a GRanges object. More details can be found in the vignette. biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/ZarnackGroup/m6Aboost VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/m6Aboost/issues git_url: https://git.bioconductor.org/packages/m6Aboost git_branch: devel git_last_commit: 123d4ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/m6Aboost_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/m6Aboost_1.13.0.zip vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html vignetteTitles: m6Aboost Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R dependencyCount: 170 Package: Maaslin2 Version: 1.21.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4, tibble Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE MD5sum: 58399be64acc8898df5c1ece40b6f857 NeedsCompilation: no Title: "Multivariable Association Discovery in Population-scale Meta-omics Studies" Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: devel git_last_commit: 3dacd30 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-18 source.ver: src/contrib/Maaslin2_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Maaslin2_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Maaslin2_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Maaslin2_1.21.0.tgz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: Maaslin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: Macarron, MMUPHin dependencyCount: 127 Package: Macarron Version: 1.11.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table, dynamicTreeCut, Maaslin2, plyr, stats, psych, xml2, httr, RJSONIO, logging, methods, utils Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE Archs: x64 MD5sum: 3bc52e549adcf74a774deb03ff89c7a8 NeedsCompilation: no Title: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets Description: Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition. biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction, Clustering Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/macarron VignetteBuilder: knitr BugReports: https://forum.biobakery.org/c/microbial-community-profiling/macarron git_url: https://git.bioconductor.org/packages/Macarron git_branch: devel git_last_commit: 60ff933 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-19 source.ver: src/contrib/Macarron_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Macarron_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Macarron_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Macarron_1.11.0.tgz vignettes: vignettes/Macarron/inst/doc/Macarron.html vignetteTitles: Macarron hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Macarron/inst/doc/Macarron.R dependencyCount: 205 Package: maCorrPlot Version: 1.77.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: f0bbcb146935882624964755d530fb4a NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: devel git_last_commit: 25a3a06 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/maCorrPlot_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maCorrPlot_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maCorrPlot_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maCorrPlot_1.77.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACSQuantifyR Version: 1.21.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: 9ef89ae19d9e9ecd54e2fc6bd6fd3133 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: devel git_last_commit: 9364d97 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MACSQuantifyR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MACSQuantifyR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MACSQuantifyR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MACSQuantifyR_1.21.0.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 85 Package: MACSr Version: 1.15.0 Depends: R (>= 4.1.0) Imports: utils, reticulate, S4Vectors, methods, basilisk, ExperimentHub, AnnotationHub Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata License: BSD_3_clause + file LICENSE MD5sum: 2a76a759c9641a3fc76c0f9e2bdc9cae NeedsCompilation: no Title: MACS: Model-based Analysis for ChIP-Seq Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3. biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology Author: Philippa Doherty [aut], Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSr git_branch: devel git_last_commit: 260457d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MACSr_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MACSr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MACSr_1.15.0.tgz vignettes: vignettes/MACSr/inst/doc/MACSr.html vignetteTitles: MACSr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MACSr/inst/doc/MACSr.R dependencyCount: 79 Package: made4 Version: 1.81.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 299454c9160173d1875c7957cdc1c520 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: devel git_last_commit: 654689e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/made4_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/made4_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/made4_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/made4_1.81.0.tgz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: omicade4 dependencyCount: 50 Package: MADSEQ Version: 1.33.0 Depends: R (>= 3.5.0), rjags (>= 4.6) Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) MD5sum: 1bea13413919a5ca6773026477217be2 NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues git_url: https://git.bioconductor.org/packages/MADSEQ git_branch: devel git_last_commit: 4a2b4e3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MADSEQ_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MADSEQ_1.33.0.zip vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R dependencyCount: 109 Package: maftools Version: 2.23.0 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy, pheatmap LinkingTo: Rhtslib, zlibbioc Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors, curl License: MIT + file LICENSE MD5sum: 18a09feeacc5f123dd0bfadf466fbd29 NeedsCompilation: yes Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] (ORCID: ) Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: devel git_last_commit: 689045c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/maftools_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maftools_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maftools_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maftools_2.23.0.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/cnv_analysis.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R, vignettes/maftools/inst/doc/cnv_analysis.R, vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R dependsOnMe: GNOSIS importsMe: CaMutQC, CIMICE, katdetectr, musicatk, TCGAWorkflow, aplotExtra, pathwayTMB, PMAPscore, ProgModule, Rediscover, sigminer, SMDIC, ssMutPA suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks, oncoPredict dependencyCount: 29 Package: MAGAR Version: 1.15.1 Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table, UpSetR, reshape2, jsonlite, methods, ff, argparse, impute, RnBeads.hg19, RnBeads.hg38, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 MD5sum: 0b432e4b4c4696aab3b071981144ecef NeedsCompilation: no Title: MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data Description: "Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs. biocViews: Regression, Epigenetics, DNAMethylation, SNP, GeneticVariability, MethylationArray, Microarray, CpGIsland, MethylSeq, Sequencing, mRNAMicroarray, Preprocessing, CopyNumberVariation, TwoChannel, ImmunoOncology, DifferentialMethylation, BatchEffect, QualityControl, DataImport, Network, Clustering, GraphAndNetwork Author: Michael Scherer [cre, aut] (ORCID: ) Maintainer: Michael Scherer URL: https://github.com/MPIIComputationalEpigenetics/MAGAR VignetteBuilder: knitr BugReports: https://github.com/MPIIComputationalEpigenetics/MAGAR/issues git_url: https://git.bioconductor.org/packages/MAGAR git_branch: devel git_last_commit: 42a555a git_last_commit_date: 2024-11-13 Date/Publication: 2024-12-04 source.ver: src/contrib/MAGAR_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAGAR_1.15.1.zip vignettes: vignettes/MAGAR/inst/doc/MAGAR.html vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R dependencyCount: 200 Package: magpie Version: 1.7.0 Depends: R (>= 4.3.0) Imports: utils, rtracklayer, Matrix, matrixStats, stats, S4Vectors, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi, aod, BiocParallel, DESeq2, openxlsx, RColorBrewer, reshape2, TRESS Suggests: knitr, rmarkdown, kableExtra, RUnit, TBX20BamSubset, BiocGenerics, BiocStyle License: MIT + file LICENSE MD5sum: ddbf635aebbf9eb2adc5ca4d196282a7 NeedsCompilation: no Title: MeRIP-Seq data Analysis for Genomic Power Investigation and Evaluation Description: This package aims to perform power analysis for the MeRIP-seq study. It calculates FDR, FDC, power, and precision under various study design parameters, including but not limited to sample size, sequencing depth, and testing method. It can also output results into .xlsx files or produce corresponding figures of choice. biocViews: Epitranscriptomics, DifferentialMethylation, Sequencing, RNASeq, Software Author: Daoyu Duan [aut, cre], Zhenxing Guo [aut] Maintainer: Daoyu Duan URL: https://github.com/dxd429/magpie VignetteBuilder: knitr BugReports: https://github.com/dxd429/magpie/issues git_url: https://git.bioconductor.org/packages/magpie git_branch: devel git_last_commit: c0a41dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/magpie_1.7.0.tar.gz vignettes: vignettes/magpie/inst/doc/magpie.html vignetteTitles: magpie Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/magpie/inst/doc/magpie.R dependencyCount: 110 Package: magrene Version: 1.9.0 Depends: R (>= 4.2.0) Imports: utils, stats, BiocParallel Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: f2d799330e076b034554601ca78a7e16 NeedsCompilation: no Title: Motif Analysis In Gene Regulatory Networks Description: magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index. biocViews: Software, MotifDiscovery, NetworkEnrichment, SystemsBiology, GraphAndNetwork Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/magrene VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/magrene git_url: https://git.bioconductor.org/packages/magrene git_branch: devel git_last_commit: 99513a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/magrene_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/magrene_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/magrene_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/magrene_1.9.0.tgz vignettes: vignettes/magrene/inst/doc/magrene.html vignetteTitles: Introduction to magrene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/magrene/inst/doc/magrene.R dependencyCount: 13 Package: MAI Version: 1.13.0 Depends: R (>= 3.5.0) Imports: caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: d6c551de5de0d3ef93bdbb99bbe46aae NeedsCompilation: no Title: Mechanism-Aware Imputation Description: A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present. biocViews: Software, Metabolomics, StatisticalMethod, Classification Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut] Maintainer: Jonathan Dekermanjian URL: https://github.com/KechrisLab/MAI VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MAI/issues git_url: https://git.bioconductor.org/packages/MAI git_branch: devel git_last_commit: 300bbe0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MAI_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAI_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAI_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAI_1.13.0.tgz vignettes: vignettes/MAI/inst/doc/UsingMAI.html vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MAI/inst/doc/UsingMAI.R dependencyCount: 174 Package: MAIT Version: 1.41.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: cf000a22ce3df1611fd5baa5df66347d NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: devel git_last_commit: 2f8e62b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MAIT_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAIT_1.41.0.zip vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R dependencyCount: 204 Package: makecdfenv Version: 1.83.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) MD5sum: 68ae66482d2a629691bc4c394d722c5b NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: devel git_last_commit: d0be981 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/makecdfenv_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/makecdfenv_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/makecdfenv_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/makecdfenv_1.83.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 13 Package: MANOR Version: 1.79.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: ae88a458aeb7a3c5f8126ece30b161cf NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: devel git_last_commit: 8992a9b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MANOR_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MANOR_1.79.0.zip vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.77.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 1340722a0b75db9b6e0e7cc933b5a886 NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: devel git_last_commit: e26a745 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MantelCorr_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MantelCorr_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MantelCorr_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MantelCorr_1.77.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: MAPFX Version: 1.3.0 Depends: R (>= 4.4.0) Imports: flowCore, Biobase, stringr, uwot, iCellR, igraph, ggplot2, RColorBrewer, Rfast, ComplexHeatmap, circlize, glmnetUtils, e1071, xgboost, parallel, pbapply, reshape2, gtools, utils, stats, cowplot, methods, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 3de62c929db78d0e5eee7a6f9987ff80 NeedsCompilation: no Title: MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments Description: MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well. biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics, Clustering Author: Hsiao-Chi Liao [aut, cre] (ORCID: ), Agus Salim [ctb], infinityFlow [ctb] Maintainer: Hsiao-Chi Liao URL: https://github.com/HsiaoChiLiao/MAPFX VignetteBuilder: knitr BugReports: https://github.com/HsiaoChiLiao/MAPFX/issues git_url: https://git.bioconductor.org/packages/MAPFX git_branch: devel git_last_commit: 0dd36ba git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MAPFX_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAPFX_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAPFX_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MAPFX_1.3.0.tgz vignettes: vignettes/MAPFX/inst/doc/MAPFX_Vignette.html vignetteTitles: MAPFX: MAssively Parallel Flow cytometry Xplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAPFX/inst/doc/MAPFX_Vignette.R dependencyCount: 186 Package: maPredictDSC Version: 1.45.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 23436536a1c669e8bae065e6d8a14c0e NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: devel git_last_commit: 0d81f55 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/maPredictDSC_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maPredictDSC_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maPredictDSC_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maPredictDSC_1.45.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 136 Package: mapscape Version: 1.31.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 7d22d916214ef55aea2d28e48805b932 NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: devel git_last_commit: 49e78f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mapscape_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mapscape_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mapscape_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mapscape_1.31.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 36 Package: mariner Version: 1.7.0 Depends: R (>= 4.2.0) Imports: methods, S4Vectors, BiocGenerics, BiocManager, GenomicRanges, InteractionSet, data.table, stats, rlang, glue, assertthat, plyranges, magrittr, dbscan, purrr, progress, GenomeInfoDb, strawr (>= 0.0.91), DelayedArray, HDF5Array, abind, BiocParallel, IRanges, SummarizedExperiment, rhdf5, plotgardener, RColorBrewer, colourvalues, utils, grDevices, graphics, grid Suggests: knitr, testthat (>= 3.0.0), dplyr, rmarkdown, ExperimentHub, marinerData License: GPL-3 MD5sum: cf9eb7c62347bee10b72f2c94b965573 NeedsCompilation: no Title: Mariner: Explore the Hi-Cs Description: Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization. biocViews: FunctionalGenomics, Visualization, HiC Author: Eric Davis [aut, cre] (ORCID: ) Maintainer: Eric Davis URL: http://ericscottdavis.com/mariner/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mariner git_branch: devel git_last_commit: 58efcf6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mariner_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mariner_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mariner_1.7.0.tgz vignettes: vignettes/mariner/inst/doc/introduction_to_mariner.html vignetteTitles: Introduction to mariner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mariner/inst/doc/introduction_to_mariner.R dependencyCount: 110 Package: marr Version: 1.17.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) MD5sum: 4a75724c83dd168bddd9d8e5992cd476 NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: devel git_last_commit: 88e44ee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/marr_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/marr_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/marr_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/marr_1.17.0.tgz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 66 Package: marray Version: 1.85.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: e49a983ac059de43679075ec7db44546 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: devel git_last_commit: a8c55f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/marray_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/marray_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/marray_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/marray_1.85.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, MSstatsShiny, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 7 Package: martini Version: 1.27.0 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, memoise (>= 2.0.0), methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils, LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>= 2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>= 0.12.2), knitr, testthat, readr, rmarkdown License: GPL-3 MD5sum: 842f347bba4d11e375539b5c8ffe5212 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (ORCID: ), Chloe-Agathe Azencott [aut] (ORCID: ) Maintainer: Hector Climente-Gonzalez URL: https://github.com/hclimente/martini VignetteBuilder: knitr BugReports: https://github.com/hclimente/martini/issues git_url: https://git.bioconductor.org/packages/martini git_branch: devel git_last_commit: 13d35e1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/martini_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/martini_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/martini_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/martini_1.27.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 28 Package: maser Version: 1.25.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: fbf8d77080dbbcb35e1f93eb9a14e7c8 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: devel git_last_commit: 390af67 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/maser_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maser_1.25.0.zip vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 164 Package: maSigPro Version: 1.79.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) Archs: x64 MD5sum: c46be9077376258985c443dc727794ac NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa and Maria Jose Nueda Maintainer: Maria Jose Nueda git_url: https://git.bioconductor.org/packages/maSigPro git_branch: devel git_last_commit: 4df8268 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/maSigPro_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maSigPro_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maSigPro_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maSigPro_1.79.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: maskBAD Version: 1.51.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) Archs: x64 MD5sum: b95b5b7b5925e303cef710b5d2b9444b NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: devel git_last_commit: a9dbb0e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/maskBAD_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/maskBAD_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/maskBAD_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/maskBAD_1.51.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 33 Package: MassArray Version: 1.59.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: b98aead862bab7acec1cff74f78a9710 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: devel git_last_commit: 53960bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MassArray_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MassArray_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MassArray_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MassArray_1.59.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.43.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: ddba7ec47ba72d95cb843fd873946f33 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: devel git_last_commit: faf164e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/massiR_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/massiR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/massiR_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/massiR_1.43.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 15 Package: MassSpecWavelet Version: 1.73.1 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench License: LGPL (>= 2) MD5sum: 47619cc9cbfaa9dd542f3bfc6a300088 NeedsCompilation: yes Title: Peak Detection for Mass Spectrometry data using wavelet-based algorithms Description: Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See ) Maintainer: Sergio Oller Moreno URL: https://github.com/zeehio/MassSpecWavelet VignetteBuilder: knitr BugReports: http://github.com/zeehio/MassSpecWavelet/issues git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: devel git_last_commit: 5443820 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/MassSpecWavelet_1.73.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MassSpecWavelet_1.73.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MassSpecWavelet_1.73.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MassSpecWavelet_1.73.1.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.html, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html vignetteTitles: Finding local maxima, Using the MassSpecWavelet package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.R, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, NMRphasing, Rnmr1D, speaq suggestsMe: downlit dependencyCount: 0 Package: MAST Version: 1.33.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress, Matrix Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: 32f6eba6564fd970099df0d9e2beaa44 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: devel git_last_commit: 35654e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MAST_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MAST_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MAST_1.33.0.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R dependsOnMe: POWSC importsMe: celaref, singleCellTK, DWLS suggestsMe: clusterExperiment, EWCE, MARVEL, Seurat dependencyCount: 74 Package: mastR Version: 1.7.0 Depends: R (>= 4.3.0) Imports: AnnotationDbi, Biobase, dplyr, edgeR, ggplot2, ggpubr, graphics, grDevices, GSEABase, limma, Matrix, methods, msigdb, org.Hs.eg.db, patchwork, SeuratObject, SingleCellExperiment, stats, SummarizedExperiment, tidyr, utils Suggests: BiocManager, BiocStyle, BisqueRNA, clusterProfiler, ComplexHeatmap, depmap, enrichplot, ggrepel, ggvenn, gridExtra, jsonlite, knitr, rmarkdown, RobustRankAggreg, rvest, scuttle, singscore, splatter, testthat (>= 3.0.0), UpSetR License: MIT + file LICENSE Archs: x64 MD5sum: 252eee1ddc99ee4911e84c7a0135e774 NeedsCompilation: no Title: Markers Automated Screening Tool in R Description: mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology. biocViews: Software, GeneExpression, Transcriptomics, DifferentialExpression, Visualization Author: Jinjin Chen [aut, cre] (ORCID: ), Ahmed Mohamed [aut, ctb] (ORCID: ), Chin Wee Tan [ctb] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/mastR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/mastR/issues git_url: https://git.bioconductor.org/packages/mastR git_branch: devel git_last_commit: 492c795 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mastR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mastR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mastR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mastR_1.7.0.tgz vignettes: vignettes/mastR/inst/doc/mastR_Demo.html vignetteTitles: mastR_Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mastR/inst/doc/mastR_Demo.R dependencyCount: 150 Package: matchBox Version: 1.49.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 7504469c7453fac03c630e09963ca421 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: devel git_last_commit: 61179a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/matchBox_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/matchBox_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/matchBox_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/matchBox_1.49.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.19.0 Depends: matrixStats (>= 1.4.1) Imports: methods Suggests: Matrix, sparseMatrixStats, SparseArray, DelayedArray, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 Archs: x64 MD5sum: b2071f9f5549f0bd15d7b2f230eff839 NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (ORCID: ), Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: devel git_last_commit: dbb20c2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MatrixGenerics_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixGenerics_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MatrixGenerics_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MatrixGenerics_1.19.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, SparseArray, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: atena, CoreGx, crisprDesign, CTexploreR, demuxSNP, DESeq2, dreamlet, escape, FLAMES, genefilter, glmGamPoi, imcRtools, lemur, lineagespot, mia, miaSim, miloR, MinimumDistance, PDATK, RaggedExperiment, RAIDS, RCSL, scater, scFeatures, scone, scPCA, scran, scuttle, scviR, shinyMethyl, spatzie, StabMap, tadar, TENxIO, tLOH, tpSVG, transformGamPoi, universalmotif, VanillaICE, homosapienDEE2CellScore, spatialLIBD, SCIntRuler suggestsMe: bnem, cypress, MungeSumstats, scrapper dependencyCount: 2 Package: MatrixQCvis Version: 1.15.0 Depends: DT (>= 0.33), SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ExperimentHub (>= 2.6.0), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>= 3.47.12), MASS (>= 7.3-58.1), methods (>= 4.1.0), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), sva (>= 3.52.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), httr (>= 1.4.7), jpeg (>= 0.1-10), knitr (>= 1.33), statmod (>= 1.5.0), testthat (>= 3.0.2) License: GPL-3 MD5sum: 7258db01c4ff90f9f63112c3dab430ef NeedsCompilation: no Title: Shiny-based interactive data-quality exploration for omics data Description: Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object. biocViews: Visualization, ShinyApps, GUI, QualityControl, DimensionReduction, Metabolomics, Proteomics, Transcriptomics Author: Thomas Naake [aut, cre] (ORCID: ), Wolfgang Huber [aut] (ORCID: ) Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: devel git_last_commit: ae918bf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MatrixQCvis_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixQCvis_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MatrixQCvis_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MatrixQCvis_1.15.0.tgz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: Shiny-based interactive data quality exploration of omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 189 Package: MatrixRider Version: 1.39.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 MD5sum: c4847c22fad2b69f68b7d3fc3bb15b2c NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: devel git_last_commit: bbdf4b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MatrixRider_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MatrixRider_1.39.0.zip vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 127 Package: matter Version: 2.9.1 Depends: R (>= 4.4), BiocParallel, Matrix, methods Imports: BiocGenerics, ProtGenerics, digest, irlba, stats, stats4, graphics, grDevices, parallel, utils LinkingTo: BH Suggests: BiocStyle, knitr, testthat, plotly License: Artistic-2.0 | file LICENSE MD5sum: b12c0c4344cba943826e65cf10bff3bf NeedsCompilation: yes Title: Out-of-core statistical computing and signal processing Description: Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images. biocViews: Infrastructure, DataRepresentation, DataImport, DimensionReduction, Preprocessing Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/matter/issues git_url: https://git.bioconductor.org/packages/matter git_branch: devel git_last_commit: bcfe9fb git_last_commit_date: 2024-12-04 Date/Publication: 2024-12-05 source.ver: src/contrib/matter_2.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/matter_2.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/matter_2.9.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/matter_2.9.1.tgz vignettes: vignettes/matter/inst/doc/matter2-guide.html, vignettes/matter/inst/doc/matter2-signal.html vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data structures, 2. Matter 2: Signal and image processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/matter/inst/doc/matter2-guide.R, vignettes/matter/inst/doc/matter2-signal.R dependsOnMe: CardinalIO importsMe: Cardinal dependencyCount: 24 Package: MBAmethyl Version: 1.41.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: ff2716b3d2938644363eefcf8deeb5e0 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: devel git_last_commit: 6cbb183 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MBAmethyl_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBAmethyl_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBAmethyl_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBAmethyl_1.41.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.41.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 697dcb91cdc8971e69c0a5486536c7c4 NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: devel git_last_commit: 8637000 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MBASED_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBASED_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBASED_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBASED_1.41.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 48 Package: MBCB Version: 1.61.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>=2) Archs: x64 MD5sum: c700a207dac0eba122cf49509ed28184 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Bo Yao URL: https://qbrc.swmed.edu/ git_url: https://git.bioconductor.org/packages/MBCB git_branch: devel git_last_commit: 6753198 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MBCB_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBCB_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBCB_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBCB_1.61.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: mbkmeans Version: 1.23.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: 9e40b317b70dfc708c339aba2d0b4b5a NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: devel git_last_commit: 446bb72 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/mbkmeans_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mbkmeans_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mbkmeans_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mbkmeans_1.23.0.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R importsMe: clusterExperiment suggestsMe: bluster, concordexR, scDblFinder dependencyCount: 91 Package: mBPCR Version: 1.61.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: d823908377a8c5be1a9b2c25a36707b7 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: devel git_last_commit: 4db7f5f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mBPCR_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mBPCR_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mBPCR_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mBPCR_1.61.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 122 Package: MBQN Version: 2.19.0 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData, rmarkdown Suggests: knitr License: GPL-3 + file LICENSE MD5sum: cc3d7aaf1b2951dc9ce4a89b3f257db4 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Eva Brombacher [aut, cre] (ORCID: ), Clemens Kreutz [aut, ctb] (ORCID: ), Ariane Schad [aut, ctb] (ORCID: ) Maintainer: Eva Brombacher URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: devel git_last_commit: a95ce36 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MBQN_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBQN_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBQN_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBQN_2.19.0.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 111 Package: mbQTL Version: 1.7.0 Depends: R (>= 4.3.0) Imports: MatrixEQTL, dplyr, ggplot2, readxl, stringr, tidyr, metagenomeSeq, pheatmap, broom, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 85f92ce7f6aab04b800b8f2dad2976e7 NeedsCompilation: no Title: mbQTL: A package for SNP-Taxa mGWAS analysis Description: mbQTL is a statistical R package for simultaneous 16srRNA,16srDNA (microbial) and variant, SNP, SNV (host) relationship, correlation, regression studies. We apply linear, logistic and correlation based statistics to identify the relationships of taxa, genus, species and variant, SNP, SNV in the infected host. We produce various statistical significance measures such as P values, FDR, BC and probability estimation to show significance of these relationships. Further we provide various visualization function for ease and clarification of the results of these analysis. The package is compatible with dataframe, MRexperiment and text formats. biocViews: SNP, Microbiome, WholeGenome, Metagenomics, StatisticalMethod, Regression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Steven Schiff [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh URL: "https://github.com/Mercedeh66/mbQTL" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mbQTL git_branch: devel git_last_commit: 4621e93 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-18 source.ver: src/contrib/mbQTL_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mbQTL_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mbQTL_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mbQTL_1.7.0.tgz vignettes: vignettes/mbQTL/inst/doc/mbQTL_Vignette.html vignetteTitles: MbQTL_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbQTL/inst/doc/mbQTL_Vignette.R dependencyCount: 77 Package: MBttest Version: 1.35.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: f2d4086110f55e17d7345e8cb59d2017 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: devel git_last_commit: 63b7016 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MBttest_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MBttest_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBttest_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MBttest_1.35.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: MCbiclust Version: 1.31.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 Archs: x64 MD5sum: 4659db6642e5713b4d7bf8a7e9f3ac48 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: devel git_last_commit: 990c62b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MCbiclust_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MCbiclust_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MCbiclust_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MCbiclust_1.31.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 135 Package: mCSEA Version: 1.27.2 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 Archs: x64 MD5sum: 3d0465dc8bfd663a894bf4f52e21753e NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: devel git_last_commit: 641202d git_last_commit_date: 2024-12-03 Date/Publication: 2024-12-03 source.ver: src/contrib/mCSEA_1.27.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/mCSEA_1.27.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mCSEA_1.27.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mCSEA_1.27.2.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R suggestsMe: shinyepico dependencyCount: 172 Package: mdp Version: 1.27.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: 588ab4d7442accae88bd5e9d980ec566 NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: devel git_last_commit: ddf480d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mdp_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mdp_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mdp_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mdp_1.27.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 36 Package: mdqc Version: 1.69.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: fc049aaeeda5d92ceaf9fce4d7bfef1e NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: devel git_last_commit: cdd5233 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mdqc_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mdqc_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mdqc_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mdqc_1.69.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.27.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 28425c9dd25093d6107d0cc9a782e8bd NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: devel git_last_commit: fe18875 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MDTS_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MDTS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MDTS_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MDTS_1.27.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 61 Package: MEAL Version: 1.37.0 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: dedc3428aacb39a205b0526c49b4d42d NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: devel git_last_commit: e76b5d7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEAL_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEAL_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEAL_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEAL_1.37.0.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 229 Package: MeasurementError.cor Version: 1.79.0 License: LGPL Archs: x64 MD5sum: dcaa2d67fc9df1c6bf5e718ab80e69aa NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: devel git_last_commit: c1d9386 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MeasurementError.cor_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MeasurementError.cor_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MeasurementError.cor_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MeasurementError.cor_1.79.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEAT Version: 1.19.0 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 8bb2b1e7c10068df11a3eb7f301580b8 NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues git_url: https://git.bioconductor.org/packages/MEAT git_branch: devel git_last_commit: 035a45b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEAT_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEAT_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEAT_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEAT_1.19.0.tgz vignettes: vignettes/MEAT/inst/doc/MEAT.html vignetteTitles: MEAT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAT/inst/doc/MEAT.R dependencyCount: 180 Package: MEB Version: 1.21.0 Depends: R (>= 3.6.0) Imports: e1071, edgeR, scater, stats, wrswoR, SummarizedExperiment, SingleCellExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: f9dca4ee9030303b13f6c2d9b4408cba NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data Description: This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: devel git_last_commit: a09a3ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEB_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEB_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEB_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEB_1.21.0.tgz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 116 Package: MEDIPS Version: 1.59.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: 6e2e0bf791cb1f5b8f86c554685c8eb1 NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: devel git_last_commit: 34d1345 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEDIPS_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEDIPS_1.59.0.zip vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R dependencyCount: 108 Package: MEDME Version: 1.67.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: cbda960041707868b250c4ce738b3fd0 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: devel git_last_commit: 0fb1ead git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEDME_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEDME_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEDME_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEDME_1.67.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 96 Package: megadepth Version: 1.17.0 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 MD5sum: aad19f0a9047a62d2421b6d9f6af1124 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (ORCID: ), David Zhang [aut, cre] (ORCID: ) Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: devel git_last_commit: 019b9f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/megadepth_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/megadepth_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/megadepth_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/megadepth_1.17.0.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R dependencyCount: 83 Package: MEIGOR Version: 1.41.0 Depends: R (>= 4.0), Rsolnp, snowfall, deSolve, CNORode Suggests: CellNOptR, knitr, BiocStyle License: GPL-3 MD5sum: 7815f22ea399df020271024135d76611 NeedsCompilation: no Title: MEIGOR - MEtaheuristics for bIoinformatics Global Optimization Description: MEIGOR provides a comprehensive environment for performing global optimization tasks in bioinformatics and systems biology. It leverages advanced metaheuristic algorithms to efficiently search the solution space and is specifically tailored to handle the complexity and high-dimensionality of biological datasets. This package supports various optimization routines and is integrated with Bioconductor's infrastructure for a seamless analysis workflow. biocViews: SystemsBiology, Optimization, Software Author: Jose A. Egea [aut, cre], David Henriques [aut], Alexandre Fdez. Villaverde [aut], Thomas Cokelaer [aut] Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: devel git_last_commit: 16a8117 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MEIGOR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MEIGOR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MEIGOR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MEIGOR_1.41.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.html vignetteTitles: MEIGOR: a software suite based on metaheuristics for global optimization in systems biology and bioinformatics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R dependencyCount: 80 Package: Melissa Version: 1.23.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 87343b3d5cbab1919694bd3d7770bc40 NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: devel git_last_commit: cffba8a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Melissa_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Melissa_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Melissa_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Melissa_1.23.0.tgz vignettes: vignettes/Melissa/inst/doc/process_files.html, vignettes/Melissa/inst/doc/run_melissa.html vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and impute scBS-seq data using Melissa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Melissa/inst/doc/process_files.R, vignettes/Melissa/inst/doc/run_melissa.R dependencyCount: 113 Package: memes Version: 1.15.0 Depends: R (>= 4.1) Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2, ggseqlogo, magrittr, matrixStats, methods, patchwork, processx, purrr, rlang, readr, stats, tools, tibble, tidyr, utils, usethis, universalmotif (>= 1.9.3), xml2 Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0), knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1), rmarkdown, covr License: MIT + file LICENSE MD5sum: 234b6f00b317a9645289b0a2d7e98930 NeedsCompilation: no Title: motif matching, comparison, and de novo discovery using the MEME Suite Description: A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows. biocViews: DataImport, FunctionalGenomics, GeneRegulation, MotifAnnotation, MotifDiscovery, SequenceMatching, Software Author: Spencer Nystrom [aut, cre, cph] (ORCID: ) Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: devel git_last_commit: ba252fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/memes_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/memes_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/memes_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/memes_1.15.0.tgz vignettes: vignettes/memes/inst/doc/core_ame.html, vignettes/memes/inst/doc/core_dreme.html, vignettes/memes/inst/doc/core_fimo.html, vignettes/memes/inst/doc/core_tomtom.html, vignettes/memes/inst/doc/install_guide.html, vignettes/memes/inst/doc/integrative_analysis.html, vignettes/memes/inst/doc/tidy_motifs.html vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif Discovery Using DREME, Motif Scanning using FIMO, Motif Comparison using TomTom, Install MEME, ChIP-seq Analysis, Tidying Motif Metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/memes/inst/doc/core_ame.R, vignettes/memes/inst/doc/core_dreme.R, vignettes/memes/inst/doc/core_fimo.R, vignettes/memes/inst/doc/core_tomtom.R, vignettes/memes/inst/doc/install_guide.R, vignettes/memes/inst/doc/integrative_analysis.R, vignettes/memes/inst/doc/tidy_motifs.R importsMe: MotifPeeker dependencyCount: 111 Package: Mergeomics Version: 1.35.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 8f674d70061e68b3b5b0ca8b790fbc37 NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: devel git_last_commit: 2b53e79 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Mergeomics_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mergeomics_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mergeomics_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mergeomics_1.35.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.43.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: e5ed009cacf9955b5b767ea0ed0e79cf NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: devel git_last_commit: cae5246 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MeSHDbi_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MeSHDbi_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MeSHDbi_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MeSHDbi_1.43.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshes, meshr, scTensor dependencyCount: 46 Package: meshes Version: 1.33.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim (>= 2.31.2), methods, utils, AnnotationHub, MeSHDbi, yulab.utils (>= 0.1.5) Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: fd832c17ee3212d61c969dfd173b078a NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: devel git_last_commit: 708aa6d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/meshes_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/meshes_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/meshes_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/meshes_1.33.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 131 Package: meshr Version: 2.13.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 9ff5c4327428edbd764ceb42460fb78d NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues git_url: https://git.bioconductor.org/packages/meshr git_branch: devel git_last_commit: 5a2870d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/meshr_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/meshr_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/meshr_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/meshr_2.13.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.html vignetteTitles: AnnotationHub-style MeSH ORA Framework from BioC 3.14 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 85 Package: MesKit Version: 1.17.0 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 075ff91e7f217840b51e32fde48aef05 NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (ORCID: ), Jianyu Chen [aut, ctb] (ORCID: ), Xin Wang [aut, ctb] (ORCID: ) Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: devel git_last_commit: 471c27c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MesKit_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MesKit_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MesKit_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MesKit_1.17.0.tgz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R importsMe: CaMutQC dependencyCount: 102 Package: messina Version: 1.43.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: b65784727fd19bb657c1b19caa1b17ac NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: devel git_last_commit: c06bc28 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/messina_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/messina_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/messina_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/messina_1.43.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 41 Package: metabCombiner Version: 1.17.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0), methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 45cb07d515c3f0429c1f56bb845bb569 NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: devel git_last_commit: e8a8053 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metabCombiner_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabCombiner_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabCombiner_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabCombiner_1.17.0.tgz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 88 Package: metabinR Version: 1.9.0 Depends: R (>= 4.3) Imports: methods, rJava Suggests: BiocStyle, cvms, data.table, dplyr, ggplot2, gridExtra, knitr, R.utils, rmarkdown, sabre, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: 6c7384560261ef376c9202d803b1cf51 NeedsCompilation: no Title: Abundance and Compositional Based Binning of Metagenomes Description: Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution. biocViews: Classification, Clustering, Microbiome, Sequencing, Software Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/metabinR SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/metabinR/issues git_url: https://git.bioconductor.org/packages/metabinR git_branch: devel git_last_commit: e7264a2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metabinR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabinR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabinR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabinR_1.9.0.tgz vignettes: vignettes/metabinR/inst/doc/metabinR_vignette.html vignetteTitles: metabinR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabinR/inst/doc/metabinR_vignette.R dependencyCount: 2 Package: MetaboAnnotation Version: 1.11.1 Depends: R (>= 4.0.0) Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics, methods, S4Vectors, Spectra (>= 1.13.2), BiocParallel, SummarizedExperiment, QFeatures, AnnotationHub, graphics, CompoundDb Suggests: testthat, knitr, msdata, BiocStyle, rmarkdown, plotly, shiny, shinyjs, DT, microbenchmark, mzR Enhances: RMariaDB, RSQLite License: Artistic-2.0 MD5sum: 282801ac454fe53196e1de2ac2ff7e41 NeedsCompilation: no Title: Utilities for Annotation of Metabolomics Data Description: High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Michael Witting [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Carolin Huber [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Nir Shachaf [ctb] Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboAnnotation VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboAnnotation/issues git_url: https://git.bioconductor.org/packages/MetaboAnnotation git_branch: devel git_last_commit: 05c3086 git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-21 source.ver: src/contrib/MetaboAnnotation_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboAnnotation_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboAnnotation_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboAnnotation_1.11.1.tgz vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html vignetteTitles: Annotation of MS-based Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R dependencyCount: 145 Package: MetaboCoreUtils Version: 1.15.0 Depends: R (>= 4.0) Imports: utils, MsCoreUtils, BiocParallel, methods, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, robustbase License: Artistic-2.0 MD5sum: a2426892090d2a6dbe93d0a4460e8dea NeedsCompilation: no Title: Core Utils for Metabolomics Data Description: MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [aut] (ORCID: ), Andrea Vicini [aut], Liesa Salzer [ctb] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Stravs [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues git_url: https://git.bioconductor.org/packages/MetaboCoreUtils git_branch: devel git_last_commit: 8267815 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetaboCoreUtils_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboCoreUtils_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboCoreUtils_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboCoreUtils_1.15.0.tgz vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html vignetteTitles: Core Utils for Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, Spectra, xcms dependencyCount: 24 Package: metabolomicsWorkbenchR Version: 1.17.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, httptest, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 MD5sum: 246e73924cf2c8511a44523b28721ec0 NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: devel git_last_commit: 3a7c458 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metabolomicsWorkbenchR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabolomicsWorkbenchR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabolomicsWorkbenchR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabolomicsWorkbenchR_1.17.0.tgz vignettes: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html vignetteTitles: Example using structToolbox, Introduction_to_metabolomicsWorkbenchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R suggestsMe: fobitools, MetMashR dependencyCount: 70 Package: metabomxtr Version: 1.41.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 28e7955f78ad73da48c11e90b94c36ad NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: devel git_last_commit: 26b046c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metabomxtr_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metabomxtr_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metabomxtr_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metabomxtr_1.41.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 58 Package: MetaboSignal Version: 1.37.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 6a7bba34e719c3c949dc28f4f2f8f886 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: devel git_last_commit: 3fa4cf5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetaboSignal_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaboSignal_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaboSignal_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaboSignal_1.37.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 198 Package: metaCCA Version: 1.35.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 88769fb8eca66677a89e1ad14cd6c966 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: devel git_last_commit: e5665b0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metaCCA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaCCA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metaCCA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metaCCA_1.35.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.29.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr, rmarkdown License: GPL (>= 2) MD5sum: e647a7d4b668f6bec0f8e3b3180cb833 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: devel git_last_commit: 19f7705 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetaCyto_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaCyto_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaCyto_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaCyto_1.29.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 105 Package: metagene2 Version: 1.23.0 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 368fe76b0052ba6800b2c546db3234e4 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: devel git_last_commit: 2d45109 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metagene2_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metagene2_1.23.0.zip vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 94 Package: metagenomeSeq Version: 1.49.1 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW License: Artistic-2.0 Archs: x64 MD5sum: 45962347184bd5bf032f51f1b76d1661 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: devel git_last_commit: 96adc06 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/metagenomeSeq_1.49.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/metagenomeSeq_1.49.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metagenomeSeq_1.49.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metagenomeSeq_1.49.1.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: microbiomeExplorer, etec16s importsMe: Maaslin2, mbQTL suggestsMe: interactiveDisplay, scTreeViz, Wrench dependencyCount: 32 Package: metahdep Version: 1.65.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: x64 MD5sum: e77a3bf50c2297d6876c9f58552166d9 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: devel git_last_commit: c7cca38 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metahdep_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metahdep_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metahdep_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metahdep_1.65.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.43.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 0502c32326efc36cd9dbbfcff2291688 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (ORCID: ), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: devel git_last_commit: 5510242 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metaMS_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaMS_1.43.0.zip vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 163 Package: MetaNeighbor Version: 1.27.0 Depends: R(>= 3.5) Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4), Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1.2), SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph, dplyr, tidyr, tibble, ggplot2 Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2), UpSetR License: MIT + file LICENSE MD5sum: 22151c9d5519939d9718fc25825d775d NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Stephan Fischer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: devel git_last_commit: 7dda94c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetaNeighbor_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaNeighbor_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaNeighbor_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaNeighbor_1.27.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 78 Package: MetaPhOR Version: 1.9.0 Depends: R (>= 4.2.0) Imports: utils, ggplot2, ggrepel, stringr, pheatmap, grDevices, stats, clusterProfiler, RecordLinkage, RCy3 Suggests: BiocStyle, knitr, rmarkdown, kableExtra License: Artistic-2.0 MD5sum: d4d489f98830118b0968c75f6ccea5f3 NeedsCompilation: no Title: Metabolic Pathway Analysis of RNA Description: MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models. biocViews: Metabolomics, RNASeq, Pathways, GeneExpression, DifferentialExpression, KEGG, Sequencing, Microarray Author: Emily Isenhart [aut, cre], Spencer Rosario [aut] Maintainer: Emily Isenhart SystemRequirements: Cytoscape (>= 3.9.0) for the cytoPath() examples VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaPhOR git_branch: devel git_last_commit: ffbf2ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetaPhOR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetaPhOR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetaPhOR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetaPhOR_1.9.0.tgz vignettes: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.html vignetteTitles: MetaPhOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.R dependencyCount: 169 Package: metapod Version: 1.15.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 83055b599c8739cef63a9715b7024ec0 NeedsCompilation: yes Title: Meta-Analyses on P-Values of Differential Analyses Description: Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. biocViews: MultipleComparison, DifferentialPeakCalling Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapod git_branch: devel git_last_commit: 25083c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metapod_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metapod_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metapod_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metapod_1.15.0.tgz vignettes: vignettes/metapod/inst/doc/overview.html vignetteTitles: Meta-analysis strategies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapod/inst/doc/overview.R importsMe: csaw, mumosa, scp, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.13.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: c6d646b0b9e814967c1a78eee4da9cd7 NeedsCompilation: no Title: Conducts pathway test of metabolomics data using a weighted permutation test Description: The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. biocViews: Technology, MassSpectrometry, Metabolomics, Pathways Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: devel git_last_commit: 9fe5c68 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metapone_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metapone_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metapone_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metapone_1.13.0.tgz vignettes: vignettes/metapone/inst/doc/metapone.html vignetteTitles: metapone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapone/inst/doc/metapone.R dependencyCount: 60 Package: metaSeq Version: 1.47.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 8d614846aea4a4df54b6abc1ea578648 NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: devel git_last_commit: dbdffb0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metaSeq_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/metaSeq_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/metaSeq_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/metaSeq_1.47.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 15 Package: metaseqR2 Version: 1.19.0 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, txdbmaker, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocStyle, BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) MD5sum: 626d4e5baa42c294306423099ac2e57b NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues git_url: https://git.bioconductor.org/packages/metaseqR2 git_branch: devel git_last_commit: 22e1b29 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/metaseqR2_1.19.0.tar.gz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 239 Package: MetCirc Version: 1.37.0 Depends: R (>= 4.4), amap (>= 0.8), circlize (>= 0.4.16), scales (>= 1.3.0), shiny (>= 1.8.1.1), Spectra (>= 1.15.3) Imports: ggplot2 (>= 3.5.1), MsCoreUtils (>= 1.17.0), S4Vectors (>= 0.43.1) Suggests: BiocGenerics, graphics (>= 4.4), grDevices (>= 4.4), knitr (>= 1.48), testthat (>= 3.2.1.1) License: GPL (>= 3) MD5sum: 1fddca9edfc5ebb0a015e28297366668 NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ShinyApps, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: devel git_last_commit: 6a5a2cb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetCirc_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetCirc_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetCirc_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetCirc_1.37.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.html vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 84 Package: methimpute Version: 1.29.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: f46dc5293a86c0863ac128d35e338c4e NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: devel git_last_commit: 4287169 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methimpute_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methimpute_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methimpute_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methimpute_1.29.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 63 Package: methInheritSim Version: 1.29.0 Depends: R (>= 3.5.0) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 626ae577ea8cf5d83fdd2db864ea53c5 NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: devel git_last_commit: 7089998 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-27 source.ver: src/contrib/methInheritSim_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methInheritSim_1.29.0.zip vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 111 Package: MethPed Version: 1.35.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: ede50947817c844604e578df05c1340b NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: devel git_last_commit: 0b7abdc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MethPed_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethPed_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethPed_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethPed_1.35.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 9 Package: MethReg Version: 1.17.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, sesame, AnnotationHub, ExperimentHub, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress, utils, openxlsx, JASPAR2024, RSQLite, TFBSTools Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, R.utils, doParallel, reshape2, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, data.table, downloader License: GPL-3 Archs: x64 MD5sum: 1778e4faf112a29a20b0735a383248ab NeedsCompilation: no Title: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription Description: Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis. biocViews: MethylationArray, Regression, GeneExpression, Epigenetics, GeneTarget, Transcription Author: Tiago Silva [aut, cre] (ORCID: ), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: devel git_last_commit: ba3e766 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-13 source.ver: src/contrib/MethReg_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethReg_1.17.0.zip vignettes: vignettes/MethReg/inst/doc/MethReg.html vignetteTitles: MethReg: estimating regulatory potential of DNA methylation in gene transcription hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethReg/inst/doc/MethReg.R dependencyCount: 178 Package: methrix Version: 1.21.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, S4Vectors, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, BSgenome.Hsapiens.UCSC.hg19, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 951d21b904aafafc8e8d367f5d3b0e42 NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (ORCID: ), Reka Toth [aut] (ORCID: ), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues git_url: https://git.bioconductor.org/packages/methrix git_branch: devel git_last_commit: 7d44855 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methrix_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methrix_1.21.0.zip vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R dependencyCount: 94 Package: MethTargetedNGS Version: 1.39.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings, pwalign Imports: utils, graphics, stats License: Artistic-2.0 MD5sum: 5afeaea1d57c8a230f658e49b5a3b97b NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: devel git_last_commit: 70f3c0d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MethTargetedNGS_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethTargetedNGS_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethTargetedNGS_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethTargetedNGS_1.39.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 51 Package: MethylAid Version: 1.41.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 89d2ad8277efe9fcbffe1f74a8a6e7c1 NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: devel git_last_commit: 1f7b25a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MethylAid_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylAid_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethylAid_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethylAid_1.41.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependsOnMe: MethylAidData dependencyCount: 170 Package: methylCC Version: 1.21.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: GPL-3 Archs: x64 MD5sum: 0f251a910b62982276cf18939c553e25 NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: devel git_last_commit: 109b2f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/methylCC_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylCC_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylCC_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylCC_1.21.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The methylCC user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 161 Package: methylclock Version: 1.13.0 Depends: R (>= 4.1.0), methylclockData, devtools, quadprog Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute, PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2, ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra, preprocessCore, dynamicTreeCut, planet LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE MD5sum: d1bfc0736c547fc4faf5fe26d5fb224d NeedsCompilation: yes Title: Methylclock - DNA methylation-based clocks Description: This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/methylclock VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/methylclock/issues git_url: https://git.bioconductor.org/packages/methylclock git_branch: devel git_last_commit: 5639edc git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/methylclock_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylclock_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylclock_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylclock_1.13.0.tgz vignettes: vignettes/methylclock/inst/doc/methylclock.html vignetteTitles: DNAm age using diffrent methylation clocks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methylclock/inst/doc/methylclock.R dependencyCount: 298 Package: methylGSA Version: 1.25.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 MD5sum: ee554c9854b75d29eb846989d4ecf7ae NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: devel git_last_commit: fb33fc3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylGSA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylGSA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylGSA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylGSA_1.25.0.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 216 Package: methyLImp2 Version: 1.3.0 Depends: R (>= 4.3.0), ChAMPdata Imports: BiocParallel, parallel, stats, methods, corpcor, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5c7b3cd77349f038770508aa536085d3 NeedsCompilation: no Title: Missing value estimation of DNA methylation data Description: This package allows to estimate missing values in DNA methylation data. methyLImp method is based on linear regression since methylation levels show a high degree of inter-sample correlation. Implementation is parallelised over chromosomes since probes on different chromosomes are usually independent. Mini-batch approach to reduce the runtime in case of large number of samples is available. biocViews: DNAMethylation, Microarray, Software, MethylationArray, Regression Author: Pietro Di Lena [aut] (ORCID: ), Anna Plaksienko [aut, cre] (ORCID: ), Claudia Angelini [aut] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Anna Plaksienko URL: https://github.com/annaplaksienko/methyLImp2 VignetteBuilder: knitr BugReports: https://github.com/annaplaksienko/methyLImp2/issues git_url: https://git.bioconductor.org/packages/methyLImp2 git_branch: devel git_last_commit: 613be1f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methyLImp2_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methyLImp2_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methyLImp2_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methyLImp2_1.3.0.tgz vignettes: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.html vignetteTitles: methyLImp2 vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.R dependencyCount: 49 Package: methylInheritance Version: 1.31.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 Archs: x64 MD5sum: 539d83c776186fa5499766439f911ca0 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: devel git_last_commit: 9150e3e git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-27 source.ver: src/contrib/methylInheritance_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylInheritance_1.31.0.zip vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 114 Package: methylKit Version: 1.33.1 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: 0e2a41cdf1d4c051b0871cc782396538 NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Blume [aut], Katarzyna Wreczycka [ctb] Maintainer: Altuna Akalin , Alexander Blume URL: https://github.com/al2na/methylKit SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/al2na/methylKit/issues git_url: https://git.bioconductor.org/packages/methylKit git_branch: devel git_last_commit: c249499 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/methylKit_1.33.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylKit_1.33.1.zip vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: deconvR, methInheritSim, methylInheritance dependencyCount: 107 Package: MethylMix Version: 2.37.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: 79cb2a389812aa8a9504b8f336fadcd5 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: devel git_last_commit: 6007130 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MethylMix_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylMix_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MethylMix_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MethylMix_2.37.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 52 Package: methylMnM Version: 1.45.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: 146c0c3f7a0fde28cf65238fb4176c24 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: devel git_last_commit: ed89f51 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylMnM_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylMnM_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylMnM_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylMnM_1.45.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 11 Package: methylPipe Version: 1.41.0 Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) MD5sum: 9e13412107e56ef86841dd664400369e NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: devel git_last_commit: 9ff081a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylPipe_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylPipe_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylPipe_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylPipe_1.41.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 164 Package: methylscaper Version: 1.15.0 Depends: R (>= 4.4.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, pwalign, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, devtools, R.utils License: GPL-2 MD5sum: 0ef9c5099e9e130e40d4e4a2bb480fc1 NeedsCompilation: no Title: Visualization of Methylation Data Description: methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. biocViews: DNAMethylation, Epigenetics, Sequencing, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda URL: https://github.com/rhondabacher/methylscaper/ VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/methylscaper/issues git_url: https://git.bioconductor.org/packages/methylscaper git_branch: devel git_last_commit: 271ec91 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylscaper_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylscaper_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylscaper_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylscaper_1.15.0.tgz vignettes: vignettes/methylscaper/inst/doc/methylScaper.html vignetteTitles: Using methylscaper to visualize joint methylation and nucleosome occupancy data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R dependencyCount: 110 Package: MethylSeekR Version: 1.47.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: 84f4645d7ced612df68dab046006111f NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: devel git_last_commit: bd272df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MethylSeekR_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MethylSeekR_1.47.0.zip vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 84 Package: methylSig Version: 1.19.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, GenomeInfoDb, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 MD5sum: c1800d6484851cf0105f11f7a20e675d NeedsCompilation: no Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: devel git_last_commit: 3088278 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylSig_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylSig_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylSig_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylSig_1.19.0.tgz vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html, vignettes/methylSig/inst/doc/using-methylSig.html vignetteTitles: Updating methylSig code, Using methylSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R, vignettes/methylSig/inst/doc/using-methylSig.R dependencyCount: 92 Package: methylumi Version: 2.53.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio, GenomicFeatures Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 Archs: x64 MD5sum: 31cc8c7298e5e9841808cf20d5baa8d2 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: devel git_last_commit: 491e60e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/methylumi_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/methylumi_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/methylumi_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/methylumi_2.53.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, missMethyl dependencyCount: 159 Package: MetID Version: 1.25.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: ea17309fd7fc3ebe061d85dcec0c97bf NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: devel git_last_commit: 0715d00 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetID_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetID_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetID_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetID_1.25.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 132 Package: MetMashR Version: 1.1.0 Depends: R (>= 4.3.0), struct Imports: dplyr, methods, httr, scales, ggthemes, ggplot2, utils, rlang, cowplot, stats Suggests: covr, httptest, knitr, rmarkdown, testthat (>= 3.0.0), rgoslin, DT, RSQLite, CompoundDb, BiocStyle, BiocFileCache, msPurity, ChemmineOB, rsvg, metabolomicsWorkbenchR, KEGGREST, plyr, magick, structToolbox, RVenn, ggVennDiagram, patchwork, XML, GO.db, tidytext, tidyr, tidyselect, ComplexUpset, jsonlite, openxlsx License: GPL-3 MD5sum: 2542c9686fad9562d67df4419c4679a1 NeedsCompilation: no Title: Metabolite Mashing with R Description: A package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included. biocViews: WorkflowStep, Metabolomics, KEGG Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://computational-metabolomics.github.io/MetMashR/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/MetMashR/issues git_url: https://git.bioconductor.org/packages/MetMashR git_branch: devel git_last_commit: e797206 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetMashR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetMashR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetMashR_1.1.0.tgz vignettes: vignettes/MetMashR/inst/doc/annotate_mixtures.html, vignettes/MetMashR/inst/doc/exploring_mtox.html, vignettes/MetMashR/inst/doc/Extending_MetMashR.html, vignettes/MetMashR/inst/doc/using_MetMashR.html vignetteTitles: Annotation of mixtures of standards, Exploring the MTox700+ library, Extending MetMashR, Using MetMashR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetMashR/inst/doc/annotate_mixtures.R, vignettes/MetMashR/inst/doc/exploring_mtox.R, vignettes/MetMashR/inst/doc/Extending_MetMashR.R, vignettes/MetMashR/inst/doc/using_MetMashR.R dependencyCount: 81 Package: MetNet Version: 1.25.0 Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), corpcor (>= 1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3 (>= 1.7.0), methods (>= 3.5), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>= 1.6.0) License: GPL (>= 3) MD5sum: 8621732d1186aacdb2650d01ec8263fa NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre], Liesa Salzer [ctb] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: devel git_last_commit: 3d26654 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MetNet_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MetNet_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MetNet_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MetNet_1.25.0.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 93 Package: mfa Version: 1.29.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: x64 MD5sum: a67a938b85c271eee088bd744cde3b2e NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: devel git_last_commit: cd71406 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mfa_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mfa_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mfa_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mfa_1.29.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter dependencyCount: 71 Package: Mfuzz Version: 2.67.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 0b43e9ae45320117984fa5c57bcfeb9b NeedsCompilation: no Title: Soft clustering of omics time series data Description: The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: devel git_last_commit: c882261 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Mfuzz_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mfuzz_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mfuzz_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mfuzz_2.67.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, MultiRNAflow importsMe: Patterns suggestsMe: DAPAR, pctax dependencyCount: 17 Package: MGFM Version: 1.41.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 Archs: x64 MD5sum: f97c6d5399eec487078e8de969698eae NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: devel git_last_commit: b6b997c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MGFM_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MGFM_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MGFM_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MGFM_1.41.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 49 Package: MGFR Version: 1.33.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 Archs: x64 MD5sum: 856241c3a0436d2983ce4a6f70287cfa NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: devel git_last_commit: 3887a1b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MGFR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MGFR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MGFR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MGFR_1.33.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier dependencyCount: 72 Package: MGnifyR Version: 1.3.0 Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment, SummarizedExperiment, BiocGenerics Imports: mia, ape, dplyr, httr, methods, plyr, reshape2, S4Vectors, urltools, utils, tidyjson Suggests: biomformat, broom, ggplot2, knitr, rmarkdown, testthat, xml2, BiocStyle, miaViz, vegan, scater, phyloseq, magick License: Artistic-2.0 | file LICENSE MD5sum: 0babca2499c753a778cd83618a0533ac NeedsCompilation: no Title: R interface to EBI MGnify metagenomics resource Description: Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework. biocViews: Infrastructure, DataImport, Metagenomics Author: Tuomas Borman [aut, cre] (ORCID: ), Ben Allen [aut], Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/MGnifyR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/MGnifyR/issues git_url: https://git.bioconductor.org/packages/MGnifyR git_branch: devel git_last_commit: da1771c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MGnifyR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MGnifyR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MGnifyR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MGnifyR_1.3.0.tgz vignettes: vignettes/MGnifyR/inst/doc/MGnifyR_long.html, vignettes/MGnifyR/inst/doc/MGnifyR.html vignetteTitles: MGnifyR,, extended vignette, MGnifyR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MGnifyR/inst/doc/MGnifyR_long.R, vignettes/MGnifyR/inst/doc/MGnifyR.R suggestsMe: iSEEtree dependencyCount: 188 Package: mgsa Version: 1.55.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: 5fc28fbf350fc811940cde8bb5c85bde NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: devel git_last_commit: d873604 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mgsa_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mgsa_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mgsa_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mgsa_1.55.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R dependencyCount: 9 Package: mia Version: 1.15.6 Depends: R (>= 4.0), MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment (>= 1.99.3) Imports: ape, BiocGenerics, BiocParallel, Biostrings, bluster, DECIPHER, decontam, DelayedArray, DelayedMatrixStats, DirichletMultinomial, dplyr, IRanges, MASS, MatrixGenerics, mediation, methods, rbiom, rlang, S4Vectors, scater, scuttle, stats, tibble, tidyr, utils, vegan Suggests: ade4, BiocStyle, biomformat, dada2, knitr, miaViz, microbiomeDataSets, NMF, patchwork, phyloseq, reldist, rhdf5, rmarkdown, stringr, testthat, topicdoc, topicmodels, yaml License: Artistic-2.0 | file LICENSE MD5sum: 141908abcab70c2571bfea04ffd4fb4d NeedsCompilation: no Title: Microbiome analysis Description: mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. biocViews: Microbiome, Software, DataImport Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Sudarshan A. Shetty [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Yang Cao [ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos [ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb], Domenick Braccia [ctb], Basil Courbayre [ctb], Muluh Muluh [ctb], Giulio Benedetti [ctb], Moritz Emanuel Beber [ctb] (ORCID: ), Nitesh Turaga [ctb], Chouaib Benchraka [ctb], Akewak Jeba [ctb], Himmi Lindgren [ctb], Noah De Gunst [ctb], Théotime Pralas [ctb], Shadman Ishraq [ctb], Eineje Ameh [ctb], Artur Sannikov [ctb], Hervé Pagès [ctb], Rajesh Shigdel [ctb], Katariina Pärnänen [ctb], Pande Erawijantari [ctb], Danielle Callan [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/mia VignetteBuilder: knitr BugReports: https://github.com/microbiome/mia/issues git_url: https://git.bioconductor.org/packages/mia git_branch: devel git_last_commit: 2dc3430 git_last_commit_date: 2024-11-15 Date/Publication: 2024-11-15 source.ver: src/contrib/mia_1.15.6.tar.gz win.binary.ver: bin/windows/contrib/4.5/mia_1.15.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mia_1.15.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mia_1.15.6.tgz vignettes: vignettes/mia/inst/doc/mia.html vignetteTitles: mia hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mia/inst/doc/mia.R dependsOnMe: miaViz importsMe: iSEEtree, MGnifyR, curatedMetagenomicData suggestsMe: ANCOMBC, miaSim, philr, bugphyzz, MicrobiomeBenchmarkData, MiscMetabar dependencyCount: 183 Package: miaSim Version: 1.13.0 Depends: TreeSummarizedExperiment Imports: SummarizedExperiment, deSolve, stats, poweRlaw, MatrixGenerics, S4Vectors Suggests: ape, cluster, foreach, doParallel, dplyr, GGally, ggplot2, igraph, network, reshape2, sna, vegan, rmarkdown, knitr, BiocStyle, testthat, mia, miaViz, colourvalues, philentropy License: Artistic-2.0 | file LICENSE MD5sum: 0c7431bc8644aef121f5aaf96542d4dd NeedsCompilation: no Title: Microbiome Data Simulation Description: Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Yagmur Simsek [cre, aut], Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Tuomas Borman [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Yagmur Simsek URL: https://github.com/microbiome/miaSim VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaSim/issues git_url: https://git.bioconductor.org/packages/miaSim git_branch: devel git_last_commit: 667f35c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miaSim_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miaSim_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miaSim_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miaSim_1.13.0.tgz vignettes: vignettes/miaSim/inst/doc/miaSim.html vignetteTitles: miaSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaSim/inst/doc/miaSim.R dependencyCount: 80 Package: miaViz Version: 1.15.2 Depends: R (>= 4.0), ggplot2, ggraph (>= 2.0), mia (>= 1.13.0), SummarizedExperiment, TreeSummarizedExperiment Imports: ape, BiocGenerics, BiocParallel, DelayedArray, DirichletMultinomial, dplyr, ggnewscale, ggrepel, ggplot2, ggraph, ggtree, methods, rlang, scater, S4Vectors, SingleCellExperiment, stats, tibble, tidygraph, tidyr, tidytext, tidytree, viridis Suggests: BiocStyle, bluster, circlize, ComplexHeatmap, ggh4x, knitr, patchwork, rmarkdown, testthat, vegan License: Artistic-2.0 | file LICENSE MD5sum: 8ac1b3ed5b1babc2491920f7a642ca27 NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages. biocViews: Microbiome, Software, Visualization Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Basil Courbayre [ctb], Giulio Benedetti [ctb] (ORCID: ), Théotime Pralas [ctb], Nitesh Turaga [ctb], Chouaib Benchraka [ctb], Sam Hillman [ctb], Muluh Muluh [ctb], Noah De Gunst [ctb], Ely Seraidarian [ctb], Himmi Lindgren [ctb], Vivian Ikeh [ctb] Maintainer: Tuomas Borman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: devel git_last_commit: 94696fa git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-01 source.ver: src/contrib/miaViz_1.15.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/miaViz_1.15.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miaViz_1.15.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miaViz_1.15.2.tgz vignettes: vignettes/miaViz/inst/doc/miaViz.html vignetteTitles: miaViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaViz/inst/doc/miaViz.R importsMe: iSEEtree suggestsMe: MGnifyR, mia, miaSim dependencyCount: 202 Package: MiChip Version: 1.61.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 4cbbe78f97364667ee4dc2ebb4bc549b NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: devel git_last_commit: 8c80304 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MiChip_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiChip_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiChip_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiChip_1.61.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 7 Package: microbiomeExplorer Version: 1.17.0 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 106a659d2b3affb302d44b3417b9bb02 NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: devel git_last_commit: f28fc46 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-18 source.ver: src/contrib/microbiomeExplorer_1.17.0.tar.gz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 192 Package: MicrobiomeProfiler Version: 1.13.0 Depends: R (>= 4.2.0) Imports: clusterProfiler (>= 4.5.2), config, DT, enrichplot, golem, gson, methods, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, stats, utils, yulab.utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0), prettydoc License: GPL-2 MD5sum: 3ff817046593001a77935a80cca99b6d NeedsCompilation: no Title: An R/shiny package for microbiome functional enrichment analysis Description: This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis. biocViews: Microbiome, Software, Visualization,KEGG Author: Guangchuang Yu [cre, aut] (ORCID: ), Meijun Chen [aut] (ORCID: ) Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/, https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler git_branch: devel git_last_commit: 2233f8a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MicrobiomeProfiler_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MicrobiomeProfiler_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiomeProfiler_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MicrobiomeProfiler_1.13.0.tgz vignettes: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R dependencyCount: 155 Package: MicrobiotaProcess Version: 1.19.0 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.4.2), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, cli, plyr, dtplyr, ggtreeExtra, data.table, ggfun (>= 0.1.1) Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, jsonlite, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, ggupset, ggVennDiagram, gghalves, ggalluvial (>= 0.11.1), forcats, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach, parallel, shadowtext, DirichletMultinomial, ggpp, BiocManager License: GPL (>= 3.0) Archs: x64 MD5sum: 8825c1a383ddd335a51218b5b16eeee2 NeedsCompilation: no Title: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: devel git_last_commit: b30acb4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MicrobiotaProcess_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MicrobiotaProcess_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiotaProcess_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MicrobiotaProcess_1.19.0.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R suggestsMe: parafac4microbiome dependencyCount: 110 Package: microRNA Version: 1.65.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 795ddd271ac80d7fce95e13387370d70 NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: devel git_last_commit: fcc2f78 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/microRNA_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/microRNA_1.65.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 26 Package: microSTASIS Version: 1.7.0 Depends: R (>= 4.2.0) Imports: BiocParallel, ggplot2, ggside, grid, rlang, stats, stringr, TreeSummarizedExperiment Suggests: BiocStyle, gghighlight, knitr, rmarkdown, methods, RefManageR, sessioninfo, SingleCellExperiment, SummarizedExperiment, testthat (>= 3.0.0) License: GPL-3 MD5sum: 799daca72ae582ee03265a331de93605 NeedsCompilation: no Title: Microbiota STability ASsessment via Iterative cluStering Description: The toolkit 'µSTASIS', or microSTASIS, has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output. biocViews: GeneticVariability, BiomedicalInformatics, Clustering, MultipleComparison, Microbiome Author: Pedro Sánchez-Sánchez [aut, cre] (ORCID: ), Alfonso Benítez-Páez [aut] (ORCID: ) Maintainer: Pedro Sánchez-Sánchez URL: https://doi.org/10.1093/bib/bbac055 VignetteBuilder: knitr BugReports: https://github.com/BiotechPedro/microSTASIS git_url: https://git.bioconductor.org/packages/microSTASIS git_branch: devel git_last_commit: cc85111 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/microSTASIS_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microSTASIS_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/microSTASIS_1.7.0.tgz vignettes: vignettes/microSTASIS/inst/doc/microSTASIS.html vignetteTitles: Introduction to microSTASIS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/microSTASIS/inst/doc/microSTASIS.R dependencyCount: 91 Package: MICSQTL Version: 1.5.0 Depends: R (>= 4.3.0), SummarizedExperiment, stats Imports: TCA, nnls, purrr, TOAST, magrittr, BiocParallel, ggplot2, ggpubr, ggridges, glue, S4Vectors, dirmult Suggests: testthat (>= 3.0.0), rmarkdown, knitr, BiocStyle License: GPL-3 MD5sum: d55d30ed4d380e2402b880c71b358e77 NeedsCompilation: no Title: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci) Description: Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool. biocViews: GeneExpression, Genetics, Proteomics, RNASeq, Sequencing, SingleCell, Software, Visualization, CellBasedAssays, Coverage Author: Yue Pan [aut] (ORCID: ), Qian Li [aut, cre] (ORCID: ), Iain Carmichael [ctb] Maintainer: Qian Li URL: https://bioconductor.org/packages/MICSQTL, https://github.com/YuePan027/MICSQTL VignetteBuilder: knitr BugReports: https://github.com/YuePan027/MICSQTL/issues git_url: https://git.bioconductor.org/packages/MICSQTL git_branch: devel git_last_commit: 3dd556e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MICSQTL_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MICSQTL_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MICSQTL_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MICSQTL_1.5.0.tgz vignettes: vignettes/MICSQTL/inst/doc/MICSQTL.html vignetteTitles: MICSQTL: Multi-omic deconvolution,, Integration and Cell-type-specific Quantitative Trait Loci hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MICSQTL/inst/doc/MICSQTL.R dependencyCount: 145 Package: midasHLA Version: 1.15.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3) Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1), formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra (>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods, stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1), stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1), utils, qdapTools (>= 1.3.3) Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>= 2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown, seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1), tidyr (>= 1.1.2) License: MIT + file LICENCE MD5sum: 61ec868e8abeb24fb6c006ac8f4f12be NeedsCompilation: no Title: R package for immunogenomics data handling and association analysis Description: MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology. biocViews: CellBiology, Genetics, StatisticalMethod Author: Christian Hammer [aut], Maciej Migdał [aut, cre] Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: devel git_last_commit: b204d6f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/midasHLA_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/midasHLA_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/midasHLA_1.15.0.tgz vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html, vignettes/midasHLA/inst/doc/MiDAS_vignette.html vignetteTitles: MiDAS tutorial, MiDAS quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R, vignettes/midasHLA/inst/doc/MiDAS_vignette.R dependencyCount: 139 Package: miloR Version: 2.3.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), MatrixGenerics, S4Vectors, stats, stringr, methods, igraph, irlba, utils, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices, Rcpp, pracma, numDeriv LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppML Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq, graphics, sparseMatrixStats License: GPL-3 + file LICENSE MD5sum: c790f0f45dc67696dbabf619b2205035 NeedsCompilation: yes Title: Differential neighbourhood abundance testing on a graph Description: Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model. biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software Author: Mike Morgan [aut, cre] (ORCID: ), Emma Dann [aut, ctb] Maintainer: Mike Morgan URL: https://marionilab.github.io/miloR VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/miloR/issues git_url: https://git.bioconductor.org/packages/miloR git_branch: devel git_last_commit: 959804d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miloR_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miloR_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miloR_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miloR_2.3.0.tgz vignettes: vignettes/miloR/inst/doc/milo_contrasts.html, vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html, vignettes/miloR/inst/doc/milo_glmm.html vignetteTitles: Using contrasts for differential abundance testing, Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example, Mixed effect models for Milo DA testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R, vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R, vignettes/miloR/inst/doc/milo_glmm.R dependencyCount: 117 Package: mimager Version: 1.31.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE Archs: x64 MD5sum: 7e367bff081b072678f5a2b536d73c2c NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: devel git_last_commit: b2c3bfc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mimager_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mimager_1.31.0.zip vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 76 Package: mina Version: 1.15.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL MD5sum: 8f10396e8d39186158bb3c75315c649d NeedsCompilation: yes Title: Microbial community dIversity and Network Analysis Description: An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way. biocViews: Software, WorkflowStep Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb] Maintainer: Rui Guan VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: devel git_last_commit: e5829ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mina_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mina_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mina_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mina_1.15.0.tgz vignettes: vignettes/mina/inst/doc/mina.html vignetteTitles: Microbial dIversity and Network Analysis with MINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mina/inst/doc/mina.R dependencyCount: 95 Package: MineICA Version: 1.47.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX, future, future.apply Enhances: doMC License: GPL-2 MD5sum: bf39ae61deb73fffdda3a3a8b7a335ea NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: devel git_last_commit: 0cd440c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MineICA_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MineICA_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MineICA_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MineICA_1.47.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 220 Package: minet Version: 3.65.0 Imports: infotheo License: Artistic-2.0 MD5sum: 99e8824bddd66161401595f5b9e1fdb4 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: devel git_last_commit: 7fc4954 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/minet_3.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/minet_3.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/minet_3.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/minet_3.65.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, RTN, TCGAWorkflow, PRANA, TGS suggestsMe: CNORfeeder, TCGAbiolinks, WGCNA dependencyCount: 1 Package: minfi Version: 1.53.1 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 MD5sum: c22693e1aff1841fdbcffaa6282166d8 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: devel git_last_commit: 3852366 git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-19 source.ver: src/contrib/minfi_1.53.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/minfi_1.53.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/minfi_1.53.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/minfi_1.53.1.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, conumee, methylumi, REMP, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICv2anno.20a1.hg38, IlluminaHumanMethylationEPICv2manifest, IlluminaHumanMethylationMSAanno.ilm10a1.hg38, IlluminaHumanMethylationMSAmanifest, BeadSorted.Saliva.EPIC, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, minfiData, minfiDataEPIC, methylationArrayAnalysis importsMe: deconvR, DMRcate, epimutacions, funtooNorm, iNETgrate, MEAL, MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl, quantro, recountmethylation, shinyepico, shinyMethyl, skewr, HiBED, EMAS suggestsMe: epivizr, epivizrChart, GeoTcgaData, Harman, mCSEA, MultiDataSet, planet, RnBeads, brgedata, epimutacionsData, GSE159526, MLML2R dependencyCount: 144 Package: MinimumDistance Version: 1.51.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: 6c446d351a854c25de7b9cd7848bf916 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: devel git_last_commit: 4c9f42d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MinimumDistance_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MinimumDistance_1.51.0.zip vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 98 Package: MiPP Version: 1.79.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 00a2e2ed6c1148ed40aa7ad33ce803f2 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: devel git_last_commit: 6b61592 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MiPP_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiPP_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiPP_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiPP_1.79.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: miQC Version: 1.15.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: ca67888bba35670b75acf030ff0af54f NeedsCompilation: no Title: Flexible, probabilistic metrics for quality control of scRNA-seq data Description: Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing, Sequencing Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut] Maintainer: Ariel Hippen URL: https://github.com/greenelab/miQC VignetteBuilder: knitr BugReports: https://github.com/greenelab/miQC/issues git_url: https://git.bioconductor.org/packages/miQC git_branch: devel git_last_commit: c01c77f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miQC_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miQC_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miQC_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miQC_1.15.0.tgz vignettes: vignettes/miQC/inst/doc/miQC.html vignetteTitles: miQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miQC/inst/doc/miQC.R dependencyCount: 67 Package: MIRA Version: 1.29.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 0538d6088b26105cb62f537d3a409580 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: devel git_last_commit: 699390a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MIRA_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MIRA_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MIRA_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MIRA_1.29.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 105 Package: MiRaGE Version: 1.49.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL Archs: x64 MD5sum: 34d1d98a87d6700f85bd7d68e29f8667 NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: devel git_last_commit: 99be42d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MiRaGE_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MiRaGE_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MiRaGE_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MiRaGE_1.49.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 47 Package: miRBaseConverter Version: 1.31.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 8af75bd48820d767827c48df9c187f62 NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu Taosheng Xu [aut, cre] (ORCID: ) Maintainer: Taosheng Xu Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: devel git_last_commit: 163132a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRBaseConverter_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRBaseConverter_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRBaseConverter_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRBaseConverter_1.31.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R suggestsMe: EpiMix dependencyCount: 1 Package: miRcomp Version: 1.37.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: fc5d44e1b95b91ffcdf4aec16bc7f2e3 NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: devel git_last_commit: f81710e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRcomp_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRcomp_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRcomp_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRcomp_1.37.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 9 Package: mirIntegrator Version: 1.37.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) Archs: x64 MD5sum: 743b6444050b42ee24034c9c9549767f NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: devel git_last_commit: f0be694 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mirIntegrator_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mirIntegrator_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mirIntegrator_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mirIntegrator_1.37.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 78 Package: MIRit Version: 1.3.0 Depends: MultiAssayExperiment, R (>= 4.4.0) Imports: AnnotationDbi, BiocFileCache, BiocParallel, DESeq2, edgeR, fgsea, genekitr, geneset, ggplot2, ggpubr, graph, graphics, graphite, grDevices, httr, limma, methods, Rcpp, Rgraphviz (>= 2.44.0), rlang, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, biomaRt, BSgenome.Hsapiens.UCSC.hg38, GenomicRanges, ggrepel, ggridges, Gviz, gwasrapidd, knitr, MonoPoly, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 79b5da64cf134ce0232c4c6873b094e7 NeedsCompilation: yes Title: Integrate microRNA and gene expression to decipher pathway complexity Description: MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization. biocViews: Software, GeneRegulation, NetworkEnrichment, NetworkInference, Epigenetics, FunctionalGenomics, SystemsBiology, Network, Pathways, GeneExpression, DifferentialExpression Author: Jacopo Ronchi [aut, cre] (ORCID: ), Maria Foti [fnd] (ORCID: ) Maintainer: Jacopo Ronchi URL: https://github.com/jacopo-ronchi/MIRit VignetteBuilder: knitr BugReports: https://github.com/jacopo-ronchi/MIRit/issues git_url: https://git.bioconductor.org/packages/MIRit git_branch: devel git_last_commit: 57bfd48 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MIRit_1.3.0.tar.gz vignettes: vignettes/MIRit/inst/doc/MIRit.html vignetteTitles: Integrate miRNA and gene expression data with MIRit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRit/inst/doc/MIRit.R dependencyCount: 194 Package: miRLAB Version: 1.37.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown License: GPL (>=2) MD5sum: 480edb6ab8ee3290d8b96efb20418223 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: devel git_last_commit: 8fe9ef1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRLAB_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRLAB_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRLAB_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRLAB_1.37.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 197 Package: miRNAmeConverter Version: 1.35.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: fdc01469baf2925d054a4d02ecad0b93 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: devel git_last_commit: 9ede4bb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRNAmeConverter_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNAmeConverter_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNAmeConverter_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNAmeConverter_1.35.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 54 Package: miRNApath Version: 1.67.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 763f8cf122ffc9d261bc827e60f77d34 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: devel git_last_commit: 510d73a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRNApath_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNApath_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNApath_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNApath_1.67.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.41.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 Archs: x64 MD5sum: 63262de4887759a5cae1ffbf02f2c939 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: devel git_last_commit: 18c2dc7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRNAtap_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRNAtap_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRNAtap_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRNAtap_1.41.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: miRNAtap.db dependencyCount: 55 Package: miRSM Version: 2.3.0 Depends: R (>= 4.4.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, fabia, NMF, biclust, iBBiG, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, clusterProfiler, ReactomePA, DOSE, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: cd76f3ef01f5d2a392b8b476f68e648f NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: devel git_last_commit: b0bce7f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRSM_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRSM_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRSM_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRSM_2.3.0.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 262 Package: miRspongeR Version: 2.11.0 Depends: R (>= 4.4.0) Imports: corpcor, SPONGE, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, linkcomm, utils, Rcpp, org.Hs.eg.db, foreach, doParallel Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 0f1b678ed813c4c15ef45e35c02c79e4 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge regulation Description: This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software, SingleCell, Spatial, RNASeq Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: devel git_last_commit: c3dd126 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/miRspongeR_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/miRspongeR_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/miRspongeR_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/miRspongeR_2.11.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: Identification and analysis of miRNA sponge regulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R dependencyCount: 276 Package: mirTarRnaSeq Version: 1.15.0 Depends: R (>= 4.1.0), ggplot2 Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils, viridis Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE License: MIT + file LICENSE MD5sum: 971c94fc259fce478aec52cbb7b75bde NeedsCompilation: no Title: mirTarRnaSeq Description: mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments. biocViews: miRNA, Regression, Software, Sequencing, SmallRNA, TimeCourse, DifferentialExpression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: devel git_last_commit: 60572a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mirTarRnaSeq_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mirTarRnaSeq_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mirTarRnaSeq_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mirTarRnaSeq_1.15.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 58 Package: missMethyl Version: 1.41.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICv2manifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 MD5sum: d54daf2292df496b0b30973b8aaa27a8 NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale , Calandra Grima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: devel git_last_commit: 9006ed4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/missMethyl_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/missMethyl_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/missMethyl_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/missMethyl_1.41.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 170 Package: missRows Version: 1.27.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 25d78c7e290b7a15bfe385d05d1ce732 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: devel git_last_commit: 560ef6b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/missRows_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/missRows_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/missRows_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/missRows_1.27.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 75 Package: mistyR Version: 1.15.0 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr (>= 1.1.0), filelock, furrr (>= 0.2.0), ggplot2, methods, purrr, ranger, readr (>= 2.0.0), ridge, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, tidyselect (>= 1.2.0), utils, withr Suggests: BiocStyle, covr, earth, future, igraph (>= 1.2.7), iml, kernlab, knitr, MASS, rmarkdown, RSNNS, testthat (>= 3.0.0), xgboost License: GPL-3 MD5sum: b05d468c6b542805b9a2f5ad6f453bc9 NeedsCompilation: no Title: Multiview Intercellular SpaTial modeling framework Description: mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution. biocViews: Software, BiomedicalInformatics, CellBiology, SystemsBiology, Regression, DecisionTree, SingleCell, Spatial Author: Jovan Tanevski [cre, aut] (ORCID: ), Ricardo Omar Ramirez Flores [ctb] (ORCID: ), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski URL: https://saezlab.github.io/mistyR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: devel git_last_commit: 89f8c5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mistyR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mistyR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mistyR_1.15.0.tgz vignettes: vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R dependencyCount: 109 Package: mitch Version: 1.19.3 Depends: R (>= 4.4) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r, kableExtra Suggests: stringi, testthat (>= 2.1.0), HGNChelper, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: CC BY-SA 4.0 + file LICENSE MD5sum: c245734bcbd86f4d596354292a4adca9 NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome, DNAMethylation, MethylationArray Author: Mark Ziemann [aut, cre, cph] (ORCID: ), Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr BugReports: https://github.com/markziemann/mitch git_url: https://git.bioconductor.org/packages/mitch git_branch: devel git_last_commit: 6550cbb git_last_commit_date: 2024-11-26 Date/Publication: 2024-11-26 source.ver: src/contrib/mitch_1.19.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/mitch_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mitch_1.19.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mitch_1.19.3.tgz vignettes: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html, vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: Applying mitch to pathway analysis of Infinium Methylation array data, mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.R, vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 102 Package: mitoClone2 Version: 1.13.0 Depends: R (>= 4.1.0) Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices, graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods, ggplot2 LinkingTo: Rhtslib (>= 1.13.1) Suggests: knitr, rmarkdown, Biostrings, testthat License: GPL-3 MD5sum: 2cb47a06670bc0200fd9dda643c53b3e NeedsCompilation: yes Title: Clonal Population Identification in Single-Cell RNA-Seq Data using Mitochondrial and Somatic Mutations Description: This package primarily identifies variants in mitochondrial genomes from BAM alignment files. It filters these variants to remove RNA editing events then estimates their evolutionary relationship (i.e. their phylogenetic tree) and groups single cells into clones. It also visualizes the mutations and providing additional genomic context. biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell, Alignment Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke [aut] Maintainer: Benjamin Story URL: https://github.com/benstory/mitoClone2 SystemRequirements: GNU make, PhISCS (optional) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitoClone2 git_branch: devel git_last_commit: 020fb39 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mitoClone2_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mitoClone2_1.13.0.zip vignettes: vignettes/mitoClone2/inst/doc/clustering.html, vignettes/mitoClone2/inst/doc/overview.html vignetteTitles: Computation of phylogenetic trees and clustering of mutations, Variant Calling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoClone2/inst/doc/clustering.R, vignettes/mitoClone2/inst/doc/overview.R dependencyCount: 108 Package: mixOmics Version: 6.31.0 Depends: R (>= 4.4.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils, gsignal, rgl Suggests: BiocStyle, knitr, rmarkdown, testthat, microbenchmark, magick License: GPL (>= 2) MD5sum: a180decbfaf23cb4803c8135c33c8afa NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb], Eva Hamrud [ctb, cre] Maintainer: Eva Hamrud URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: devel git_last_commit: c6921b0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mixOmics_6.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mixOmics_6.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mixOmics_6.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mixOmics_6.31.0.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: timeOmics, mixKernel, sgPLS importsMe: AlpsNMR, DepecheR, PLSDAbatch, POMA, Coxmos, Holomics, iTensor, MSclassifR, plsmod, plsRcox, SISIR suggestsMe: autonomics, eoPredData, MetabolomicsBasics, pctax, RVAideMemoire, SelectBoost, sharp dependencyCount: 90 Package: MLInterfaces Version: 1.87.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats, magrittr, SummarizedExperiment Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, BiocStyle, knitr, testthat Enhances: parallel License: LGPL MD5sum: 0ff110bf271f614007d2da209f554f57 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vincent Carey [cre, aut] (ORCID: ), Jess Mar [aut], Jason Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb] (Translated vignettes from Sweave to Rmarkdown / HTML.) Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: devel git_last_commit: 7982d3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MLInterfaces_1.87.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MLInterfaces_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MLInterfaces_1.87.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MLInterfaces_1.87.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf, vignettes/MLInterfaces/inst/doc/MLint_devel.html, vignettes/MLInterfaces/inst/doc/MLprac2_2.html vignetteTitles: MLInterfaces Computer Cluster, MLInterfaces 2.0 -- a new design, A machine learning tutorial tutorial: applications of the Bioconductor MLInterfaces package to gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, dGAselID, nlcv dependencyCount: 124 Package: MLP Version: 1.55.0 Imports: AnnotationDbi, gplots, graphics, stats, utils Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: a47af6ec8389d393f63eb638b6f065ae NeedsCompilation: no Title: Mean Log P Analysis Description: Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available. biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt [aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre], Katarzyna Gorczak [ctb] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: devel git_last_commit: 679711e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MLP_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MLP_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MLP_1.55.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 51 Package: MLSeq Version: 2.25.0 Depends: caret, ggplot2 Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, e1071, kernlab License: GPL(>=2) MD5sum: 0b555115dc4fccffda24aeab8e231544 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: devel git_last_commit: 97e5509 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MLSeq_2.25.0.tar.gz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 144 Package: MMDiff2 Version: 1.35.0 Depends: R (>= 3.5.0), Rsamtools, Biobase Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: 95b1132e7c1a771c13ac15a2e6bc1c79 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: devel git_last_commit: d7fad9d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MMDiff2_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MMDiff2_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MMDiff2_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MMDiff2_1.35.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 107 Package: MMUPHin Version: 1.21.0 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, stringr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: d8a1366dbace6878af8c8aef4dd61cac NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA SystemRequirements: glpk (>= 4.57) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: devel git_last_commit: 6b438ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-18 source.ver: src/contrib/MMUPHin_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MMUPHin_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MMUPHin_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MMUPHin_1.21.0.tgz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 138 Package: mnem Version: 1.23.0 Depends: R (>= 4.1) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071, ggplot2, wesanderson LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM, BiocStyle License: GPL-3 Archs: x64 MD5sum: e2246018c10efeaef8ce1c30a031c07b NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/mnem/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/mnem/issues git_url: https://git.bioconductor.org/packages/mnem git_branch: devel git_last_commit: f68e173 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mnem_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mnem_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mnem_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mnem_1.23.0.tgz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependsOnMe: nempi importsMe: bnem, dce, epiNEM dependencyCount: 83 Package: moanin Version: 1.15.0 Depends: R (>= 4.0), SummarizedExperiment, topGO, stats Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics, methods, grDevices, reshape2, NMI, zoo, ClusterR, splines, matrixStats Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, markdown, covr, BiocStyle License: BSD 3-clause License + file LICENSE Archs: x64 MD5sum: 9dd4b481d1521d2db2c558b427bf9a31 NeedsCompilation: no Title: An R Package for Time Course RNASeq Data Analysis Description: Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis. biocViews: TimeCourse, GeneExpression, RNASeq, Microarray, DifferentialExpression, Clustering Author: Elizabeth Purdom [aut] (ORCID: ), Nelle Varoquaux [aut, cre] (ORCID: ) Maintainer: Nelle Varoquaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/moanin git_branch: devel git_last_commit: 24c12c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/moanin_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/moanin_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/moanin_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/moanin_1.15.0.tgz vignettes: vignettes/moanin/inst/doc/documentation.html vignetteTitles: The Moanin Package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/moanin/inst/doc/documentation.R dependencyCount: 97 Package: mobileRNA Version: 1.3.0 Depends: R (>= 4.3.0) Imports: dplyr, tidyr, ggplot2, BiocGenerics, DESeq2, edgeR, ggrepel, grDevices, pheatmap, utils, tidyselect, progress, RColorBrewer, GenomicRanges, rtracklayer, data.table, SimDesign, scales, IRanges, stats, methods, Biostrings, reticulate, S4Vectors, GenomeInfoDb, SummarizedExperiment, rlang, bioseq, grid Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 907d073bcfa05a20e658fbc20b5c5124 NeedsCompilation: no Title: mobileRNA: Investigate the RNA mobilome & population-scale changes Description: Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time. biocViews: Visualization, RNASeq, Sequencing, SmallRNA, GenomeAssembly, Clustering, ExperimentalDesign, QualityControl, WorkflowStep, Alignment, Preprocessing Author: Katie Jeynes-Cupper [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Katie Jeynes-Cupper SystemRequirements: GNU make, ShortStack (>= 4.0), HTSeq, HISAT2, SAMtools, Conda VignetteBuilder: knitr BugReports: https://github.com/KJeynesCupper/mobileRNA/issues git_url: https://git.bioconductor.org/packages/mobileRNA git_branch: devel git_last_commit: d0f0d6c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mobileRNA_1.3.0.tar.gz vignettes: vignettes/mobileRNA/inst/doc/mobileRNA.html vignetteTitles: mobileRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mobileRNA/inst/doc/mobileRNA.R dependencyCount: 148 Package: MODA Version: 1.33.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 0dd0d8530b1692218fd70975f3c0f459 NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: devel git_last_commit: 1417781 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MODA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MODA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MODA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MODA_1.33.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 117 Package: ModCon Version: 1.15.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: efddabe88c0a481318b2e408fb9111d9 NeedsCompilation: no Title: Modifying splice site usage by changing the mRNP code, while maintaining the genetic code Description: Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] (ORCID: ) Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: devel git_last_commit: b573fd3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ModCon_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ModCon_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ModCon_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ModCon_1.15.0.tgz vignettes: vignettes/ModCon/inst/doc/ModCon.html vignetteTitles: Designing SD context with ModCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ModCon/inst/doc/ModCon.R dependencyCount: 5 Package: Modstrings Version: 1.23.0 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 3d33a97a2cffb1100e508c979acca450 NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: devel git_last_commit: 7af36f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Modstrings_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Modstrings_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Modstrings_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Modstrings_1.23.0.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3 dependencyCount: 35 Package: MOFA2 Version: 1.17.0 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown License: file LICENSE MD5sum: 1e79cc54a97aaa0f33e40ee21a84802b NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut, cre] (ORCID: ), Damien Arnol [aut] (ORCID: ), Danila Bredikhin [aut] (ORCID: ), Britta Velten [aut] (ORCID: ) Maintainer: Ricard Argelaguet URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 git_url: https://git.bioconductor.org/packages/MOFA2 git_branch: devel git_last_commit: 12bde39 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MOFA2_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOFA2_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOFA2_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOFA2_1.17.0.tgz vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html, vignettes/MOFA2/inst/doc/getting_started_R.html, vignettes/MOFA2/inst/doc/MEFISTO_temporal.html vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a model in R, MEFISTO on simulated data (temporal) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R, vignettes/MOFA2/inst/doc/getting_started_R.R, vignettes/MOFA2/inst/doc/MEFISTO_temporal.R dependencyCount: 92 Package: MOGAMUN Version: 1.17.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 5aef2862885955f563882c8b85c4578b NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] (ORCID: ) Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: devel git_last_commit: cb76f52 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MOGAMUN_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOGAMUN_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOGAMUN_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOGAMUN_1.17.0.tgz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 68 Package: mogsa Version: 1.41.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 Archs: x64 MD5sum: 5511debeb0e35d73f3eb9b0aba832576 NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: devel git_last_commit: b1cb18d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mogsa_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mogsa_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mogsa_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mogsa_1.41.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 72 Package: MoleculeExperiment Version: 1.7.0 Depends: R (>= 2.10) Imports: SpatialExperiment, Matrix, purrr, data.table, dplyr (>= 1.1.1), magrittr, rjson, utils, methods, terra, ggplot2, rlang, cli, EBImage, rhdf5, BiocParallel, S4Vectors, stats Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 7172a79d20705d6a44b8a527b7f48b4c NeedsCompilation: no Title: Prioritising a molecule-level storage of Spatial Transcriptomics Data Description: MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors. biocViews: DataImport, DataRepresentation, Infrastructure, Software, Spatial, Transcriptomics Author: Bárbara Zita Peters Couto [aut], Nicholas Robertson [aut], Ellis Patrick [aut], Shila Ghazanfar [aut, cre] Maintainer: Shila Ghazanfar URL: https://github.com/SydneyBioX/MoleculeExperiment VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/MoleculeExperiment/issues git_url: https://git.bioconductor.org/packages/MoleculeExperiment git_branch: devel git_last_commit: d50404d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MoleculeExperiment_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MoleculeExperiment_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MoleculeExperiment_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MoleculeExperiment_1.7.0.tgz vignettes: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html vignetteTitles: "Introduction to MoleculeExperiment" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.R dependencyCount: 126 Package: MOMA Version: 1.19.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: 6ecda9e59b439a8d406630c820c53ef8 NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: devel git_last_commit: cd609f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MOMA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOMA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOMA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOMA_1.19.0.tgz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 105 Package: monaLisa Version: 1.13.0 Depends: R (>= 4.1) Imports: methods, stats, utils, grDevices, graphics, BiocGenerics, GenomicRanges, TFBSTools, Biostrings, IRanges, stabs, BSgenome, glmnet, S4Vectors, SummarizedExperiment, BiocParallel, grid, circlize, ComplexHeatmap (>= 2.11.1), XVector, GenomeInfoDb, tools, vioplot, RSQLite Suggests: JASPAR2020, JASPAR2024, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, testthat, BiocStyle, gridExtra License: GPL (>= 3) MD5sum: 66b2b0ce14185fcf4455c0a4dad74c7c NeedsCompilation: no Title: Binned Motif Enrichment Analysis and Visualization Description: Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided. biocViews: MotifAnnotation, Visualization, FeatureExtraction, Epigenetics Author: Dania Machlab [aut] (ORCID: ), Lukas Burger [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dany Mukesha [ctb] (ORCID: ), Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/monaLisa, https://bioconductor.org/packages/monaLisa/, https://fmicompbio.github.io/monaLisa/ VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/monaLisa/issues git_url: https://git.bioconductor.org/packages/monaLisa git_branch: devel git_last_commit: a32a937 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/monaLisa_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/monaLisa_1.13.0.zip vignettes: vignettes/monaLisa/inst/doc/monaLisa.html, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html vignetteTitles: monaLisa - MOtif aNAlysis with Lisa, selecting_motifs_with_randLassoStabSel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R dependencyCount: 145 Package: monocle Version: 2.35.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>= 0.1.9), limma, tibble, dplyr, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 MD5sum: c086fffdc67fac83696c419f2c65b470 NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: devel git_last_commit: f50c2db git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/monocle_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/monocle_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/monocle_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/monocle_2.35.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero importsMe: uSORT suggestsMe: sincell, grandR, Seurat dependencyCount: 78 Package: Moonlight2R Version: 1.5.2 Depends: R (>= 4.4), doParallel, foreach Imports: parmigene, randomForest, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, grDevices, graphics, GEOquery, stats, purrr, RISmed, grid, utils, ComplexHeatmap, GenomicRanges, dplyr, fuzzyjoin, rtracklayer, magrittr, qpdf, readr, seqminer, stringr, tibble, tidyHeatmap, tidyr, AnnotationHub, easyPubMed, org.Hs.eg.db, EpiMix, BiocGenerics, ggplot2, ExperimentHub, rlang, withr, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), devtools, roxygen2, png License: GPL-3 MD5sum: f26ab07ff6e32c7cc013e0e74ce1e641 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut], Katrine Meldgård [aut], Anna Melidi [aut], Xi Steven Chen [aut], Antonio Colaprico [aut], Catharina Olsen [aut], Matteo Tiberti [cre, aut], Elena Papaleo [aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/Moonlight2R SystemRequirements: CScapeSomatic VignetteBuilder: knitr BugReports: https://github.com/ELELAB/Moonlight2R/issues git_url: https://git.bioconductor.org/packages/Moonlight2R git_branch: devel git_last_commit: c6d5495 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/Moonlight2R_1.5.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/Moonlight2R_1.5.2.zip vignettes: vignettes/Moonlight2R/inst/doc/Moonlight2R.html vignetteTitles: A workflow to study mechanistic indicators for driver gene prediction with Moonlight hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Moonlight2R/inst/doc/Moonlight2R.R dependencyCount: 224 Package: MoonlightR Version: 1.33.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png, edgeR License: GPL (>= 3) Archs: x64 MD5sum: f241d49777f3c3056102beff18f3edc7 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ELELAB/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: devel git_last_commit: e51fead git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MoonlightR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MoonlightR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MoonlightR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MoonlightR_1.33.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 189 Package: mosaics Version: 2.45.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) MD5sum: dd506943324f7d75456f812653505e15 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: devel git_last_commit: 43771c2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mosaics_2.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mosaics_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mosaics_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mosaics_2.45.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 55 Package: mosbi Version: 1.13.0 Depends: R (>= 4.1) Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust, isa2, QUBIC, akmbiclust, RColorBrewer LinkingTo: Rcpp, BH, RcppParallel Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 58b3ebff2c6f295d02bc4345a37c893a NeedsCompilation: yes Title: Molecular Signature identification using Biclustering Description: This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms. biocViews: Software, StatisticalMethod, Clustering, Network Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut], Nikolai Koehler [aut] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: devel git_last_commit: ba952ac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mosbi_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mosbi_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mosbi_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mosbi_1.13.0.tgz vignettes: vignettes/mosbi/inst/doc/example-workflow.html, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.html vignetteTitles: example-workflow, similarity-metrics-evaluation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosbi/inst/doc/example-workflow.R, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.R dependencyCount: 63 Package: MOSClip Version: 1.1.0 Depends: R (>= 4.4.0) Imports: MultiAssayExperiment, methods, survminer, graph, graphite, AnnotationDbi, checkmate, ggplot2, gridExtra, igraph, pheatmap, survival, RColorBrewer, SuperExactTest, reshape, NbClust, S4Vectors, grDevices, graphics, stats, utils, ComplexHeatmap, FactoMineR, circlize, corpcor, coxrobust, elasticnet, gRbase, ggplotify, qpgraph, org.Hs.eg.db, Matrix Suggests: RUnit, BiocGenerics, MASS, BiocStyle, knitr, EDASeq, rmarkdown, kableExtra, testthat (>= 3.0.0) License: AGPL-3 MD5sum: 1fbf147a8d3216b22125600ec23fe926 NeedsCompilation: no Title: Multi Omics Survival Clip Description: Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself. biocViews: Software, StatisticalMethod, GraphAndNetwork, Survival, Regression, DimensionReduction, Pathways, Reactome Author: Paolo Martini [aut, cre] (ORCID: ), Anna Bortolato [aut] (ORCID: ), Anna Tanada [aut] (ORCID: ), Enrica Calura [aut] (ORCID: ), Stefania Pirrotta [aut] (ORCID: ), Federico Agostinis [aut] Maintainer: Paolo Martini URL: https://github.com/CaluraLab/MOSClip/ VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/MOSClip/issues git_url: https://git.bioconductor.org/packages/MOSClip git_branch: devel git_last_commit: 412bda8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MOSClip_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOSClip_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOSClip_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MOSClip_1.1.0.tgz vignettes: vignettes/MOSClip/inst/doc/mosclip_vignette.html vignetteTitles: MOSClip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSClip/inst/doc/mosclip_vignette.R dependencyCount: 216 Package: mosdef Version: 1.3.1 Depends: R (>= 4.4.0) Imports: DT, ggplot2, ggforce, ggrepel, graphics, grDevices, htmltools, methods, AnnotationDbi, topGO, GO.db, clusterProfiler, goseq, utils, RColorBrewer, rlang, DESeq2, scales, SummarizedExperiment, S4Vectors, stats Suggests: knitr, rmarkdown, macrophage, org.Hs.eg.db, GeneTonic, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg38.knownGene, BiocStyle License: MIT + file LICENSE MD5sum: 9598b0e72678a992178c8fa58e91c3a4 NeedsCompilation: no Title: MOSt frequently used and useful Differential Expression Functions Description: This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. biocViews: GeneExpression, Software, Transcription, Transcriptomics, DifferentialExpression, Visualization, ReportWriting, GeneSetEnrichment, GO Author: Leon Dammer [aut] (ORCID: ), Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/imbeimainz/mosdef VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/mosdef/issues git_url: https://git.bioconductor.org/packages/mosdef git_branch: devel git_last_commit: de8bb7a git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/mosdef_1.3.1.tar.gz vignettes: vignettes/mosdef/inst/doc/mosdef_userguide.html vignetteTitles: The mosdef User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosdef/inst/doc/mosdef_userguide.R importsMe: GeneTonic, ideal, pcaExplorer dependencyCount: 182 Package: MOSim Version: 2.3.0 Depends: R (>= 4.2.0) Imports: HiddenMarkov, zoo, IRanges, S4Vectors, dplyr, ggplot2, lazyeval, matrixStats, methods, rlang, stringi, stringr, scran, Seurat, Signac, edgeR, Rcpp LinkingTo: cpp11, Rcpp Suggests: testthat, knitr, rmarkdown, codetools, BiocStyle, stats, utils, purrr, scales, tibble, tidyr, Biobase, scater, SingleCellExperiment, decor, markdown, Rsamtools, igraph, leiden, bluster License: GPL-3 MD5sum: 2f58b2bf23b7a3bfe11716c6b3944ad6 NeedsCompilation: yes Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carolina Monzó [aut], Carlos Martínez [aut], Sonia Tarazona [cre, aut] Maintainer: Sonia Tarazona URL: https://github.com/ConesaLab/MOSim VignetteBuilder: knitr BugReports: https://github.com/ConesaLab/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: devel git_last_commit: 8e78c32 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MOSim_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MOSim_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MOSim_2.3.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.html, vignettes/MOSim/inst/doc/scMOSim.html vignetteTitles: %\VignetteEngine{knitr::knitr}Wiki of how to use mosim, Wiki of how to use sc_mosim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R, vignettes/MOSim/inst/doc/scMOSim.R dependencyCount: 199 Package: Motif2Site Version: 1.11.0 Depends: R (>= 4.1) Imports: S4Vectors, stats, utils, methods, grDevices, graphics, BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges, GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805 License: GPL-2 MD5sum: 2b9d46dad244176b365c4d8d79f31594 NeedsCompilation: no Title: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions Description: Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves. biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling, Epigenetics, SequenceMatching Author: Peyman Zarrineh [cre, aut] (ORCID: ) Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/ManchesterBioinference/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: devel git_last_commit: 9d1afd0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Motif2Site_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Motif2Site_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Motif2Site_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Motif2Site_1.11.0.tgz vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html vignetteTitles: Motif2Site hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R dependencyCount: 126 Package: motifbreakR Version: 2.21.0 Depends: R (>= 4.4.0), grid, MotifDb Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment, pwalign, DT, bsicons, BiocFileCache, biomaRt, bslib, shiny, vroom Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hg38 License: GPL-2 MD5sum: 9fdf33eaa1dd098155fa573a39abcc82 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 32 species, a total of 109 versions). biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription Author: Simon Gert Coetzee [aut, cre] (ORCID: ), Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: devel git_last_commit: 81a16d2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/motifbreakR_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifbreakR_2.21.0.zip vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 195 Package: motifcounter Version: 1.31.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 MD5sum: 0d381bd7135b436646920100113f46f0 NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: devel git_last_commit: 08f5123 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/motifcounter_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifcounter_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifcounter_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifcounter_1.31.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 26 Package: MotifDb Version: 1.49.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR, markdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: e480594498cb1bcae2cd9e57f4427eef NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: devel git_last_commit: c4cd303 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MotifDb_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MotifDb_1.49.0.zip vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, generegulation importsMe: rTRMui suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, igvR, memes, MMDiff2, motifcounter, motifStack, motifTestR, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 61 Package: motifmatchr Version: 1.29.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE MD5sum: df20eee2c1360463a4fa803ba4a16a69 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: devel git_last_commit: 0c15309 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/motifmatchr_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifmatchr_1.29.0.zip vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: ATACseqTFEA, enhancerHomologSearch, epiregulon, esATAC, pageRank, spatzie suggestsMe: chromVAR, GRaNIE, MethReg, CAGEWorkflow, MOCHA, Signac dependencyCount: 128 Package: MotifPeeker Version: 0.99.13 Depends: R (>= 4.4.0) Imports: BiocFileCache, BiocParallel, DT, ggplot2, plotly, universalmotif, GenomicRanges, IRanges, rtracklayer, tools, htmltools, rmarkdown, viridis, SummarizedExperiment, htmlwidgets, Rsamtools, GenomicAlignments, GenomeInfoDb, Biostrings, BSgenome, memes, S4Vectors, dplyr, purrr, tidyr, heatmaply, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, downloadthis, knitr, markdown, methods, remotes, rworkflows, testthat (>= 3.0.0), withr, emoji, curl, jsonlite License: GPL (>= 3) MD5sum: b9d03f93a4050c340a95b945c0628ce2 NeedsCompilation: no Title: Benchmarking Epigenomic Profiling Methods Using Motif Enrichment Description: MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, MotifDiscovery, SequenceMatching, Software, Alignment Author: Hiranyamaya Dash [cre, aut] (ORCID: ), Thomas Roberts [aut] (ORCID: ), Maria Weinert [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/MotifPeeker SystemRequirements: MEME Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MotifPeeker/issues git_url: https://git.bioconductor.org/packages/MotifPeeker git_branch: devel git_last_commit: 45eb2ac git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/MotifPeeker_0.99.13.tar.gz vignettes: vignettes/MotifPeeker/inst/doc/MotifPeeker.html, vignettes/MotifPeeker/inst/doc/troubleshooting.html vignetteTitles: MotifPeeker, troubleshooting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MotifPeeker/inst/doc/MotifPeeker.R, vignettes/MotifPeeker/inst/doc/troubleshooting.R dependencyCount: 189 Package: motifStack Version: 1.51.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML, TFBSTools Suggests: Cairo, grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020 License: GPL (>= 2) MD5sum: 669ed040f9178c17ba06411cf70c622e NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: devel git_last_commit: 2974913 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/motifStack_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifStack_1.51.0.zip vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, motifbreakR, ribosomeProfilingQC, TCGAWorkflow suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif dependencyCount: 147 Package: motifTestR Version: 1.3.1 Depends: Biostrings, GenomicRanges, ggplot2 (>= 3.5.0), R (>= 4.3.0), Imports: GenomeInfoDb, graphics, harmonicmeanp, IRanges, matrixStats, methods, parallel, patchwork, rlang, S4Vectors, stats, universalmotif Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, extraChIPs, ggdendro, knitr, MotifDb, rmarkdown, rtracklayer, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 159d32b8823e3f122d9aeed982053e6d NeedsCompilation: no Title: Perform key tests for binding motifs in sequence data Description: Taking a set of sequence motifs as PWMs, test a set of sequences for over-representation of these motifs, as well as any positional features within the set of motifs. Enrichment analysis can be undertaken using multiple statistical approaches. The package also contains core functions to prepare data for analysis, and to visualise results. biocViews: MotifAnnotation, ChIPSeq, ChipOnChip, SequenceMatching, Software Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/motifTestR VignetteBuilder: knitr BugReports: https://github.com/smped/motifTestR/issues git_url: https://git.bioconductor.org/packages/motifTestR git_branch: devel git_last_commit: 8bd1db5 git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-01 source.ver: src/contrib/motifTestR_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/motifTestR_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/motifTestR_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/motifTestR_1.3.1.tgz vignettes: vignettes/motifTestR/inst/doc/motifAnalysis.html vignetteTitles: Motif Analysis Using motifTestR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifTestR/inst/doc/motifAnalysis.R dependencyCount: 66 Package: MouseFM Version: 1.17.0 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 6837b92bc761521efaa75c2d2a75372f NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (ORCID: ), Inken Wohlers [aut] (ORCID: ), Hauke Busch [aut] (ORCID: ) Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: devel git_last_commit: 16b409c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/MouseFM_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MouseFM_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MouseFM_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MouseFM_1.17.0.tgz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 95 Package: MPAC Version: 1.1.0 Depends: R (>= 4.4.0) Imports: data.table (>= 1.14.2), SummarizedExperiment (>= 1.30.2), BiocParallel (>= 1.28.3), fitdistrplus (>= 1.1), igraph (>= 1.4.3), BiocSingular (>= 1.10.0), S4Vectors (>= 0.32.3), SingleCellExperiment (>= 1.16.0), bluster (>= 1.4.0), fgsea (>= 1.20.0), scran (>= 1.22.1), ComplexHeatmap (>= 2.16.0), grid, stats Suggests: rmarkdown, knitr, svglite, bookdown(>= 0.34), testthat (>= 3.0.0) License: GPL-3 MD5sum: 223ff26999fa1ba659ad11d2831fce42 NeedsCompilation: no Title: Multi-omic Pathway Analysis of Cancer Description: Multi-omic Pathway Analysis of Cancer (MPAC), integrates multi-omic data for understanding cancer mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization. biocViews: Software, Technology, Sequencing, RNASeq, Survival, Clustering, ImmunoOncology Author: Peng Liu [aut, cre] (ORCID: ), Paul Ahlquist [aut], Irene Ong [aut], Anthony Gitter [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/MPAC VignetteBuilder: knitr BugReports: https://github.com/pliu55/MPAC/issues git_url: https://git.bioconductor.org/packages/MPAC git_branch: devel git_last_commit: ac72f36 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MPAC_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MPAC_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPAC_1.1.0.tgz vignettes: vignettes/MPAC/inst/doc/MPAC.html vignetteTitles: MPAC: Multi-omic Pathway Analysis of Cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPAC/inst/doc/MPAC.R dependencyCount: 112 Package: MPFE Version: 1.43.0 License: GPL (>= 3) Archs: x64 MD5sum: fa116110e11e331c446f82b8aba12381 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: devel git_last_commit: 939ad78 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MPFE_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MPFE_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPFE_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MPFE_1.43.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.29.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 51cfa1dd391f8882b8f32fdd0b60b054 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: devel git_last_commit: 033bb6a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mpra_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mpra_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mpra_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mpra_1.29.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 50 Package: MPRAnalyze Version: 1.25.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: 8b4a43db43ae08b83a24c4c6c460701f NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression, ExperimentalDesign, Classification Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer [ctb], Fabian J Theis [ctb], Nir Yosef [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: devel git_last_commit: a9ca154 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MPRAnalyze_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MPRAnalyze_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MPRAnalyze_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MPRAnalyze_1.25.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 56 Package: msa Version: 1.39.0 Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn, pwalign License: GPL (>= 2) Archs: x64 MD5sum: bb2e03698c501ce0adde620dd0b94574 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta [aut], Christoph Kainrath [aut], Ulrich Bodenhofer [aut,cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/msa SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: devel git_last_commit: 76c1c29 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msa_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msa_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msa_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msa_1.39.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq, surfaltr, SeedMatchR suggestsMe: idpr, bio3d dependencyCount: 27 Package: MSA2dist Version: 1.11.1 Depends: R (>= 4.4.0) Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel, dplyr, foreach, methods, parallel, pwalign, rlang, seqinr, stats, stringi, stringr, tibble, tidyr, utils LinkingTo: Rcpp, RcppThread Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle License: GPL-3 + file LICENSE MD5sum: f42df85858d4c2d3a5fda4b48bdf21c2 NeedsCompilation: yes Title: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis Description: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] (ORCID: ) Maintainer: Kristian K Ullrich URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist, https://mpievolbio-it.pages.gwdg.de/MSA2dist/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues git_url: https://git.bioconductor.org/packages/MSA2dist git_branch: devel git_last_commit: b5ab038 git_last_commit_date: 2024-11-14 Date/Publication: 2024-11-18 source.ver: src/contrib/MSA2dist_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSA2dist_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSA2dist_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSA2dist_1.11.1.tgz vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html vignetteTitles: MSA2dist Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R importsMe: doubletrouble dependencyCount: 66 Package: MsBackendMassbank Version: 1.15.0 Depends: R (>= 4.0), Spectra (>= 1.15.10) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics (>= 1.35.3), MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 MD5sum: 5ec3e25d2dee236b13109a5136555b7a NeedsCompilation: no Title: Mass Spectrometry Data Backend for MassBank record Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMassbank VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMassbank/issues git_url: https://git.bioconductor.org/packages/MsBackendMassbank git_branch: devel git_last_commit: 98147f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsBackendMassbank_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMassbank_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMassbank_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMassbank_1.15.0.tgz vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html vignetteTitles: Description and usage of MsBackendMassbank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R dependencyCount: 31 Package: MsBackendMetaboLights Version: 1.1.0 Depends: R (>= 4.2.0), Spectra (>= 1.15.12) Imports: curl, ProtGenerics, BiocFileCache, S4Vectors, methods Suggests: testthat, rmarkdown, mzR, knitr, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: bd1c9dc61bc5048c2c7cd9c7048a04ea NeedsCompilation: no Title: Retrieve Mass Spectrometry Data from MetaboLights Description: MetaboLights is one of the main public repositories for storage of metabolomics experiments, which includes analysis results as well as raw data. The MsBackendMetaboLights package provides functionality to retrieve and represent mass spectrometry (MS) data from MetaboLights. Data files are downloaded and cached locally avoiding repetitive downloads. MS data from metabolomics experiments can thus be directly and seamlessly integrated into R-based analysis workflows with the Spectra and MsBackendMetaboLights package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Philippine Louail [aut] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMetaboLights VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMetaboLights/issues git_url: https://git.bioconductor.org/packages/MsBackendMetaboLights git_branch: devel git_last_commit: 36596b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsBackendMetaboLights_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMetaboLights_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMetaboLights_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMetaboLights_1.1.0.tgz vignettes: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.html vignetteTitles: Retrieve and Use Mass Spectrometry Data from MetaboLights hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.R dependencyCount: 68 Package: MsBackendMgf Version: 1.15.1 Depends: R (>= 4.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: c3c4e49ab53acb840a11f172e0770fe7 NeedsCompilation: no Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf) Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Witting [ctb] (ORCID: ), Adriano Rutz [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMgf VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues git_url: https://git.bioconductor.org/packages/MsBackendMgf git_branch: devel git_last_commit: 40c14e0 git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-20 source.ver: src/contrib/MsBackendMgf_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMgf_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMgf_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMgf_1.15.1.tgz vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html vignetteTitles: Description and usage of MsBackendMgf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R suggestsMe: CompoundDb, MsBackendRawFileReader, xcms dependencyCount: 30 Package: MsBackendMsp Version: 1.11.0 Depends: R (>= 4.1.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 2742a5e244c10f432d24abc5851857e1 NeedsCompilation: no Title: Mass Spectrometry Data Backend for NIST msp Files Description: Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: Neumann Steffen [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMsp VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues git_url: https://git.bioconductor.org/packages/MsBackendMsp git_branch: devel git_last_commit: 2de3721 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsBackendMsp_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendMsp_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMsp_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMsp_1.11.0.tgz vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html vignetteTitles: MsBackendMsp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R dependencyCount: 30 Package: MsBackendRawFileReader Version: 1.13.1 Depends: R (>= 4.1), methods, Spectra (>= 1.15.10) Imports: ProtGenerics (>= 1.35.3), MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.13.1), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 1b5c402dbf65a6265481b2a05757092b NeedsCompilation: yes Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files Description: implements a MsBackend for the Spectra package using Thermo Fisher Scientific's NewRawFileReader .Net libraries. The package is generalizing the functionality introduced by the rawrr package Methods defined in this package are supposed to extend the Spectra Bioconductor package. biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (ORCID: ), Tobias Kockmann [aut] (ORCID: ), Roger Gine Bertomeu [ctb] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/MsBackendRawFileReader SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader git_branch: devel git_last_commit: d2c3762 git_last_commit_date: 2024-11-07 Date/Publication: 2024-11-07 source.ver: src/contrib/MsBackendRawFileReader_1.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendRawFileReader_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendRawFileReader_1.13.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendRawFileReader_1.13.1.tgz vignettes: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html vignetteTitles: On Using and Extending the `MsBackendRawFileReader` Backend. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R dependencyCount: 31 Package: MsBackendSql Version: 1.7.1 Depends: R (>= 4.2.0), Spectra (>= 1.15.10) Imports: BiocParallel, S4Vectors, methods, ProtGenerics (>= 1.35.3), DBI, MsCoreUtils, IRanges, data.table, progress, stringi, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, msdata, rmarkdown, microbenchmark, mzR License: Artistic-2.0 MD5sum: c0baf4f741f07856c4988f82040df6ef NeedsCompilation: no Title: SQL-based Mass Spectrometry Data Backend Description: SQL-based mass spectrometry (MS) data backend supporting also storange and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Chong Tang [ctb], Laurent Gatto [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendSql VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendSql/issues git_url: https://git.bioconductor.org/packages/MsBackendSql git_branch: devel git_last_commit: 1e59add git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-20 source.ver: src/contrib/MsBackendSql_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsBackendSql_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendSql_1.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsBackendSql_1.7.1.tgz vignettes: vignettes/MsBackendSql/inst/doc/MsBackendSql.html vignetteTitles: Storing Mass Spectrometry Data in SQL Databases hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendSql/inst/doc/MsBackendSql.R suggestsMe: MsExperiment dependencyCount: 44 Package: MsCoreUtils Version: 1.19.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, Matrix, preprocessCore, missForest Enhances: HDF5Array License: Artistic-2.0 MD5sum: 5ca22cfd359ea7d765e28f45d184058f NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (ORCID: ), Michael Witting [ctb] (ORCID: ), Samuel Wieczorek [ctb], Roger Gine Bertomeu [ctb] (ORCID: ), Mar Garcia-Aloy [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: devel git_last_commit: 6a5c06d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsCoreUtils_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsCoreUtils_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsCoreUtils_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsCoreUtils_1.19.0.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: CompoundDb, hdxmsqc, MetaboAnnotation, MetaboCoreUtils, MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnbase, PSMatch, QFeatures, qmtools, scp, Spectra, SpectraQL, xcms suggestsMe: MetNet, msqrob2 dependencyCount: 13 Package: MsDataHub Version: 1.7.0 Imports: ExperimentHub, utils Suggests: ExperimentHubData, DT, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), Spectra, mzR, PSMatch, QFeatures (>= 1.13.3) License: Artistic-2.0 MD5sum: 71c3c78d324145a83a831e90b58a0369 NeedsCompilation: no Title: Mass Spectrometry Data on ExperimentHub Description: The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments. biocViews: ExperimentHubSoftware, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Kristina Gomoryova [ctb] (ORCID: ), Johannes Rainer [aut] (ORCID: ) Maintainer: Laurent Gatto URL: https://rformassspectrometry.github.io/MsDataHub VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsDataHub/issues git_url: https://git.bioconductor.org/packages/MsDataHub git_branch: devel git_last_commit: 1c89558 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsDataHub_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsDataHub_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsDataHub_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsDataHub_1.7.0.tgz vignettes: vignettes/MsDataHub/inst/doc/MsDataHub.html vignetteTitles: Mass Spectrometry Data on ExperimentHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsDataHub/inst/doc/MsDataHub.R suggestsMe: QFeatures, scp dependencyCount: 67 Package: MsExperiment Version: 1.9.0 Depends: R (>= 4.2), ProtGenerics (>= 1.35.2), Imports: methods, S4Vectors, IRanges, Spectra, SummarizedExperiment, QFeatures, DBI, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown, rpx, mzR, msdata, MsBackendSql (>= 1.3.2), RSQLite License: Artistic-2.0 MD5sum: 4cab680c929a4d0020f8dd703e6eeada NeedsCompilation: no Title: Infrastructure for Mass Spectrometry Experiments Description: Infrastructure to store and manage all aspects related to a complete proteomics or metabolomics mass spectrometry (MS) experiment. The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, ExperimentalDesign, DataImport Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/MsExperiment VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsExperiment/issues git_url: https://git.bioconductor.org/packages/MsExperiment git_branch: devel git_last_commit: 75834c3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsExperiment_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsExperiment_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsExperiment_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsExperiment_1.9.0.tgz vignettes: vignettes/MsExperiment/inst/doc/MsExperiment.html vignetteTitles: Managing Mass Spectrometry Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsExperiment/inst/doc/MsExperiment.R importsMe: MsQuality, squallms, xcms dependencyCount: 120 Package: MsFeatures Version: 1.15.0 Depends: R (>= 4.1) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: 3607e6d9b7f1694f876e59f09fafa109 NeedsCompilation: no Title: Functionality for Mass Spectrometry Features Description: The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. biocViews: Infrastructure, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues git_url: https://git.bioconductor.org/packages/MsFeatures git_branch: devel git_last_commit: 2fb27a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsFeatures_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsFeatures_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsFeatures_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsFeatures_1.15.0.tgz vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html vignetteTitles: Grouping Mass Spectrometry Features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R importsMe: xcms suggestsMe: qmtools dependencyCount: 43 Package: msgbsR Version: 1.31.0 Depends: R (>= 3.5.0), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 MD5sum: a7967b5a29d89101b3721b884007a2bf NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: devel git_last_commit: c7b70de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msgbsR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msgbsR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msgbsR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msgbsR_1.31.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 182 Package: msImpute Version: 1.17.0 Depends: R (>= 3.5.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr, dplyr Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) Archs: x64 MD5sum: b4763d49a01f3f0bc622212b8497ca70 NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP). biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] (ORCID: ) Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: devel git_last_commit: 1dd79da git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msImpute_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msImpute_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msImpute_1.17.0.tgz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 97 Package: mslp Version: 1.9.0 Depends: R (>= 4.2.0) Imports: data.table (>= 1.13.0), doRNG, fmsb, foreach, magrittr, org.Hs.eg.db, pROC, randomForest, RankProd, stats, utils Suggests: BiocStyle, doFuture, future, knitr, rmarkdown, roxygen2, tinytest License: GPL-3 Archs: x64 MD5sum: 9a8e2d83d966760d465e1fb900f68b2f NeedsCompilation: no Title: Predict synthetic lethal partners of tumour mutations Description: An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed. biocViews: Pharmacogenetics, Pharmacogenomics Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mslp git_branch: devel git_last_commit: ee73d2b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mslp_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mslp_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mslp_1.9.0.tgz vignettes: vignettes/mslp/inst/doc/mslp.html vignetteTitles: mslp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mslp/inst/doc/mslp.R dependencyCount: 64 Package: msmsEDA Version: 1.45.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 669f1d42b72544607956faf5d98266c7 NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: devel git_last_commit: 42fe18d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msmsEDA_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msmsEDA_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msmsEDA_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msmsEDA_1.45.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman, RforProteomics dependencyCount: 142 Package: msmsTests Version: 1.45.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue Suggests: xtable License: GPL-2 MD5sum: 4d417dc06f3f9985677f8843f5d5cd5a NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: devel git_last_commit: 0615a12 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msmsTests_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msmsTests_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msmsTests_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msmsTests_1.45.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R importsMe: MSnID suggestsMe: RforProteomics dependencyCount: 146 Package: MSnbase Version: 2.33.2 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.29.1) Imports: MsCoreUtils, PSMatch, BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.43.3), magick, msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, XML, shiny, magrittr, SummarizedExperiment, Spectra License: Artistic-2.0 MD5sum: ecf2045311a83a3cf0795c35eb20e6b8 NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker, Lieven Clement and Pascal Maas. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: devel git_last_commit: 7fad86f7 git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-12 source.ver: src/contrib/MSnbase_2.33.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSnbase_2.33.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSnbase_2.33.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSnbase_2.33.2.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: bandle, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, synapter, DAPARdata, pRolocdata, RforProteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, omXplore, peakPantheR, PrInCE, PRONE, ptairMS, RMassBank, squallms, topdownr, xcms, qPLEXdata suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, LCMSQA, pmd, RAMClustR dependencyCount: 136 Package: MSnID Version: 1.41.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 MD5sum: ce3e90b4b981d7e679ae742a19e6de81 NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: devel git_last_commit: dc343c5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSnID_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSnID_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSnID_1.41.0.tgz vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf, vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: Handling Modifications with MSnID, MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/handling_mods.R, vignettes/MSnID/inst/doc/msnid_vignette.R suggestsMe: RforProteomics dependencyCount: 170 Package: mspms Version: 0.99.7 Depends: R (>= 4.4.0) Imports: QFeatures, SummarizedExperiment, magrittr, rlang, dplyr, purrr, stats, tidyr, stringr, ggplot2, ggseqlogo, heatmaply, readr, rstatix, tibble, ggpubr Suggests: knitr, testthat (>= 3.0.0), downloadthis, DT, rmarkdown, BiocStyle, imputeLCMD License: MIT + file LICENSE MD5sum: 7746d22ee2df667053eac599cd567a7f NeedsCompilation: no Title: Tools for the analysis of MSP-MS data Description: This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided. biocViews: Proteomics, MassSpectrometry, Preprocessing Author: Charlie Bayne [aut, cre] (ORCID: ) Maintainer: Charlie Bayne URL: https://github.com/baynec2/mspms VignetteBuilder: knitr BugReports: https://github.com/baynec2/mspms/issues git_url: https://git.bioconductor.org/packages/mspms git_branch: devel git_last_commit: 9653326 git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-03 source.ver: src/contrib/mspms_0.99.7.tar.gz win.binary.ver: bin/windows/contrib/4.5/mspms_0.99.7.zip vignettes: vignettes/mspms/inst/doc/mspms_vignette.html vignetteTitles: mspms_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mspms/inst/doc/mspms_vignette.R dependencyCount: 170 Package: MSPrep Version: 1.17.0 Depends: R (>= 4.1.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), crmn, preprocessCore, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, missForest, sva, VIM, Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 MD5sum: aee1b9f3b7822c6a1815a634342214eb NeedsCompilation: no Title: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data Description: Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. biocViews: Metabolomics, MassSpectrometry, Preprocessing Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes [aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut], Katerina Kechris [aut, cph, ths] Maintainer: Max McGrath URL: https://github.com/KechrisLab/MSPrep VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MSPrep/issues git_url: https://git.bioconductor.org/packages/MSPrep git_branch: devel git_last_commit: 5202f3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSPrep_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSPrep_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSPrep_1.17.0.tgz vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html vignetteTitles: Using MSPrep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R dependencyCount: 148 Package: msPurity Version: 1.33.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE Archs: x64 MD5sum: b8cb1ac24bcd199e0a5c453a6bc71c02 NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (ORCID: ), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: devel git_last_commit: 6c0644f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msPurity_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msPurity_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msPurity_1.33.0.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R suggestsMe: MetMashR dependencyCount: 70 Package: msqrob2 Version: 1.15.0 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, MultiAssayExperiment, codetools Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata, MSnbase, matrixStats, MsCoreUtils, covr License: Artistic-2.0 MD5sum: aa7e1468ef6b249d177b29f02ac9f18c NeedsCompilation: no Title: Robust statistical inference for quantitative LC-MS proteomics Description: msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, MultipleComparison, Regression, ExperimentalDesign, Software, ImmunoOncology, Normalization, TimeCourse, Preprocessing Author: Lieven Clement [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Oliver M. Crook [aut] (ORCID: ), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (ORCID: ), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement URL: https://github.com/statOmics/msqrob2 VignetteBuilder: knitr BugReports: https://github.com/statOmics/msqrob2/issues git_url: https://git.bioconductor.org/packages/msqrob2 git_branch: devel git_last_commit: 1d90849 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/msqrob2_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/msqrob2_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/msqrob2_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/msqrob2_1.15.0.tgz vignettes: vignettes/msqrob2/inst/doc/cptac.html vignetteTitles: A. label-free workflow with two group design hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msqrob2/inst/doc/cptac.R dependencyCount: 124 Package: MsQuality Version: 1.7.0 Depends: R (>= 4.2.0) Imports: BiocParallel (>= 1.32.0), ggplot2 (>= 3.3.5), htmlwidgets (>= 1.5.3), methods (>= 4.2.0), msdata (>= 0.32.0), MsExperiment (>= 0.99.0), plotly (>= 4.9.4.1), ProtGenerics (>= 1.24.0), rlang (>= 1.1.1), rmzqc (>= 0.5.0), shiny (>= 1.6.0), shinydashboard (>= 0.7.1), Spectra (>= 1.13.2), stats (>= 4.2.0), stringr (>= 1.4.0), tibble (>= 3.1.4), tidyr (>= 1.1.3), utils (>= 4.2.0) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), dplyr (>= 1.0.5), knitr (>= 1.11), mzR (>= 2.32.0), rmarkdown (>= 2.7), S4Vectors (>= 0.29.17), testthat (>= 2.2.1) License: GPL-3 MD5sum: 753bef765dd70f9c2cf1a5a24a5ff810 NeedsCompilation: no Title: MsQuality - Quality metric calculation from Spectra and MsExperiment objects Description: The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. biocViews: Metabolomics, Proteomics, MassSpectrometry, QualityControl Author: Thomas Naake [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ) Maintainer: Thomas Naake URL: https://www.github.com/tnaake/MsQuality/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MsQuality git_branch: devel git_last_commit: c5505d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MsQuality_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MsQuality_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MsQuality_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MsQuality_1.7.0.tgz vignettes: vignettes/MsQuality/inst/doc/MsQuality.html vignetteTitles: QC for metabolomics and proteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsQuality/inst/doc/MsQuality.R dependencyCount: 146 Package: MSstats Version: 4.15.0 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, htmltools, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, plotly, marray, stats, grDevices, graphics, methods, statmod, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown, mockery License: Artistic-2.0 MD5sum: 8ea12f7e763a4e29041c175b11ec7521 NeedsCompilation: yes Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: devel git_last_commit: e7b07e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/MSstats_4.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstats_4.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstats_4.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstats_4.15.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html, vignettes/MSstats/inst/doc/MSstatsWorkflow.html vignetteTitles: MSstats: Protein/Peptide significance analysis, MSstats: End to End Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R, vignettes/MSstats/inst/doc/MSstatsWorkflow.R importsMe: artMS, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT dependencyCount: 100 Package: MSstatsBig Version: 1.5.0 Imports: arrow, DBI, dplyr, MSstats, MSstatsConvert, readr, sparklyr, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: c3002316043715283b27f4a4771a9965 NeedsCompilation: no Title: MSstats Preprocessing for Larger than Memory Data Description: MSstats package provide tools for preprocessing, summarization and differential analysis of mass spectrometry (MS) proteomics data. Recently, some MS protocols enable acquisition of data sets that result in larger than memory quantitative data. MSstats functions are not able to process such data. MSstatsBig package provides additional converter functions that enable processing larger than memory data sets. biocViews: MassSpectrometry, Proteomics, Software Author: Mateusz Staniak [aut, cre], Devon Kohler [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsBig git_branch: devel git_last_commit: 0338d8f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSstatsBig_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsBig_1.5.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsBig_1.5.0.tgz vignettes: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.html vignetteTitles: MSstatsBig Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.R dependencyCount: 123 Package: MSstatsConvert Version: 1.17.1 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 MD5sum: 641aca8297322470b87122ade727c0fb NeedsCompilation: no Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut, cre], Devon Kohler [aut], Anthony Wu [aut], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: devel git_last_commit: d3a2847 git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-12 source.ver: src/contrib/MSstatsConvert_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsConvert_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsConvert_1.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsConvert_1.17.1.tgz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R importsMe: MSstats, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT dependencyCount: 9 Package: MSstatsLiP Version: 1.13.0 Depends: R (>= 4.1) Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats, MSstatsConvert, data.table, Biostrings, MSstatsPTM, Rcpp, checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse, scales, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight License: Artistic-2.0 MD5sum: e5539d5b80514cb200595b9a3b4dca7c NeedsCompilation: yes Title: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments Description: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: devel git_last_commit: 256206e git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-26 source.ver: src/contrib/MSstatsLiP_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsLiP_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLiP_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLiP_1.13.0.tgz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R dependencyCount: 195 Package: MSstatsLOBD Version: 1.15.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 MD5sum: 823aa6c839ecfe76c20b60573dcb2445 NeedsCompilation: no Title: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD) Description: The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) . biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: devel git_last_commit: 3e3285d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSstatsLOBD_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsLOBD_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLOBD_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLOBD_1.15.0.tgz vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html vignetteTitles: LOB/LOD Estimation Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R dependencyCount: 37 Package: MSstatsPTM Version: 2.9.1 Depends: R (>= 4.3) Imports: dplyr, gridExtra, stringr, stats, ggplot2, stringi, grDevices, MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel LinkingTo: Rcpp Suggests: knitr, rmarkdown, tinytest, covr, mockery, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 735c3f9a531c1c16f62030b67d737ab2 NeedsCompilation: yes Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: devel git_last_commit: f1d70f4 git_last_commit_date: 2024-11-25 Date/Publication: 2024-11-26 source.ver: src/contrib/MSstatsPTM_2.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsPTM_2.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsPTM_2.9.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsPTM_2.9.1.tgz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP, MSstatsShiny dependencyCount: 116 Package: MSstatsQC Version: 2.25.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 Archs: x64 MD5sum: 370bcc5f149d778873363af83bf0a59d NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: devel git_last_commit: 0ea25be git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSstatsQC_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsQC_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQC_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQC_2.25.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 148 Package: MSstatsQCgui Version: 1.27.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 MD5sum: 4c0fa732e4260fd8af17101766107483 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: devel git_last_commit: 7c1f776 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MSstatsQCgui_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsQCgui_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQCgui_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQCgui_1.27.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 150 Package: MSstatsShiny Version: 1.9.0 Depends: R (>= 4.2) Imports: shiny, shinyBS, shinyjs, shinybusy, dplyr, ggplot2,plotly, data.table, Hmisc, MSstats, MSstatsTMT, MSstatsPTM, MSstatsConvert, gplots, marray, DT, readxl, ggrepel, uuid, utils, stats, htmltools, methods, tidyr, grDevices, graphics,mockery Suggests: rmarkdown, tinytest, sessioninfo, knitr, testthat (>= 3.0.0), shinytest2, License: Artistic-2.0 MD5sum: 53f7ec33dbc56b0874dd79d6616e5676 NeedsCompilation: no Title: MSstats GUI for Statistical Anaylsis of Proteomics Experiments Description: MSstatsShiny is an R-Shiny graphical user interface (GUI) integrated with the R packages MSstats, MSstatsTMT, and MSstatsPTM. It provides a point and click end-to-end analysis pipeline applicable to a wide variety of experimental designs. These include data-dependedent acquisitions (DDA) which are label-free or tandem mass tag (TMT)-based, as well as DIA, SRM, and PRM acquisitions and those targeting post-translational modifications (PTMs). The application automatically saves users selections and builds an R script that recreates their analysis, supporting reproducible data analysis. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, ShinyApps, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl, GUI Author: Devon Kohler [aut, cre], Deril Raju [aut], Maanasa Kaza [aut], Cristina Pasi [aut], Ting Huang [aut], Mateusz Staniak [aut], Dhaval Mohandas [aut], Eduard Sabido [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues git_url: https://git.bioconductor.org/packages/MSstatsShiny git_branch: devel git_last_commit: 41427c3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-26 source.ver: src/contrib/MSstatsShiny_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsShiny_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsShiny_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsShiny_1.9.0.tgz vignettes: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.html vignetteTitles: MSstatsPTM LabelFree Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.R dependencyCount: 155 Package: MSstatsTMT Version: 2.15.1 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate, plotly, htmltools Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 54787a7b82dcc55e7e0759a5de43f0d8 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Devon Kohler [aut, cre], Ting Huang [aut], Meena Choi [aut], Mateusz Staniak [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Devon Kohler URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: devel git_last_commit: 6bf3322 git_last_commit_date: 2024-11-14 Date/Publication: 2024-11-15 source.ver: src/contrib/MSstatsTMT_2.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MSstatsTMT_2.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsTMT_2.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MSstatsTMT_2.15.1.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsPTM, MSstatsShiny dependencyCount: 103 Package: MuData Version: 1.11.0 Depends: Matrix, S4Vectors, rhdf5 Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: 7999b99c97e87f5a129bcf9ddb59fd6e NeedsCompilation: no Title: Serialization for MultiAssayExperiment Objects Description: Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. biocViews: DataImport Author: Danila Bredikhin [aut, cre] (ORCID: ), Ilia Kats [aut] (ORCID: ) Maintainer: Danila Bredikhin URL: https://github.com/ilia-kats/MuData VignetteBuilder: knitr BugReports: https://github.com/ilia-kats/MuData/issues git_url: https://git.bioconductor.org/packages/MuData git_branch: devel git_last_commit: 372bd2d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MuData_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MuData_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MuData_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MuData_1.11.0.tgz vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html, vignettes/MuData/inst/doc/Getting-Started.html vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with MuData, Getting started with MuDataMae hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R, vignettes/MuData/inst/doc/Getting-Started.R dependencyCount: 62 Package: Mulcom Version: 1.57.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: b7e70e7d6a6df45746ee345d52fa8068 NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: devel git_last_commit: 9ce9679 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Mulcom_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Mulcom_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Mulcom_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Mulcom_1.57.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 15 Package: MultiAssayExperiment Version: 1.33.1 Depends: SummarizedExperiment (>= 1.3.81), R (>= 3.5.0) Imports: Biobase, BiocBaseUtils, BiocGenerics, DelayedArray, GenomicRanges, IRanges, methods, S4Vectors, tidyr, utils Suggests: BiocStyle, HDF5Array, knitr, maftools, R.rsp, RaggedExperiment, reshape2, rmarkdown, survival, survminer, testthat, UpSetR License: Artistic-2.0 MD5sum: eb7003297ad5e2241c2be9b557f210a0 NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb], Lori Shepherd [ctb], Hervé Pagès [ctb], Vincent J Carey [aut, ctb], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr, R.rsp Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: devel git_last_commit: 325c85c git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/MultiAssayExperiment_1.33.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiAssayExperiment_1.33.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiAssayExperiment_1.33.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiAssayExperiment_1.33.1.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: alabaster.mae, CAGEr, cBioPortalData, ClassifyR, evaluomeR, hipathia, HoloFoodR, InTAD, MGnifyR, mia, midasHLA, MIRit, missRows, QFeatures, terraTCGAdata, curatedPCaData, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, scMultiome, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, gDRcore, gDRimport, gDRutils, gINTomics, glmSparseNet, GOpro, hermes, LinkHD, metabolomicsWorkbenchR, MOMA, MOSClip, msqrob2, MuData, MultiBaC, MultimodalExperiment, nipalsMCIA, OMICsPCA, omicsPrint, omXplore, padma, PDATK, PharmacoGx, phenomis, ropls, scp, scPipe, survClust, TCGAutils, vsclust, xcore, curatedTBData, LegATo, TCGAWorkflow, MOCHA suggestsMe: CNVRanger, funOmics, maftools, MOFA2, MultiDataSet, RaggedExperiment, updateObject, brgedata, MOFAdata, teal, teal.slice dependencyCount: 57 Package: MultiBaC Version: 1.17.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 Archs: x64 MD5sum: fb8ba80f2156a4c502c9cb17ee10a2fb NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: devel git_last_commit: 032c958 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MultiBaC_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiBaC_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiBaC_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiBaC_1.17.0.tgz vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html vignetteTitles: MultiBaC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R dependencyCount: 108 Package: multiClust Version: 1.37.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 868b295c1be5befb4057896ec60c0237 NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: devel git_last_commit: 9909f55 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multiClust_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiClust_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiClust_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiClust_1.37.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 44 Package: multicrispr Version: 1.17.1 Depends: R (>= 4.0) Imports: BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 MD5sum: 1ffbfa8515bfa736b90eb7ac5a59ddfd NeedsCompilation: no Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Richie ´Cotton [aut], Rene Wiegandt [ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/multicrispr VignetteBuilder: knitr BugReports: https://github.com/bhagwataditya/multicrispr/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: devel git_last_commit: 2519b98 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/multicrispr_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/multicrispr_1.17.1.zip vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html, vignettes/multicrispr/inst/doc/genome_arithmetics.html, vignettes/multicrispr/inst/doc/prime_editing.html vignetteTitles: grna_design, genome_arithmetics, prime_editing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R, vignettes/multicrispr/inst/doc/genome_arithmetics.R, vignettes/multicrispr/inst/doc/prime_editing.R dependencyCount: 173 Package: MultiDataSet Version: 1.35.0 Depends: R (>= 4.1), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE Archs: x64 MD5sum: d2a318cce05873cad8a7a9be91c6ed25 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: devel git_last_commit: f5c8571 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MultiDataSet_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiDataSet_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiDataSet_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiDataSet_1.35.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, phenomis, ropls dependencyCount: 69 Package: multiGSEA Version: 1.17.2 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, metaboliteIDmapping, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: abad2d63a5f5ce73d42f29f5eaaceea1 NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (ORCID: ), Jörg Hackermüller [aut] (ORCID: ) Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: devel git_last_commit: 6def494 git_last_commit_date: 2024-11-25 Date/Publication: 2024-11-25 source.ver: src/contrib/multiGSEA_1.17.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiGSEA_1.17.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiGSEA_1.17.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiGSEA_1.17.2.tgz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 123 Package: multiHiCcompare Version: 1.25.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 1fe82ca51339595182302435441c1ee0 NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: devel git_last_commit: 62d7e43 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multiHiCcompare_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiHiCcompare_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiHiCcompare_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiHiCcompare_1.25.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: juiceboxVisualization, multiHiCcompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R importsMe: HiCDOC, OHCA suggestsMe: HiCcompare dependencyCount: 94 Package: MultiMed Version: 2.29.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 48a0a7fcb67017cf1144eb755ad4170b NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: devel git_last_commit: e0a8cee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MultiMed_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultiMed_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultiMed_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultiMed_2.29.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.29.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 2.0), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: 2d3d9b1ee6c26900075aef22a6f00903 NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [aut], Spencer Mahaffey [cre, aut], Katerina Kechris [aut, cph, ths] Maintainer: Spencer Mahaffey URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: devel git_last_commit: 2e6eb45 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multiMiR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiMiR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiMiR_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiMiR_1.29.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R suggestsMe: EpiMix dependencyCount: 58 Package: MultimodalExperiment Version: 1.7.0 Depends: R (>= 4.3.0), IRanges, S4Vectors Imports: BiocGenerics, MultiAssayExperiment, methods, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: d09ed84d87473852430d6bb8cabf009c NeedsCompilation: no Title: Integrative Bulk and Single-Cell Experiment Container Description: MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data. biocViews: DataRepresentation, Infrastructure, SingleCell Author: Lucas Schiffer [aut, cre] (ORCID: ) Maintainer: Lucas Schiffer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultimodalExperiment git_branch: devel git_last_commit: d92c351 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MultimodalExperiment_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MultimodalExperiment_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MultimodalExperiment_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MultimodalExperiment_1.7.0.tgz vignettes: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.html vignetteTitles: MultimodalExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.R dependencyCount: 58 Package: MultiRNAflow Version: 1.5.0 Depends: Mfuzz (>= 2.58.0), R (>= 4.3) Imports: Biobase (>= 2.54.0), ComplexHeatmap (>= 2.14.0), DESeq2 (>= 1.38.1), factoextra (>= 1.0.7), FactoMineR (>= 2.6), ggalluvial (>= 0.12.3), ggplot2 (>= 3.4.0), ggplotify (>= 0.1.2), ggrepel (>= 0.9.2), gprofiler2 (>= 0.2.1), graphics (>= 4.2.2), grDevices (>= 4.2.2), grid (>= 4.2.2), plot3D (>= 1.4), plot3Drgl (>= 1.0.3), reshape2 (>= 1.4.4), S4Vectors (>= 0.36.2), stats (>= 4.2.2), SummarizedExperiment (>= 1.28.0), UpSetR (>= 1.4.0), utils (>= 4.2.2) Suggests: BiocGenerics (>= 0.40.0), BiocStyle, e1071 (>= 1.7.12), knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 | file LICENSE MD5sum: 458417663840e6c3de32e10cfc393072 NeedsCompilation: no Title: An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions Description: Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, TimeCourse, Preprocessing, Visualization, Normalization, PrincipalComponent, Clustering, DifferentialExpression, GeneSetEnrichment, Pathways Author: Rodolphe Loubaton [aut, cre] (ORCID: ), Nicolas Champagnat [aut, ths] (ORCID: ), Laurent Vallat [aut, ths] (ORCID: ), Pierre Vallois [aut] (ORCID: ), Région Grand Est [fnd], Cancéropôle Est [fnd] Maintainer: Rodolphe Loubaton URL: https://github.com/loubator/MultiRNAflow VignetteBuilder: knitr BugReports: https://github.com/loubator/MultiRNAflow/issues git_url: https://git.bioconductor.org/packages/MultiRNAflow git_branch: devel git_last_commit: 63c2461 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MultiRNAflow_1.5.0.tar.gz vignettes: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.pdf, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.html vignetteTitles: MultiRNAflow: A R package for analysing RNA-seq raw counts with different time points and several biological conditions., Running_analysis_with_MultiRNAflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.R, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.R dependencyCount: 188 Package: multiscan Version: 1.67.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 28304602c7eb1a50f32aff98300e9eb6 NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: devel git_last_commit: b974c24 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multiscan_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiscan_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiscan_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiscan_1.67.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 7 Package: multistateQTL Version: 1.3.0 Depends: QTLExperiment, SummarizedExperiment, ComplexHeatmap, data.table, collapse Imports: methods, S4Vectors, grid, dplyr, tidyr, matrixStats, stats, fitdistrplus, viridis, ggplot2, circlize, mashr, grDevices Suggests: testthat, BiocStyle, knitr, covr, rmarkdown License: GPL-3 MD5sum: ee030fbf2762baba14e5fc1f9ef72285 NeedsCompilation: no Title: Toolkit for the analysis of multi-state QTL data Description: A collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package 'multistateQTL' contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a 'QTLExperiment' object, which is based on the 'SummarisedExperiment' framework. biocViews: FunctionalGenomics, GeneExpression, Sequencing, Visualization, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, ctb] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/multistateQTL VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/multistateQTL/issues git_url: https://git.bioconductor.org/packages/multistateQTL git_branch: devel git_last_commit: d831d40 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multistateQTL_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multistateQTL_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multistateQTL_1.3.0.tgz vignettes: vignettes/multistateQTL/inst/doc/multiStateQTL.html vignetteTitles: multistateQTL: Orchestrating multi-state QTL analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multistateQTL/inst/doc/multiStateQTL.R dependencyCount: 117 Package: multiWGCNA Version: 1.5.0 Depends: R (>= 4.3.0), ggalluvial Imports: stringr, readr, WGCNA, dplyr, reshape2, data.table, patchwork, scales, igraph, flashClust, ggplot2, dcanr, cowplot, ggrepel, methods, SummarizedExperiment Suggests: BiocStyle, doParallel, ExperimentHub, knitr, markdown, rmarkdown, testthat (>= 3.0.0), vegan License: GPL-3 Archs: x64 MD5sum: 69e3eb1596d6f70b6c2861302a9f994c NeedsCompilation: no Title: multiWGCNA Description: An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Regression, Clustering Author: Dario Tommasini [aut, cre] (ORCID: ), Brent Fogel [aut, ctb] Maintainer: Dario Tommasini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiWGCNA git_branch: devel git_last_commit: b2881a4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multiWGCNA_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multiWGCNA_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multiWGCNA_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multiWGCNA_1.5.0.tgz vignettes: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.html, vignettes/multiWGCNA/inst/doc/autism_full_workflow.html vignetteTitles: Astrocyte multiWGCNA network, General Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.R, vignettes/multiWGCNA/inst/doc/autism_full_workflow.R suggestsMe: multiWGCNAdata dependencyCount: 148 Package: multtest Version: 2.63.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: be7aca1943b19bca8473ab3a9ae64e32 NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: devel git_last_commit: baeb862 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/multtest_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/multtest_2.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/multtest_2.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/multtest_2.63.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, GUIDEseq, metabomxtr, nethet, OCplus, RTopper, SARC, SingleCellSignalR, singleCellTK, synapter, webbioc, hddplot, INCATome, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, CAMERA, ecolitk, factDesign, GOstats, GSEAlm, ropls, topGO, xcms, cherry, POSTm dependencyCount: 15 Package: mumosa Version: 1.15.0 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 MD5sum: 6f503d8cd183b1dabe19e1daa63dd0d7 NeedsCompilation: no Title: Multi-Modal Single-Cell Analysis Methods Description: Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities. biocViews: ImmunoOncology, SingleCell, RNASeq Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: http://bioconductor.org/packages/mumosa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/mumosa git_branch: devel git_last_commit: df075a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mumosa_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mumosa_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mumosa_1.15.0.tgz vignettes: vignettes/mumosa/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mumosa/inst/doc/overview.R dependencyCount: 85 Package: MungeSumstats Version: 1.15.12 Depends: R(>= 4.1) Imports: data.table, utils, R.utils, dplyr, stats, GenomicRanges, GenomeInfoDb, IRanges, ieugwasr(>= 1.0.1), BSgenome, Biostrings, stringr, VariantAnnotation, methods, parallel, rtracklayer(>= 1.59.1), RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, Rsamtools, MatrixGenerics, badger, BiocParallel, GenomicFiles License: Artistic-2.0 MD5sum: 31d2b1990a2198e894bca269ac90be6d NeedsCompilation: no Title: Standardise summary statistics from GWAS Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [aut, cre] (ORCID: ), Brian Schilder [aut, ctb] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats, https://al-murphy.github.io/MungeSumstats/ VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: devel git_last_commit: 07eee45 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/MungeSumstats_1.15.12.tar.gz win.binary.ver: bin/windows/contrib/4.5/MungeSumstats_1.15.12.zip vignettes: vignettes/MungeSumstats/inst/doc/docker.html, vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: docker, MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/docker.R, vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 95 Package: muscat Version: 1.21.0 Depends: R (>= 4.4) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, rlang, S4Vectors, scales, scater, scuttle, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition, viridis Suggests: BiocStyle, countsimQC, ExperimentHub, iCOBRA, knitr, patchwork, phylogram, RColorBrewer, reshape2, rmarkdown, statmod, stageR, testthat, UpSetR License: GPL-3 MD5sum: b12eed2af5c7172844ea91e2b61b9d61 NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Jeroen Gilis [aut], Davide Risso [aut], Lieven Clement [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues git_url: https://git.bioconductor.org/packages/muscat git_branch: devel git_last_commit: 0bf2615 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-13 source.ver: src/contrib/muscat_1.21.0.tar.gz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/detection.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "3. Differential detection", "2. Data simulation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/detection.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: dreamlet, muscData dependencyCount: 178 Package: muscle Version: 3.49.0 Depends: Biostrings License: Unlimited MD5sum: 144d601eea9a36461e1c4c31db78a254 NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: devel git_last_commit: 25320f4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/muscle_3.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/muscle_3.49.0.zip vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R suggestsMe: seqmagick dependencyCount: 26 Package: musicatk Version: 2.1.0 Depends: R (>= 4.4.0), NMF Imports: SummarizedExperiment, VariantAnnotation, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, decompTumor2Sig, topicmodels, ggrepel, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, maftools, shiny, stringi, tidyverse, ggpubr, Matrix (>= 1.6.1), scales, conclust Suggests: TCGAbiolinks, shinyBS, shinyalert, shinybusy, shinydashboard, shinyjs, shinyjqui, sortable, testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr, shinyWidgets, cowplot, withr License: LGPL-3 MD5sum: 9ddb9df34fa7487261a5925cca11b3ac NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [aut] (ORCHID: 0000-0002-3968-9250), Natasha Gurevich [aut] (ORCHID: 0000-0002-0747-6840), Tao Guo [aut] (ORCHID: 0009-0005-8960-9203), Joshua D. Campbell [aut, cre] (ORCID: ) Maintainer: Joshua D. Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: devel git_last_commit: 45539aa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/musicatk_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/musicatk_2.1.0.zip vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 265 Package: MutationalPatterns Version: 3.17.0 Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: cb6e81e6ada005066c267820a8a284aa NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (ORCID: ), Francis Blokzijl [aut] (ORCID: ), Roel Janssen [aut] (ORCID: ), Jurrian de Kanter [ctb] (ORCID: ), Rurika Oka [ctb] (ORCID: ), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (ORCID: ), Edwin Cuppen [aut] (ORCID: ) Maintainer: Mark van Roosmalen URL: https://doi.org/doi:10.1186/s12864-022-08357-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: devel git_last_commit: 1bdb319 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/MutationalPatterns_3.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MutationalPatterns_3.17.0.zip vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R importsMe: RESOLVE suggestsMe: SUITOR dependencyCount: 124 Package: MVCClass Version: 1.81.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 21893935ad32913fe46c1fb2ad4164e3 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: devel git_last_commit: 72d5a1c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MVCClass_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MVCClass_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MVCClass_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MVCClass_1.81.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.31.0 Depends: R (>= 3.5.0) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 5ed75f82cb770ab70c0c620c4b30033f NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: devel git_last_commit: f47f1f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/MWASTools_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/MWASTools_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MWASTools_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/MWASTools_1.31.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 123 Package: mygene Version: 1.43.0 Depends: R (>= 3.2.1), GenomicFeatures, txdbmaker Imports: methods, utils, stats, httr (>= 0.3), jsonlite (>= 0.9.7), Hmisc, sqldf, plyr, S4Vectors Suggests: BiocStyle License: Artistic-2.0 MD5sum: 6949c47dba9ad742603226325aa74f70 NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: devel git_last_commit: 66cce1e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mygene_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mygene_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mygene_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mygene_1.43.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal suggestsMe: CRISPRball dependencyCount: 149 Package: myvariant Version: 1.37.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 87210f6ba43340b0f6946a05edba0514 NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: devel git_last_commit: ef53b5c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/myvariant_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/myvariant_1.37.0.zip vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 133 Package: mzID Version: 1.45.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) Archs: x64 MD5sum: 4fed8b4cc7058bb0fd5a0863c1b28ecb NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (ORCID: ), Thomas Pedersen [aut] (ORCID: ), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: devel git_last_commit: 3c854c5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/mzID_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/mzID_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mzID_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mzID_1.45.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSnbase, MSnID, TargetDecoy suggestsMe: mzR, PSMatch, RforProteomics dependencyCount: 11 Package: mzR Version: 2.41.1 Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: b964425b3bbd683ae916b60865565985 NeedsCompilation: yes Title: parser for netCDF, mzXML and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: devel git_last_commit: 0368dbf git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-19 source.ver: src/contrib/mzR_2.41.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/mzR_2.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/mzR_2.41.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/mzR_2.41.1.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSnbase importsMe: adductomicsR, CluMSID, MSnID, msPurity, peakPantheR, RMassBank, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, koinar, MetaboAnnotation, MsBackendMetaboLights, MsBackendRawFileReader, MsBackendSql, MsDataHub, MsExperiment, MsQuality, PSMatch, qcmetrics, Spectra, SpectraQL, msdata, RforProteomics, chromConverter, erah dependencyCount: 11 Package: NADfinder Version: 1.31.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager, rmarkdown License: GPL (>= 2) MD5sum: dfcfd756d9a1b057ea71d8c7c05d3ad1 NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: devel git_last_commit: 2b498e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NADfinder_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NADfinder_1.31.0.zip vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 245 Package: NanoMethViz Version: 3.3.0 Depends: R (>= 4.0.0), methods, ggplot2 (>= 3.4.0) Imports: cpp11 (>= 0.2.5), readr, cli, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, dbscan, e1071, fs, GenomicRanges, Biostrings, ggrastr, glue, graphics, IRanges, limma (>= 3.44.0), patchwork, purrr, rlang, R.utils, Rsamtools, scales (>= 1.2.0), stats, stringr, tibble, tidyr, utils, withr, zlibbioc LinkingTo: Rcpp Suggests: BiocStyle, DSS, Mus.musculus (>= 1.3.1), Homo.sapiens (>= 1.3.1), org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) Archs: x64 MD5sum: e23392fc80d7c7d293146c0821421322 NeedsCompilation: yes Title: Visualise methylation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, LongRead, Visualization, DifferentialMethylation, DNAMethylation, Epigenetics, DataImport Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz, https://shians.github.io/NanoMethViz/ SystemRequirements: C++20 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: devel git_last_commit: 1ee21fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NanoMethViz_3.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoMethViz_3.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoMethViz_3.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoMethViz_3.3.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/UsersGuide.html vignetteTitles: User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/UsersGuide.R dependencyCount: 150 Package: NanoStringDiff Version: 1.37.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: fd9c7ddb654eab15b7b39e125d3c5edd NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: devel git_last_commit: ceaa04c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NanoStringDiff_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoStringDiff_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringDiff_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoStringDiff_1.37.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R suggestsMe: NanoTube dependencyCount: 9 Package: NanoStringNCTools Version: 1.15.0 Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2 Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes, grDevices, IRanges, methods, pheatmap, RColorBrewer, stats, utils Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf License: MIT MD5sum: 83604c71b14e0e5766bad817719a44a1 NeedsCompilation: no Title: NanoString nCounter Tools Description: Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq Author: Patrick Aboyoun [aut], Nicole Ortogero [aut], Maddy Griswold [cre], Zhi Yang [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: devel git_last_commit: daac9af git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NanoStringNCTools_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoStringNCTools_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringNCTools_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoStringNCTools_1.15.0.tgz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools, GeoMxWorkflows importsMe: GeoDiff dependencyCount: 89 Package: NanoTube Version: 1.13.0 Depends: R (>= 4.1), Biobase, ggplot2, limma Imports: fgsea, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, RUVSeq, shiny, testthat, xlsx License: GPL-3 + file LICENSE Archs: x64 MD5sum: 4cc02ed05ecf1af624142d6d468586c1 NeedsCompilation: no Title: An Easy Pipeline for NanoString nCounter Data Analysis Description: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages. biocViews: Software, GeneExpression, DifferentialExpression, QualityControl Author: Caleb Class [cre, aut] (ORCID: ), Caiden Lukan [ctb] Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: devel git_last_commit: fe1b611 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NanoTube_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NanoTube_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NanoTube_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NanoTube_1.13.0.tgz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 57 Package: NBAMSeq Version: 1.23.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: abab2b087a76451bb6e6571ed2bc0dcc NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: devel git_last_commit: fbb8e45 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NBAMSeq_1.23.0.tar.gz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 95 Package: ncdfFlow Version: 2.53.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: cpp11,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: AGPL-3.0-only MD5sum: 1d7f8df7d33533de25ff85eea5495b2b NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: devel git_last_commit: b6e8c04 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ncdfFlow_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncdfFlow_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncdfFlow_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncdfFlow_2.53.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace suggestsMe: COMPASS, cydar dependencyCount: 19 Package: ncGTW Version: 1.21.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: ab868ba3f17505a26b0b5116f87cc27d NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: devel git_last_commit: 5de9a8f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ncGTW_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncGTW_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncGTW_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncGTW_1.21.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 147 Package: NCIgraph Version: 1.55.0 Depends: R (>= 4.0.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.oo Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 6b0e8d949277d08403424779584cc26e NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: devel git_last_commit: bbee7a4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NCIgraph_1.55.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 57 Package: ncRNAtools Version: 1.17.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: 5faa7ae333b4fcb0f345bd8243b29433 NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Guy-Bart Stan [aut] (ORCID: ), Rodrigo Ledesma-Amaro [aut] (ORCID: ) Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: devel git_last_commit: d839ffb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ncRNAtools_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ncRNAtools_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ncRNAtools_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ncRNAtools_1.17.0.tgz vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R dependencyCount: 55 Package: ndexr Version: 1.29.0 Depends: RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 87271ed2fb58adcf2a91fa67b0cdc54a NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer [cre, aut] (ORCID: ), Frank Kramer [ctb], Alex Ishkin [ctb], Dexter Pratt [ctb] Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: devel git_last_commit: b913e3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ndexr_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ndexr_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ndexr_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ndexr_1.29.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R dependencyCount: 41 Package: nearBynding Version: 1.17.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 8196a3fee43b7189be10dc2571f6437f NeedsCompilation: no Title: Discern RNA structure proximal to protein binding Description: Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats. biocViews: Visualization, MotifDiscovery, DataRepresentation, StructuralPrediction, Clustering, MultipleComparison Author: Veronica Busa [cre] Maintainer: Veronica Busa SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.22), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: devel git_last_commit: 287e346 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nearBynding_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nearBynding_1.17.0.zip vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf vignetteTitles: nearBynding Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R dependencyCount: 114 Package: Nebulosa Version: 1.17.0 Depends: R (>= 4.0), ggplot2, patchwork Imports: SingleCellExperiment, SummarizedExperiment, SeuratObject, ks, Matrix, stats, methods, ggrastr Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, Seurat License: GPL-3 MD5sum: d4aa735b6712adec7c40f4b11bee5344 NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] (ORCID: ) Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: devel git_last_commit: 29d6923 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Nebulosa_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Nebulosa_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Nebulosa_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Nebulosa_1.17.0.tgz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R suggestsMe: scCustomize, SCpubr dependencyCount: 98 Package: nempi Version: 1.15.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit, BiocStyle License: GPL-3 MD5sum: 3a9a4a76d1c66dbeb6318fb976bf4e6a NeedsCompilation: no Title: Inferring unobserved perturbations from gene expression data Description: Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models. biocViews: Software, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneSignaling, Pathways, Network, Classification, NeuralNetwork, NetworkInference, ATACSeq, DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell, SystemsBiology Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/nempi/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/nempi/issues git_url: https://git.bioconductor.org/packages/nempi git_branch: devel git_last_commit: f9f289f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nempi_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nempi_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nempi_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nempi_1.15.0.tgz vignettes: vignettes/nempi/inst/doc/nempi.html vignetteTitles: nempi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nempi/inst/doc/nempi.R dependencyCount: 115 Package: NetActivity Version: 1.9.0 Depends: R (>= 4.1.0) Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods, methods, NetActivityData, SummarizedExperiment, utils Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0), tidyverse License: MIT + file LICENSE MD5sum: 54ea588982b8187960f92539230b9213 NeedsCompilation: no Title: Compute gene set scores from a deep learning framework Description: #' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models. biocViews: RNASeq, Microarray, Transcription, FunctionalGenomics, GO, GeneExpression, Pathways, Software Author: Carlos Ruiz-Arenas [aut, cre] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetActivity git_branch: devel git_last_commit: 8c47a8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NetActivity_1.9.0.tar.gz vignettes: vignettes/NetActivity/inst/doc/NetActivity.html vignetteTitles: "Gene set scores computation with NetActivity" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NetActivity/inst/doc/NetActivity.R dependencyCount: 81 Package: netboost Version: 2.15.0 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, BiocStyle, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, rmarkdown License: GPL-3 OS_type: unix MD5sum: 1e40f74b6ce418c58afe29fc3aefd3c4 NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: pascal.schlosser@uniklinik-freiburg.de git_url: https://git.bioconductor.org/packages/netboost git_branch: devel git_last_commit: 5fe0a60 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/netboost_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netboost_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netboost_2.15.0.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 122 Package: nethet Version: 1.39.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 Archs: x64 MD5sum: c63e0e882caea11af11d8eebba01e156 NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: devel git_last_commit: 585f30a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nethet_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nethet_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nethet_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nethet_1.39.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 78 Package: NetPathMiner Version: 1.43.1 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 9370b227d7831c87ed204069854dead0 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed [aut, cre] (ORCID: ), Tim Hancock [aut], Tim Hancock [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: devel git_last_commit: b338534 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/NetPathMiner_1.43.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NetPathMiner_1.43.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NetPathMiner_1.43.1.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 17 Package: netprioR Version: 1.33.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: 7bd5c4c40d2144d6f3fcc8d2b707529f NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: devel git_last_commit: 9afdf83 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/netprioR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/netprioR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netprioR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netprioR_1.33.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 48 Package: netresponse Version: 1.67.0 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) Archs: x64 MD5sum: 558241aae920cf98f6d80e09b6363628 NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: devel git_last_commit: 71a1a40 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/netresponse_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/netresponse_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netresponse_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netresponse_1.67.0.tgz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 77 Package: NetSAM Version: 1.47.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 2.0.0), tools (>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0) Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10), foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0), R2HTML (>= 2.2.0), DBI (>= 0.5-1) Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr, markdown License: LGPL Archs: x64 MD5sum: 3bb23fdb9594b014d78082ff32f3efc0 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules. Author: Jing Wang Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: devel git_last_commit: c829ddb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NetSAM_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NetSAM_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NetSAM_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NetSAM_1.47.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 138 Package: netSmooth Version: 1.27.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 MD5sum: 194984c4e926d64b87e37446df6b9299 NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: devel git_last_commit: 4d3aa76 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/netSmooth_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/netSmooth_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netSmooth_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netSmooth_1.27.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R dependencyCount: 177 Package: NewWave Version: 1.17.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 MD5sum: d622f6a38e2c1a03c3aff17f6fd0556d NeedsCompilation: no Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: devel git_last_commit: ef0cec5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NewWave_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NewWave_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NewWave_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NewWave_1.17.0.tgz vignettes: vignettes/NewWave/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NewWave/inst/doc/vignette.R dependencyCount: 56 Package: ngsReports Version: 2.9.0 Depends: R (>= 4.2.0), BiocGenerics, ggplot2 (>= 3.5.0), patchwork (>= 1.1.1), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.1.0), forcats, ggdendro, grDevices (>= 3.6.0), grid, jsonlite, lifecycle, lubridate, methods, plotly (>= 4.9.4), reshape2, rlang, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, DT, knitr, pander, readr, testthat, truncnorm License: LGPL-3 Archs: x64 MD5sum: 55b5f3b0553180ef1f656f6e201115c4 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects. biocViews: QualityControl, ReportWriting Author: Stevie Pederson [aut, cre] (ORCID: ), Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Stevie Pederson URL: https://github.com/smped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/smped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: devel git_last_commit: eb02955 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ngsReports_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ngsReports_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ngsReports_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ngsReports_2.9.0.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 99 Package: nipalsMCIA Version: 1.5.2 Depends: R (>= 4.3.0) Imports: ComplexHeatmap, dplyr, fgsea, ggplot2 (>= 3.0.0), graphics, grid, methods, MultiAssayExperiment, SummarizedExperiment, pracma, rlang, RSpectra, scales, stats Suggests: BiocFileCache, BiocStyle, circlize, ggpubr, KernSmooth, knitr, piggyback, reshape2, rmarkdown, rpart, Seurat (>= 4.0.0), spatstat.explore, stringr, survival, tidyverse, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 925f0aa76576e476324821beb91e142a NeedsCompilation: no Title: Multiple Co-Inertia Analysis via the NIPALS Method Description: Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies. biocViews: Software, Clustering, Classification, MultipleComparison, Normalization, Preprocessing, SingleCell Author: Maximilian Mattessich [cre] (ORCID: ), Joaquin Reyna [aut] (ORCID: ), Edel Aron [aut] (ORCID: ), Ferhat Ay [aut] (ORCID: ), Steven Kleinstein [aut] (ORCID: ), Anna Konstorum [aut] (ORCID: ) Maintainer: Maximilian Mattessich URL: https://github.com/Muunraker/nipalsMCIA VignetteBuilder: knitr BugReports: https://github.com/Muunraker/nipalsMCIA/issues git_url: https://git.bioconductor.org/packages/nipalsMCIA git_branch: devel git_last_commit: cc5fde5 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/nipalsMCIA_1.5.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/nipalsMCIA_1.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nipalsMCIA_1.5.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nipalsMCIA_1.5.2.tgz vignettes: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.html, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.html, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.html vignetteTitles: Analysis of MCIA Decomposition, Predicting New MCIA scores, Single Cell Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.R, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.R, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.R dependencyCount: 102 Package: nnNorm Version: 2.71.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 4c1fd87352174e61b49cc88ed980223b NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: devel git_last_commit: 18cab7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nnNorm_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nnNorm_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nnNorm_2.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nnNorm_2.71.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 9 Package: nnSVG Version: 1.11.0 Depends: R (>= 4.2) Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods Suggests: BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat License: MIT + file LICENSE MD5sum: 218b6c64f04970dd34a2a965981a3f2b NeedsCompilation: no Title: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data Description: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Lukas M. Weber [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues git_url: https://git.bioconductor.org/packages/nnSVG git_branch: devel git_last_commit: 9c91054 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nnSVG_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nnSVG_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nnSVG_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nnSVG_1.11.0.tgz vignettes: vignettes/nnSVG/inst/doc/nnSVG.html vignetteTitles: nnSVG Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R importsMe: spoon suggestsMe: tpSVG dependencyCount: 87 Package: NOISeq Version: 2.51.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 4b1fbace1be3e90f1f2893dc73a1bf6c NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: devel git_last_commit: bf39f9c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NOISeq_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NOISeq_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NOISeq_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NOISeq_2.51.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: broadSeq, CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR, GeoTcgaData dependencyCount: 12 Package: NoRCE Version: 1.19.0 Depends: R (>= 4.2.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,GenomeInfoDb, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db,zlibbioc, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods,markdown License: MIT + file LICENSE MD5sum: b2066a1609888adcb5bd47e6f7240b2a NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: devel git_last_commit: 18045b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NoRCE_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NoRCE_1.19.0.zip vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 129 Package: normalize450K Version: 1.35.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 78484d03102ea8c39d04117448bd2b97 NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: devel git_last_commit: 089eb29 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/normalize450K_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/normalize450K_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/normalize450K_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/normalize450K_1.35.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 13 Package: NormalyzerDE Version: 1.25.0 Depends: R (>= 4.1.0) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 98e96afa5fc4b9ea485284bcdc2f5aed NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: devel git_last_commit: cfe738a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NormalyzerDE_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NormalyzerDE_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NormalyzerDE_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NormalyzerDE_1.25.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.html vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R importsMe: PRONE dependencyCount: 107 Package: NormqPCR Version: 1.53.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 0432eadf6037ee41bc21dfebfaaf7d60 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: devel git_last_commit: 291a5b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NormqPCR_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NormqPCR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NormqPCR_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NormqPCR_1.53.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 48 Package: normr Version: 1.33.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 Archs: x64 MD5sum: 393c7ceb3efa5ec4f9e7e0fca3546aea NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: devel git_last_commit: 828cac9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/normr_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/normr_1.33.0.zip vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 92 Package: NPARC Version: 1.19.0 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 MD5sum: 13cf50fe79a1a2db0b4b40e37f9ab2c7 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: devel git_last_commit: 6cd65a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NPARC_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NPARC_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NPARC_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NPARC_1.19.0.tgz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 39 Package: npGSEA Version: 1.43.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: fc99ca3500439255b5e38f2e20b3ab47 NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: devel git_last_commit: 0cb512d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/npGSEA_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/npGSEA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/npGSEA_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/npGSEA_1.43.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 51 Package: NTW Version: 1.57.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 7738655c0d448bc2a87e4a1fb50e4dbf NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: devel git_last_commit: 1d0ba97 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NTW_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NTW_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NTW_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NTW_1.57.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 3 Package: nucleoSim Version: 1.35.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 1e78d58be37b69699a2144aa06c1c63e NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: devel git_last_commit: 334622c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nucleoSim_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nucleoSim_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nucleoSim_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nucleoSim_1.35.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 9 Package: nucleR Version: 2.39.0 Depends: R (>= 3.5.0), methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: bc7c033a79a42ac8e58571d47700e343 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Alba Sala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: devel git_last_commit: 242e3bb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nucleR_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nucleR_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nucleR_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nucleR_2.39.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 90 Package: nuCpos Version: 1.25.0 Depends: R (>= 4.2.0) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 6d0e72d7066c2d6c91d88e7874b55efe NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. nuCpos calculates local and whole nucleosomal histone binding affinity (HBA) scores for a given 147-bp sequence. Note: This package was designed to demonstrate the use of chemical maps in prediction. As the parental package NuPoP now provides chemical-map-based prediction, the function for dHMM-based prediction was removed from this package. nuCpos continues to provide functions for HBA calculation. biocViews: Genetics, Epigenetics, NucleosomePositioning Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: devel git_last_commit: 8f49c5e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nuCpos_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nuCpos_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nuCpos_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nuCpos_1.25.0.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: nullranges Version: 1.13.0 Imports: stats, IRanges, GenomicRanges, GenomeInfoDb, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, ks, DNAcopy, RcppHMM, AnnotationHub, ExperimentHub, nullrangesData, excluderanges, ensembldb, EnsDb.Hsapiens.v86, BSgenome.Hsapiens.UCSC.hg38, patchwork, plotgardener, dplyr, purrr, magrittr, tidyr, cobalt, DiagrammeR, tidySummarizedExperiment License: GPL-3 MD5sum: 5980e2646decf5d6c0bdd149e810420c NeedsCompilation: no Title: Generation of null ranges via bootstrapping or covariate matching Description: Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package. biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics, Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation, Annotation, GenomeWideAssociation, HistoneModification, ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel Author: Michael Love [aut, cre] (ORCID: ), Wancen Mu [aut] (ORCID: ), Eric Davis [aut] (ORCID: ), Douglas Phanstiel [aut] (ORCID: ), Stuart Lee [aut] (ORCID: ), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love URL: https://nullranges.github.io/nullranges, https://github.com/nullranges/nullranges VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: devel git_last_commit: 8b24586 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/nullranges_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/nullranges_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/nullranges_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/nullranges_1.13.0.tgz vignettes: vignettes/nullranges/inst/doc/bootRanges.html, vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_pool_set.html, vignettes/nullranges/inst/doc/matchRanges.html, vignettes/nullranges/inst/doc/nullranges.html vignetteTitles: 1. Introduction to bootRanges, 4. Matching case study II: CTCF orientation, 3. Matching case study I: CTCF occupancy, 5. Creating a pool set for matchRanges, 2. Introduction to matchRanges, 0. Introduction to nullranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/bootRanges.R, vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_pool_set.R, vignettes/nullranges/inst/doc/matchRanges.R, vignettes/nullranges/inst/doc/nullranges.R importsMe: tidyomics dependencyCount: 95 Package: NuPoP Version: 2.15.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 7f5f067ac4d6c00f11b2268b57b3b9ed NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP. biocViews: Genetics,Visualization,Classification,NucleosomePositioning, HiddenMarkovModel Author: Ji-Ping Wang ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: devel git_last_commit: 6983e4d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/NuPoP_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/NuPoP_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NuPoP_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/NuPoP_2.15.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP.html vignetteTitles: NuPoP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R suggestsMe: nuCpos dependencyCount: 2 Package: occugene Version: 1.67.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 6eebfe10442f3c757a3ebb0dac4874fd NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: devel git_last_commit: c94c038 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/occugene_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/occugene_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/occugene_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/occugene_1.67.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.81.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp License: LGPL MD5sum: 23dee7a7069666cd5179cbd06af28655 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: devel git_last_commit: 530dc28 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OCplus_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OCplus_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OCplus_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OCplus_1.81.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 20 Package: octad Version: 1.9.0 Depends: R (>= 4.2.0), magrittr, dplyr, ggplot2, edgeR, RUVSeq, DESeq2, limma, rhdf5, foreach, Rfast, octad.db, stats, httr, qpdf, ExperimentHub, AnnotationHub, Biobase, S4Vectors Imports: EDASeq, GSVA, data.table, htmlwidgets, plotly, reshape2, grDevices, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: ef7599273b99997b87f05720915b9b4b NeedsCompilation: no Title: Open Cancer TherApeutic Discovery (OCTAD) Description: OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds. biocViews: Classification, GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, GeneSetEnrichment Author: E. Chekalin [aut, cre], S. Paithankar [aut], B. Zeng [aut], B. Glicksberg [ctb], P. Newbury [ctb], J. Xing [ctb], K. Liu [ctb], A. Wen [ctb], D. Joseph [ctb], B. Chen [aut] Maintainer: E. Chekalin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/octad git_branch: devel git_last_commit: 33eb152 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/octad_1.9.0.tar.gz vignettes: vignettes/octad/inst/doc/octad.html vignetteTitles: octad hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/octad/inst/doc/octad.R dependencyCount: 190 Package: odseq Version: 1.35.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: 5b6cb03994667432b1986fd1df905296 NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: devel git_last_commit: c7de581 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/odseq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/odseq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/odseq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/odseq_1.35.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 40 Package: OGRE Version: 1.11.0 Depends: R (>= 4.2.0), S4Vectors Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz, IRanges, AnnotationHub, grDevices, stats, GenomeInfoDb, shiny, shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) License: Artistic-2.0 MD5sum: 3b3ecdf353e81735378038f40666bce6 NeedsCompilation: no Title: Calculate, visualize and analyse overlap between genomic regions Description: OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level. biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation, Metagenomics, Visualization, Sequencing Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb], Sarah Sandmann [ctb], Sandra Laurentino [ctb] Maintainer: Sven Berres URL: https://github.com/svenbioinf/OGRE/ VignetteBuilder: knitr BugReports: https://github.com/svenbioinf/OGRE/issues git_url: https://git.bioconductor.org/packages/OGRE git_branch: devel git_last_commit: 817241b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OGRE_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OGRE_1.11.0.zip vignettes: vignettes/OGRE/inst/doc/OGRE.html vignetteTitles: OGRE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGRE/inst/doc/OGRE.R dependencyCount: 173 Package: oligo Version: 1.71.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) Archs: x64 MD5sum: 5d1e895194461fc917a0dbe2cc58ea77 NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho URL: https://github.com/benilton/oligo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: devel git_last_commit: b720ab9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oligo_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oligo_1.71.0.zip vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd importsMe: ArrayExpress, cn.farms, frma, ITALICS, mimager suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI, RCPA dependencyCount: 64 Package: oligoClasses Version: 1.69.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 73401f7f1c51d7554ee9a98bbce1ee71 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: devel git_last_commit: 37e0a0e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oligoClasses_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oligoClasses_1.69.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, maEndToEnd importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 60 Package: OLIN Version: 1.85.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: ddfcf6f50ba1cd6309668ae7279f83d4 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: devel git_last_commit: f6d5168 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OLIN_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OLIN_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OLIN_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OLIN_1.85.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui dependencyCount: 11 Package: OLINgui Version: 1.81.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 46217904e7b2610c3c5d0432573f3f71 NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: devel git_last_commit: 49b185c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OLINgui_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OLINgui_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OLINgui_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OLINgui_1.81.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 17 Package: omada Version: 1.9.0 Depends: pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc (>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>= 3.3.5), reshape (>= 0.8.8), genieclust (>= 1.1.3), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr(>= 1.0.7), stats (>= 4.1.2), clValid(>= 0.7) Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: d15e85b820a271eeff7a9a584a9ca784 NeedsCompilation: no Title: Machine learning tools for automated transcriptome clustering analysis Description: Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found. biocViews: Software, Clustering, RNASeq, GeneExpression Author: Sokratis Kariotis [aut, cre] (ORCID: ) Maintainer: Sokratis Kariotis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omada git_branch: devel git_last_commit: 5caa0b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omada_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omada_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omada_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omada_1.9.0.tgz vignettes: vignettes/omada/inst/doc/omada-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omada/inst/doc/omada-vignette.R dependencyCount: 151 Package: OmaDB Version: 2.23.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: f6e1f4ca353ab611a8b41583836a37b4 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: devel git_last_commit: c8dc611 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OmaDB_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmaDB_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OmaDB_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OmaDB_2.23.0.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R suggestsMe: orthogene, PhyloProfile dependencyCount: 60 Package: omicade4 Version: 1.47.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: 4d54298833335f07f5a47553bac29bb7 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: devel git_last_commit: 8db5f0e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omicade4_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicade4_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicade4_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicade4_1.47.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, phenomis, ropls dependencyCount: 51 Package: OmicCircos Version: 1.45.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: d21546f0aa2e3c8f3321fbc83fcbad61 NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: devel git_last_commit: 852429c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OmicCircos_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmicCircos_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OmicCircos_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OmicCircos_1.45.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 24 Package: omicplotR Version: 1.27.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE Archs: x64 MD5sum: 2acf081db8a439edb112a30b510060d3 NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: devel git_last_commit: 3f163b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omicplotR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicplotR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicplotR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicplotR_1.27.0.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 116 Package: omicRexposome Version: 1.29.0 Depends: R (>= 3.5.0), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: a82506f29faf3c15f6561fd146e6397a NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: devel git_last_commit: 0f24216 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omicRexposome_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicRexposome_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicRexposome_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicRexposome_1.29.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 226 Package: OmicsMLRepoR Version: 1.1.0 Depends: R (>= 4.4.0) Imports: dplyr, stringr, rols, tidyr, methods, stats, tibble, data.tree, jsonlite, plyr, BiocFileCache, readr, DiagrammeR, rlang, lubridate Suggests: arrow, knitr, BiocStyle, curatedMetagenomicData, testthat (>= 3.0.0), cBioPortalData License: Artistic-2.0 MD5sum: 19eee3829e249de8d683a368c3ccb747 NeedsCompilation: no Title: Search harmonized metadata created under the OmicsMLRepo project Description: This package provides functions to browse the harmonized metadata for large omics databases. This package also supports data navigation if the metadata incorporates ontology. biocViews: Software, Infrastructure, DataRepresentation Author: Sehyun Oh [aut, cre] (ORCID: ), Kaelyn Long [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/OmicsMLRepoR VignetteBuilder: knitr BugReports: https://github.com/shbrief/OmicsMLRepoR/issues git_url: https://git.bioconductor.org/packages/OmicsMLRepoR git_branch: devel git_last_commit: 1269f93 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OmicsMLRepoR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmicsMLRepoR_1.1.0.zip vignettes: vignettes/OmicsMLRepoR/inst/doc/Quickstart.html vignetteTitles: Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicsMLRepoR/inst/doc/Quickstart.R dependencyCount: 94 Package: OMICsPCA Version: 1.25.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: cd2fee889fd952ef082f84dda7cdb928 NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: devel git_last_commit: 1659da8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OMICsPCA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OMICsPCA_1.25.0.zip vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 206 Package: omicsPrint Version: 1.27.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: df82c7bf6bcb8e9a92a503c8b96ddc99 NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: devel git_last_commit: 8c45530 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omicsPrint_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicsPrint_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicsPrint_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicsPrint_1.27.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 60 Package: omicsViewer Version: 1.11.0 Depends: R (>= 4.2) Imports: survminer, survival, fastmatch, reshape2, stringr, beeswarm, grDevices, DT, shiny, shinythemes, shinyWidgets, plotly, networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea, openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics, grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2, S4Vectors, SummarizedExperiment, RSQLite, Matrix, shinycssloaders, ROCR, drc Suggests: BiocStyle, knitr, rmarkdown, unittest License: GPL-2 Archs: x64 MD5sum: b12d1e5ddfb7b060e23706530bbc13cf NeedsCompilation: no Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript. biocViews: Software, Visualization, GeneSetEnrichment, DifferentialExpression, MotifDiscovery, Network, NetworkEnrichment Author: Chen Meng [aut, cre] Maintainer: Chen Meng URL: https://github.com/mengchen18/omicsViewer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk BugReports: https://github.com/mengchen18/omicsViewer git_url: https://git.bioconductor.org/packages/omicsViewer git_branch: devel git_last_commit: 16e3d3d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omicsViewer_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omicsViewer_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omicsViewer_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omicsViewer_1.11.0.tgz vignettes: vignettes/omicsViewer/inst/doc/quickStart.html vignetteTitles: quickStart.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R dependencyCount: 193 Package: Omixer Version: 1.17.0 Depends: R (>= 4.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: cbc36a3ac0bee6ebcb1fc14fb995316d NeedsCompilation: no Title: Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies Description: Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: devel git_last_commit: c6bc854 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Omixer_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Omixer_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Omixer_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Omixer_1.17.0.tgz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 54 Package: OmnipathR Version: 3.15.0 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr(>= 1.1.0), httr, igraph, jsonlite, later, logger, lubridate, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown, RSQLite, R.utils, rvest, stats, stringi, stringr, tibble, tidyr, tidyselect, tools, utils, vctrs, withr, XML, xml2, yaml, zip Suggests: BiocStyle, bookdown, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, Rgraphviz, R.matlab, sigmajs, smoof, supraHex, testthat License: MIT + file LICENSE MD5sum: 4424da5802837e2b0ba6bf1d3ab7f4a6 NeedsCompilation: no Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (ORCID: ), Denes Turei [cre, aut] (ORCID: ), Attila Gabor [aut] (ORCID: ), Diego Mananes [aut] (ORCID: ), Aurelien Dugourd [aut] (ORCID: ) Maintainer: Denes Turei URL: https://r.omnipathdb.org/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: devel git_last_commit: bc4d2dd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OmnipathR_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OmnipathR_3.15.0.zip vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html, vignettes/OmnipathR/inst/doc/cosmos.html, vignettes/OmnipathR/inst/doc/db_manager.html, vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/extra_attrs.html, vignettes/OmnipathR/inst/doc/nichenet.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html, vignettes/OmnipathR/inst/doc/paths.html vignetteTitles: OmniPath Bioconductor workshop, COSMOS PKN, Database manager, Building networks around drug-targets using OmnipathR, Extra attributes, Using NicheNet with OmnipathR, OmnipathR: an R client for the OmniPath web service, Pathway construction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R, vignettes/OmnipathR/inst/doc/cosmos.R, vignettes/OmnipathR/inst/doc/db_manager.R, vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/extra_attrs.R, vignettes/OmnipathR/inst/doc/nichenet.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R, vignettes/OmnipathR/inst/doc/paths.R importsMe: gINTomics, wppi suggestsMe: decoupleR, dorothea dependencyCount: 89 Package: ompBAM Version: 1.11.0 Imports: utils, Rcpp Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 08954273a47228a1a4f3b9f52faaff79 NeedsCompilation: no Title: C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files Description: This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM. biocViews: Alignment, DataImport, RNASeq, Software, Sequencing, Transcriptomics, SingleCell Author: Alex Chit Hei Wong [aut, cre, cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/ompBAM VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/ompBAM git_branch: devel git_last_commit: 08d7d83 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ompBAM_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ompBAM_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ompBAM_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ompBAM_1.11.0.tgz vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html vignetteTitles: ompBAM API Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R importsMe: SpliceWiz linksToMe: SpliceWiz dependencyCount: 3 Package: omXplore Version: 1.1.0 Depends: R (>= 4.4.0), methods Imports: shiny, MSnbase, PSMatch, SummarizedExperiment, MultiAssayExperiment, shinyBS, shinyjs, shinyjqui, DT, RColorBrewer, gplots, highcharter, visNetwork, tibble, grDevices, stats, utils, htmlwidgets, vioplot, graphics, FactoMineR, dendextend, dplyr, factoextra, tidyr Suggests: knitr, rmarkdown, BiocStyle, testthat, Matrix, graph License: Artistic-2.0 MD5sum: cbe09f47688846c8becd1c92636c84b4 NeedsCompilation: no Title: Vizualization tools for 'omics' datasets with R Description: This package contains a collection of functions (written as shiny modules) for the visualisation and the statistical analysis of omics data. These plots can be displayed individually or embedded in a global Shiny module. Additionaly, it is possible to integrate third party modules to the main interface of the package omXplore. biocViews: Software, ShinyApps, MassSpectrometry, DataRepresentation, GUI, QualityControl Author: Samuel Wieczorek [aut, cre] (ORCID: ), Thomas Burger [aut], Enora Fremy [ctb] Maintainer: Samuel Wieczorek URL: https://github.com/prostarproteomics/omXplore, https://prostarproteomics.github.io/omXplore/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/omXplore/issues git_url: https://git.bioconductor.org/packages/omXplore git_branch: devel git_last_commit: 89d574c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/omXplore_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/omXplore_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/omXplore_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/omXplore_1.1.0.tgz vignettes: vignettes/omXplore/inst/doc/addingThirdPartyPlots.html, vignettes/omXplore/inst/doc/omXplore.html vignetteTitles: Adding third party plots, omXplore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omXplore/inst/doc/addingThirdPartyPlots.R, vignettes/omXplore/inst/doc/omXplore.R dependencyCount: 207 Package: oncomix Version: 1.29.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 8c70422ced1f22bf8be74b31c95377ac NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: devel git_last_commit: 34c2f12 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oncomix_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oncomix_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oncomix_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oncomix_1.29.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 66 Package: oncoscanR Version: 1.9.0 Depends: R (>= 4.2), IRanges (>= 2.30.0), GenomicRanges (>= 1.48.0), magrittr Imports: readr, S4Vectors, methods, utils Suggests: testthat (>= 3.1.4), jsonlite, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 73a4e5e5706d0624c02639c4d5ac0c60 NeedsCompilation: no Title: Secondary analyses of CNV data (HRD and more) Description: The software uses the copy number segments from a text file and identifies all chromosome arms that are globally altered and computes various genome-wide scores. The following HRD scores (characteristic of BRCA-mutated cancers) are included: LST, HR-LOH, nLST and gLOH. the package is tailored for the ThermoFisher Oncoscan assay analyzed with their Chromosome Alteration Suite (ChAS) but can be adapted to any input. biocViews: CopyNumberVariation, Microarray, Software Author: Yann Christinat [aut, cre], Geneva University Hospitals [aut, cph] Maintainer: Yann Christinat URL: https://github.com/yannchristinat/oncoscanR VignetteBuilder: knitr BugReports: https://github.com/yannchristinat/oncoscanR/issues git_url: https://git.bioconductor.org/packages/oncoscanR git_branch: devel git_last_commit: bef9380 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oncoscanR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oncoscanR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oncoscanR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oncoscanR_1.9.0.tgz vignettes: vignettes/oncoscanR/inst/doc/oncoscanR.html vignetteTitles: oncoscanR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oncoscanR/inst/doc/oncoscanR.R dependencyCount: 49 Package: OncoScore Version: 1.35.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 952893ee51d57b2ec77a4aed5d3ee264 NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [cre, aut] (ORCID: ), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut] (ORCID: ), Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: devel git_last_commit: 0ae7426 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/OncoScore_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OncoScore_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OncoScore_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OncoScore_1.35.0.tgz vignettes: vignettes/OncoScore/inst/doc/v1_introduction.html, vignettes/OncoScore/inst/doc/v2_running_OncoScore.html vignetteTitles: Introduction, Running OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/v1_introduction.R, vignettes/OncoScore/inst/doc/v2_running_OncoScore.R dependencyCount: 69 Package: OncoSimulR Version: 4.9.0 Depends: R (>= 3.5.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, stringr LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: 7b88637a9b22f94bceec029c3acf3ecd NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Alberto Gonzalez Klein [aut], Javier Mu\~noz Haro [aut], Javier Lopez Cano [aut], Niklas Endres [ctb], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Tymoteusz Wolodzko [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: devel git_last_commit: 3291e0a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OncoSimulR_4.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OncoSimulR_4.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OncoSimulR_4.9.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 78 Package: onlineFDR Version: 2.15.0 Imports: stats, Rcpp, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: dd4b56870118f4d8731d2efb251d6191 NeedsCompilation: yes Title: Online error rate control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb], Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: devel git_last_commit: 4d673fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/onlineFDR_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/onlineFDR_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/onlineFDR_2.15.0.tgz vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html, vignettes/onlineFDR/inst/doc/onlineFDR.html, vignettes/onlineFDR/inst/doc/theory.html vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR package, The theory behind onlineFDR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R, vignettes/onlineFDR/inst/doc/onlineFDR.R, vignettes/onlineFDR/inst/doc/theory.R dependencyCount: 17 Package: ontoProc Version: 2.1.3 Depends: R (>= 4.0), ontologyIndex Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub, SummarizedExperiment, reticulate, R.utils, httr, basilisk Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown, AnnotationDbi, magick License: Artistic-2.0 MD5sum: 0c0a62c83e8384757d4c6e8817e6ece1 NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vincent Carey [ctb, cre] (ORCID: ), Sara Stankiewicz [ctb], Victor Tarca [ctb] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/ontoProc VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ontoProc/issues git_url: https://git.bioconductor.org/packages/ontoProc git_branch: devel git_last_commit: e973100 git_last_commit_date: 2024-12-04 Date/Publication: 2024-12-04 source.ver: src/contrib/ontoProc_2.1.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/ontoProc_2.1.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ontoProc_2.1.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ontoProc_2.1.3.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html, vignettes/ontoProc/inst/doc/owlents.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor, owlents: using OWL directly in ontoProc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R, vignettes/ontoProc/inst/doc/owlents.R dependsOnMe: SingleRBook importsMe: pogos, tenXplore suggestsMe: scDiffCom dependencyCount: 120 Package: openCyto Version: 2.19.0 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,flowCore(>= 1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.99.1),flowClust(>= 3.11.4),RBGL,graph,data.table,RColorBrewer LinkingTo: cpp11, BH Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2), MASS License: AGPL-3.0-only MD5sum: d4b4654b2def87fe087a41e10c13385e NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: devel git_last_commit: d3a4d87 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/openCyto_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/openCyto_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/openCyto_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/openCyto_2.19.0.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime, flowWorkspace, ggcyto dependencyCount: 79 Package: openPrimeR Version: 1.29.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), pwalign, XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: 14375d1dd4c8ddafdf2775398c0e254b NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: devel git_last_commit: 74182ac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/openPrimeR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/openPrimeR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/openPrimeR_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/openPrimeR_1.29.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependencyCount: 116 Package: OpenStats Version: 1.19.1 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: be22256f8ac9a2d6559b1a3a969b4512 NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Marina Kan URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: devel git_last_commit: 4083881 git_last_commit_date: 2024-11-29 Date/Publication: 2024-11-29 source.ver: src/contrib/OpenStats_1.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/OpenStats_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OpenStats_1.19.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OpenStats_1.19.1.tgz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 126 Package: oposSOM Version: 2.25.0 Depends: R (>= 4.0.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl LinkingTo: RcppParallel, Rcpp License: GPL (>=2) MD5sum: 93a1d4b0056b2feb6f34a41c140e6272 NeedsCompilation: yes Title: Comprehensive analysis of transcriptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: devel git_last_commit: a26ada9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oposSOM_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oposSOM_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oposSOM_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oposSOM_2.25.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 87 Package: oppar Version: 1.35.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 MD5sum: 356013bd608cd54e83058297509c6ecd NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: devel git_last_commit: f758652 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oppar_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oppar_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oppar_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oppar_1.35.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 104 Package: oppti Version: 1.21.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT MD5sum: b8803010bb70e46442aef7bfb14e3d1a NeedsCompilation: no Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues git_url: https://git.bioconductor.org/packages/oppti git_branch: devel git_last_commit: 1c8e74d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/oppti_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/oppti_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/oppti_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/oppti_1.21.0.tgz vignettes: vignettes/oppti/inst/doc/analysis.html vignetteTitles: Outlier Protein and Phosphosite Target Identifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppti/inst/doc/analysis.R dependencyCount: 125 Package: optimalFlow Version: 1.19.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 MD5sum: 14e9c96630d7057a9c5586169d620834 NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: devel git_last_commit: 3518f15 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/optimalFlow_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/optimalFlow_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/optimalFlow_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/optimalFlow_1.19.0.tgz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 97 Package: OPWeight Version: 1.29.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: b69c46c788876529773682b2bdd88f52 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: devel git_last_commit: 1f5f472 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/OPWeight_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OPWeight_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OPWeight_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OPWeight_1.29.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 42 Package: OrderedList Version: 1.79.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: c5b12b717a1773d869b1d4c8075cb2da NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: devel git_last_commit: febc9a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OrderedList_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OrderedList_1.79.0.zip vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 10 Package: ORFhunteR Version: 1.15.0 Depends: Biostrings, rtracklayer, Peptides Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table, stringr, randomForest, xfun, stats, utils, parallel, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: MIT License MD5sum: 8834a241709a6322d9aa5ea77b9f68d3 NeedsCompilation: yes Title: Predict open reading frames in nucleotide sequences Description: The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Classification, FeatureExtraction Author: Vasily V. Grinev [aut, cre] (ORCID: ), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut] (ORCID: ), Petr V. Nazarov [aut] (ORCID: ) Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: devel git_last_commit: 7fcb3ee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ORFhunteR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ORFhunteR_1.15.0.zip vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html vignetteTitles: The ORFhunteR package: User’s manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R dependencyCount: 74 Package: ORFik Version: 1.27.1 Depends: R (>= 4.1.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt, biomartr (>= 1.0.7), BiocFileCache, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite, methods (>= 3.6.0), R.utils, Rcpp (>= 1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools, txdbmaker, utils, XML, xml2 (>= 1.2.0), withr LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: f2da873fc68f6f5278459e32d97d3033 NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Eivind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: devel git_last_commit: 6b522df git_last_commit_date: 2024-12-13 Date/Publication: 2024-12-13 source.ver: src/contrib/ORFik_1.27.1.tar.gz vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html, vignettes/ORFik/inst/doc/Importing_Data.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html, vignettes/ORFik/inst/doc/Working_with_transcripts.html vignetteTitles: Annotation & Alignment, Importing data, Data management, ORFik Overview, Ribo-seq pipeline, Working with transcripts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R, vignettes/ORFik/inst/doc/Importing_Data.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R, vignettes/ORFik/inst/doc/Working_with_transcripts.R dependsOnMe: RiboCrypt importsMe: TFHAZ dependencyCount: 139 Package: Organism.dplyr Version: 1.35.0 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils, BiocFileCache, DBI, dbplyr, tibble Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, magick, BiocStyle, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: a91589455b17be13cd08d528b5d6ba72 NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Organism.dplyr/issues git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: devel git_last_commit: 0892992 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Organism.dplyr_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Organism.dplyr_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Organism.dplyr_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Organism.dplyr_1.35.0.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 95 Package: OrganismDbi Version: 1.49.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4) Imports: methods, utils, stats, DBI, BiocManager, Biobase, graph, RBGL, S4Vectors, IRanges, GenomicRanges (>= 1.31.13), txdbmaker Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer, biomaRt, RUnit, RMariaDB, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 4aa38d81398e5a18a5ed24cf8fa2c833 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Martin Morgan [aut], Valerie Obenchain [aut], Aliyu Atiku Mustapha [ctb] (Converted 'OrganismDbi' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: devel git_last_commit: 223f33d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OrganismDbi_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OrganismDbi_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OrganismDbi_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OrganismDbi_1.49.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.html vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 105 Package: orthogene Version: 1.13.0 Depends: R (>= 4.1) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, grr, repmis, ggtree, tools Suggests: rworkflows, remotes, knitr, BiocStyle, markdown, rmarkdown, testthat (>= 3.0.0), piggyback, magick, GenomeInfoDbData, ape, phytools, rphylopic (>= 1.0.0), TreeTools, ggimage, OmaDB License: GPL-3 MD5sum: a71963eb973cb0358dee15502cd1be41 NeedsCompilation: no Title: Interspecies gene mapping Description: `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics, Transcriptomics, GeneExpression Author: Brian Schilder [cre] (ORCID: ) Maintainer: Brian Schilder URL: https://github.com/neurogenomics/orthogene VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/orthogene/issues git_url: https://git.bioconductor.org/packages/orthogene git_branch: devel git_last_commit: cd1c983 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/orthogene_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/orthogene_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/orthogene_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/orthogene_1.13.0.tgz vignettes: vignettes/orthogene/inst/doc/docker.html, vignettes/orthogene/inst/doc/infer_species.html, vignettes/orthogene/inst/doc/orthogene.html vignetteTitles: docker, Infer species, orthogene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/orthogene/inst/doc/docker.R, vignettes/orthogene/inst/doc/infer_species.R, vignettes/orthogene/inst/doc/orthogene.R importsMe: EWCE dependencyCount: 153 Package: orthos Version: 1.5.0 Depends: R (>= 4.3), SummarizedExperiment Imports: AnnotationHub, basilisk, BiocParallel, colorspace, cowplot, DelayedArray, dplyr, ExperimentHub, ggplot2, ggpubr, ggrepel, ggsci, grDevices, grid, HDF5Array, keras, methods, orthosData, parallel, plyr, reticulate, rlang, S4Vectors, stats, tensorflow, tidyr Suggests: BiocManager, BiocStyle, htmltools, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 99ae7fcc71a95598350c2b220de55f25 NeedsCompilation: no Title: `orthos` is an R package for variance decomposition using conditional variational auto-encoders Description: `orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts. biocViews: RNASeq, DifferentialExpression, GeneExpression Author: Panagiotis Papasaikas [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Panagiotis Papasaikas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/orthos git_branch: devel git_last_commit: 2d174a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/orthos_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/orthos_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/orthos_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/orthos_1.5.0.tgz vignettes: vignettes/orthos/inst/doc/orthosIntro.html vignetteTitles: 1. Introduction to orthos hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/orthos/inst/doc/orthosIntro.R dependencyCount: 156 Package: OSAT Version: 1.55.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 636c6226ebcfb56bd45541e7b6b6d81c NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: devel git_last_commit: 77f07d3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OSAT_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OSAT_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OSAT_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OSAT_1.55.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R suggestsMe: designit dependencyCount: 2 Package: Oscope Version: 1.37.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 0c7dacec4fbdf82d5870e4747f5667f0 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: devel git_last_commit: 4ad579a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Oscope_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Oscope_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Oscope_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Oscope_1.37.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R importsMe: scDDboost dependencyCount: 53 Package: OTUbase Version: 1.57.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 Archs: x64 MD5sum: ba08a023ed03b520f05579ce106622c4 NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: devel git_last_commit: 4de6774 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OTUbase_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OTUbase_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OTUbase_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OTUbase_1.57.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependencyCount: 70 Package: OUTRIDER Version: 1.25.0 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, ggrepel, grDevices, heatmaply, pheatmap, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, reshape2, S4Vectors, scales, splines, stats, txdbmaker, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr, GenomeInfoDb, ggbio, biovizBase License: MIT + file LICENSE MD5sum: d2bd205b414f05b962cb4e8d4e155d67 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut], Christian Mertes [aut, cre] (ORCID: ), Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/OUTRIDER/issues git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: devel git_last_commit: e21b7d6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OUTRIDER_1.25.0.tar.gz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R importsMe: FRASER dependencyCount: 171 Package: OutSplice Version: 1.7.0 Depends: R(>= 4.3) Imports: AnnotationDbi (>= 1.60.0), GenomicRanges (>= 1.49.0), GenomicFeatures (>= 1.50.2), IRanges (>= 2.32.0), org.Hs.eg.db (>= 3.16.0), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.16.0), S4Vectors (>= 0.36.0) Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: bc725943a2aba04b2c96c30856e8679d NeedsCompilation: no Title: Comparison of Splicing Events between Tumor and Normal Samples Description: An easy to use tool that can compare splicing events in tumor and normal tissue samples using either a user generated matrix, or data from The Cancer Genome Atlas (TCGA). This package generates a matrix of splicing outliers that are significantly over or underexpressed in tumors samples compared to normal denoted by chromosome location. The package also will calculate the splicing burden in each tumor and characterize the types of splicing events that occur. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, RNASeq, Software, VariantAnnotation Author: Joseph Bendik [aut] (ORCID: ), Sandhya Kalavacherla [aut] (ORCID: ), Michael Considine [aut] (ORCID: ), Bahman Afsari [aut] (ORCID: ), Michael F. Ochs [aut], Joseph Califano [aut] (ORCID: ), Daria A. Gaykalova [aut] (ORCID: ), Elana Fertig [aut] (ORCID: ), Theresa Guo [cre, aut] (ORCID: ) Maintainer: Theresa Guo URL: https://github.com/GuoLabUCSD/OutSplice VignetteBuilder: knitr BugReports: https://github.com/GuoLabUCSD/OutSplice/issues git_url: https://git.bioconductor.org/packages/OutSplice git_branch: devel git_last_commit: db952c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OutSplice_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OutSplice_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OutSplice_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OutSplice_1.7.0.tgz vignettes: vignettes/OutSplice/inst/doc/OutSplice.html vignetteTitles: Find Splicing Outliers in Tumor Samples with OutSplice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutSplice/inst/doc/OutSplice.R dependencyCount: 81 Package: OVESEG Version: 1.23.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 MD5sum: 915166a7f654b4fa6e079f7ad7b14bb5 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: devel git_last_commit: ec068d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/OVESEG_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/OVESEG_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/OVESEG_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/OVESEG_1.23.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 51 Package: PAA Version: 1.41.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE MD5sum: 6098d128913b6756d61e858d2d20929b NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: devel git_last_commit: e9dd570 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PAA_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAA_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PAA_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PAA_1.41.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 88 Package: packFinder Version: 1.19.0 Depends: R (>= 4.1.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 MD5sum: 8f06714849bb5ba3498d410c69e69051 NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: devel git_last_commit: 7ce4093 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/packFinder_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/packFinder_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/packFinder_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/packFinder_1.19.0.tgz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 36 Package: padma Version: 1.17.0 Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot, reshape2 License: GPL (>=3) MD5sum: 6b830ddfa63666563e192c7bc2662305 NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (ORCID: ), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: devel git_last_commit: 816cd17 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/padma_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/padma_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/padma_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/padma_1.17.0.tgz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 133 Package: PADOG Version: 1.49.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) Archs: x64 MD5sum: b42d3d3feb242d6d55fb186527714ffc NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: devel git_last_commit: 624952a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PADOG_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PADOG_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PADOG_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PADOG_1.49.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA suggestsMe: ReporterScore dependencyCount: 63 Package: pageRank Version: 1.17.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 913d55480cc276763cdf3a8e3bc2d4a4 NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: devel git_last_commit: a33df28 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pageRank_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pageRank_1.17.0.zip vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 130 Package: PAIRADISE Version: 1.23.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3ba6733408cd21c4e9093a12d9603a32 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: devel git_last_commit: 069378a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PAIRADISE_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAIRADISE_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PAIRADISE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PAIRADISE_1.23.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 48 Package: paircompviz Version: 1.45.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 1bccb1b4b17524e686c637831a989cc9 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: devel git_last_commit: 78a6dbf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/paircompviz_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/paircompviz_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/paircompviz_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/paircompviz_1.45.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 11 Package: pairkat Version: 1.13.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 MD5sum: 4325f6c96e87f495048fe6fac6007811 NeedsCompilation: no Title: PaIRKAT Description: PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. biocViews: Software, Metabolomics, KEGG, Pathways, Network, GraphAndNetwork, Regression Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut] Maintainer: Max McGrath VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: devel git_last_commit: def3ebb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/pairkat_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pairkat_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pairkat_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pairkat_1.13.0.tgz vignettes: vignettes/pairkat/inst/doc/using-pairkat.html vignetteTitles: using-pairkat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R dependencyCount: 55 Package: pandaR Version: 1.39.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: af18caf13f999ab456520a5797e71d58 NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: devel git_last_commit: 0d9c9dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pandaR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pandaR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pandaR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pandaR_1.39.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 46 Package: panelcn.mops Version: 1.29.0 Depends: R (>= 3.5.0), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: cb2152e9229ddb655680d651ceb77fce NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre] Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: devel git_last_commit: d91ac16 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/panelcn.mops_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/panelcn.mops_1.29.0.zip vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 42 Package: PanomiR Version: 1.11.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma, metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer, rlang, tibble, withr, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 8ebf4e0fd1ee13a553461e4c9f610fe6 NeedsCompilation: no Title: Detection of miRNAs that regulate interacting groups of pathways Description: PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data. biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA, Pathways Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut] Maintainer: Pourya Naderi URL: https://github.com/pouryany/PanomiR VignetteBuilder: knitr BugReports: https://github.com/pouryany/PanomiR/issues git_url: https://git.bioconductor.org/packages/PanomiR git_branch: devel git_last_commit: 747888b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PanomiR_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PanomiR_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PanomiR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PanomiR_1.11.0.tgz vignettes: vignettes/PanomiR/inst/doc/PanomiR.html vignetteTitles: PanomiR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R dependencyCount: 157 Package: panp Version: 1.77.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 49d0949c7c1ee6ddfbb2decf970d8b25 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: devel git_last_commit: 6b89b21 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/panp_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/panp_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/panp_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/panp_1.77.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 13 Package: PANR Version: 1.53.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 Archs: x64 MD5sum: a8a5312ac394f0746f8ef4763b84b50e NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: devel git_last_commit: a07182d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PANR_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PANR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PANR_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PANR_1.53.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 28 Package: parglms Version: 1.39.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 84500c1c24dd38aeceb7fd51e0a7c08d NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: devel git_last_commit: 1e7c61f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/parglms_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/parglms_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/parglms_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/parglms_1.39.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 38 Package: parody Version: 1.65.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 8ede48e4892dc3fafc4b54e6cd446c53 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: Vince Carey [aut, cre] (ORCID: ) Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: devel git_last_commit: d9f2085 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/parody_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/parody_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/parody_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/parody_1.65.0.tgz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: partCNV Version: 1.5.0 Depends: R (>= 3.5.0) Imports: stats, data.table, depmixS4, Seurat, SingleCellExperiment, AnnotationHub, magrittr, GenomicRanges, BiocStyle Suggests: rmarkdown, knitr, IRanges, testthat (>= 3.0.0) License: GPL-2 MD5sum: ebfc294b4460c1f4fe327838b919405c NeedsCompilation: no Title: Infer locally aneuploid cells using single cell RNA-seq data Description: This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH). biocViews: Software, CopyNumberVariation, HiddenMarkovModel, SingleCell, Classification Author: Ziyi Li [aut, cre, ctb], Ruoxing Li [ctb] Maintainer: Ziyi Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/partCNV git_branch: devel git_last_commit: 91c5b22 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/partCNV_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/partCNV_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/partCNV_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/partCNV_1.5.0.tgz vignettes: vignettes/partCNV/inst/doc/partCNV_vignette.html vignetteTitles: partCNV_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/partCNV/inst/doc/partCNV_vignette.R dependencyCount: 193 Package: PAST Version: 1.23.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 5f1adab9a9693847affa9c5035448570 NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: devel git_last_commit: 926ad93 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PAST_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PAST_1.23.0.zip vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 96 Package: Path2PPI Version: 1.37.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 08ee33182780a4e9b2017e40a2c50c58 NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: devel git_last_commit: 59d226c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Path2PPI_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Path2PPI_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Path2PPI_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Path2PPI_1.37.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 17 Package: pathifier Version: 1.45.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 46f4edddd7b93233e71e9af2ba12979f NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: devel git_last_commit: b298975 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pathifier_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathifier_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathifier_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathifier_1.45.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R importsMe: funOmics dependencyCount: 9 Package: pathlinkR Version: 1.3.3 Depends: R (>= 4.4.0) Imports: circlize, clusterProfiler, ComplexHeatmap, dplyr, fgsea, ggforce, ggplot2, ggpubr, ggraph, ggrepel, grid, igraph, purrr, sigora, stringr, tibble, tidygraph, tidyr, vegan, visNetwork Suggests: AnnotationDbi, BiocStyle, biomaRt, covr, DESeq2, jsonlite, knitr, org.Hs.eg.db, rmarkdown, scales, testthat (>= 3.0.0), vdiffr License: GPL-3 + file LICENSE MD5sum: 8e424cec4c6b56495701aaeef179bcff NeedsCompilation: no Title: Analyze and interpret RNA-Seq results Description: pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R. biocViews: GeneSetEnrichment, Network, Pathways, Reactome, RNASeq, NetworkEnrichment Author: Travis Blimkie [cre] (ORCID: ), Andy An [aut] Maintainer: Travis Blimkie URL: https://github.com/hancockinformatics/pathlinkR VignetteBuilder: knitr BugReports: https://github.com/hancockinformatics/pathlinkR/issues git_url: https://git.bioconductor.org/packages/pathlinkR git_branch: devel git_last_commit: ac34346 git_last_commit_date: 2024-11-20 Date/Publication: 2024-11-21 source.ver: src/contrib/pathlinkR_1.3.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathlinkR_1.3.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathlinkR_1.3.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathlinkR_1.3.3.tgz vignettes: vignettes/pathlinkR/inst/doc/pathlinkR.html vignetteTitles: Analyze and visualize RNA-Seq data with pathlinkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pathlinkR/inst/doc/pathlinkR.R dependencyCount: 189 Package: PathNet Version: 1.47.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 3f844bd364cd35018e489dfb7ac5271f NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: devel git_last_commit: 08e4aa3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PathNet_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PathNet_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PathNet_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PathNet_1.47.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: pathRender Version: 1.75.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 0cbc76fe9cdd7c646cd32db07efd931a NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: devel git_last_commit: 3e88ace git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pathRender_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathRender_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathRender_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathRender_1.75.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 51 Package: pathview Version: 1.47.0 Depends: R (>= 3.5.0) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, org.EcK12.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 9e8d5717bad51eff47f15437bf5f7b31 NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: devel git_last_commit: 87ffb7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pathview_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathview_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathview_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathview_1.47.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: EGSEA, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, TCGAWorkflow, lilikoi, SQMtools suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow, ReporterScore dependencyCount: 54 Package: pathwayPCA Version: 1.23.0 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: c9b8548305539cf09cfec2bea8f5ad22 NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: devel git_last_commit: c68c52f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pathwayPCA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pathwayPCA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pathwayPCA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pathwayPCA_1.23.0.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R dependencyCount: 12 Package: pcaExplorer Version: 3.1.1 Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: 7382d0843d09fbae858fa06aa9702cab NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: devel git_last_commit: 11c744f git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/pcaExplorer_3.1.1.tar.gz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R suggestsMe: GeDi dependencyCount: 231 Package: pcaMethods Version: 1.99.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 9a8028d07e3e47b07536447e63b27a1f NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: devel git_last_commit: 8148736 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pcaMethods_1.99.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pcaMethods_1.99.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pcaMethods_1.99.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pcaMethods_1.99.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD importsMe: consensusDE, destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde, SomaticSignatures, ADAPTS, CopSens, geneticae, lfproQC, LOST, MetabolomicsBasics, missCompare, multiDimBio, pmartR, polyRAD, promor, RAMClustR, santaR, scMappR suggestsMe: autonomics, cardelino, MsCoreUtils, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 10 Package: PCAN Version: 1.35.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 0e41d4de64ae2d667c93486dff99f455 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: devel git_last_commit: 71c4483 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PCAN_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PCAN_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PCAN_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PCAN_1.35.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 14 Package: PCAtools Version: 2.19.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown License: GPL-3 MD5sum: cc3aca422a7670cfcacc8f245832182a NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: devel git_last_commit: fb5bb35 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PCAtools_2.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PCAtools_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PCAtools_2.19.0.tgz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R importsMe: COTAN, CRISPRball, regionalpcs suggestsMe: RNAseqCovarImpute, scDataviz dependencyCount: 77 Package: PDATK Version: 1.15.0 Depends: R (>= 4.1), SummarizedExperiment Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus, igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro, CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics, survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel, rlang, piano, scales, survcomp, genefu, ggplot2, switchBox, reportROC, pROC, verification, utils Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array License: MIT + file LICENSE MD5sum: 49ffc2c1626f0f53262493ef0f4dc10b NeedsCompilation: no Title: Pancreatic Ductal Adenocarcinoma Tool-Kit Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification, Survival, Clustering, GenePrediction Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles [aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: devel git_last_commit: 6c52f5f git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-27 source.ver: src/contrib/PDATK_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PDATK_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PDATK_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PDATK_1.15.0.tgz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor, PDATK_introduction.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R, vignettes/PDATK/inst/doc/PDATK_introduction.R dependencyCount: 261 Package: pdInfoBuilder Version: 1.71.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: 7bd23b8817673b3401969e8610d7e320 NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: devel git_last_commit: 0504fdc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pdInfoBuilder_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pdInfoBuilder_1.71.0.zip vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 65 Package: PeacoQC Version: 1.17.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) MD5sum: 3fb67dc5649ee0bf34237458d98f6c0f NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: devel git_last_commit: 3449ab9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PeacoQC_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PeacoQC_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PeacoQC_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PeacoQC_1.17.0.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.pdf vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R importsMe: CytoPipeline dependencyCount: 84 Package: peakPantheR Version: 1.21.0 Depends: R (>= 4.2) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.5.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils, lubridate, svglite (>= 2.1.1) Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 7df6dc0f574354126eb32278ab88a15f NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (ORCID: ), Goncalo Correia [aut] (ORCID: ), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: devel git_last_commit: 4d6e977 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/peakPantheR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/peakPantheR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/peakPantheR_1.21.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 151 Package: PECA Version: 1.43.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 698fe50dbcb3d8de2d42260d3fc62062 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: devel git_last_commit: a4a29d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PECA_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PECA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PECA_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PECA_1.43.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 89 Package: peco Version: 1.19.0 Depends: R (>= 3.5.0) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: a8432650393b347d3fe0ef977082af7b NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: devel git_last_commit: a87c0cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/peco_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/peco_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/peco_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/peco_1.19.0.tgz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 119 Package: Pedixplorer Version: 1.3.0 Depends: R (>= 4.3.0) Imports: graphics, stats, methods, ggplot2, utils, grDevices, stringr, plyr, dplyr, tidyr, quadprog, Matrix, S4Vectors, shiny, readxl, shinyWidgets, htmlwidgets, DT, gridExtra, data.table, plotly, colourpicker, shinytoastr, scales, shinycssloaders Suggests: diffviewer, testthat (>= 3.0.0), vdiffr, rmarkdown, BiocStyle, knitr, withr, qpdf, shinytest2, covr, devtools, R.devices, usethis, magick License: Artistic-2.0 MD5sum: 1d72f0b86401255f086e3804b98a32a3 NeedsCompilation: no Title: Pedigree Functions Description: Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. biocViews: Software, DataRepresentation, Genetics, GraphAndNetwork, Visualization Author: Louis Le Nézet [aut, cre, ctb] (ORCID: ), Jason Sinnwell [aut], Terry Therneau [aut], Daniel Schaid [ctb], Elizabeth Atkinson [ctb] Maintainer: Louis Le Nézet URL: https://louislenezet.github.io/Pedixplorer/ VignetteBuilder: knitr BugReports: https://github.com/LouisLeNezet/Pedixplorer/issues git_url: https://git.bioconductor.org/packages/Pedixplorer git_branch: devel git_last_commit: e84d0d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Pedixplorer_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pedixplorer_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Pedixplorer_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Pedixplorer_1.3.0.tgz vignettes: vignettes/Pedixplorer/inst/doc/pedigree_alignment.html, vignettes/Pedixplorer/inst/doc/pedigree_kinship.html, vignettes/Pedixplorer/inst/doc/pedigree_object.html, vignettes/Pedixplorer/inst/doc/pedigree_plot.html, vignettes/Pedixplorer/inst/doc/Pedixplorer.html vignetteTitles: Pedigree alignment details, Pedigree kinship() details, Pedigree object, Pedigree plotting details, Pedixplorer tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pedixplorer/inst/doc/pedigree_alignment.R, vignettes/Pedixplorer/inst/doc/pedigree_kinship.R, vignettes/Pedixplorer/inst/doc/pedigree_object.R, vignettes/Pedixplorer/inst/doc/pedigree_plot.R, vignettes/Pedixplorer/inst/doc/Pedixplorer.R dependencyCount: 103 Package: pengls Version: 1.13.0 Depends: R (>= 4.2.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: 9c0e1a94eae933b8a586de201837ee4c NeedsCompilation: no Title: Fit Penalised Generalised Least Squares models Description: Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data. biocViews: Transcriptomics, Regression, TimeCourse, Spatial Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls git_url: https://git.bioconductor.org/packages/pengls git_branch: devel git_last_commit: 1a0f003 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pengls_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pengls_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pengls_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pengls_1.13.0.tgz vignettes: vignettes/pengls/inst/doc/penglsVignette.html vignetteTitles: Vignette of the pengls package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pengls/inst/doc/penglsVignette.R dependencyCount: 27 Package: PepSetTest Version: 1.1.0 Imports: dplyr, limma, lme4, MASS, matrixStats, reshape2, stats, tibble, SummarizedExperiment, methods Suggests: statmod, BiocStyle, knitr, rmarkdown, tidyr License: GPL (>= 3) MD5sum: 04384970b0f71ad64b08f8019d7c0043 NeedsCompilation: no Title: Peptide Set Test Description: Peptide Set Test (PepSetTest) is a peptide-centric strategy to infer differentially expressed proteins in LC-MS/MS proteomics data. This test detects coordinated changes in the expression of peptides originating from the same protein and compares these changes against the rest of the peptidome. Compared to traditional aggregation-based approaches, the peptide set test demonstrates improved statistical power, yet controlling the Type I error rate correctly in most cases. This test can be valuable for discovering novel biomarkers and prioritizing drug targets, especially when the direct application of statistical analysis to protein data fails to provide substantial insights. biocViews: DifferentialExpression, Regression, Proteomics, MassSpectrometry Author: Junmin Wang [aut, cre] Maintainer: Junmin Wang URL: https://github.com/JmWangBio/PepSetTest VignetteBuilder: knitr BugReports: https://github.com/JmWangBio/PepSetTest/issues git_url: https://git.bioconductor.org/packages/PepSetTest git_branch: devel git_last_commit: 7d3c75f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PepSetTest_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PepSetTest_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PepSetTest_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PepSetTest_1.1.0.tgz vignettes: vignettes/PepSetTest/inst/doc/PepSetTest.html vignetteTitles: A Tutorial for PepSetTest hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PepSetTest/inst/doc/PepSetTest.R dependencyCount: 67 Package: PepsNMR Version: 1.25.0 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: 1a9a4e00ce260319bcb0305d11d70d94 NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: devel git_last_commit: 1fcfe4b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PepsNMR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PepsNMR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PepsNMR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PepsNMR_1.25.0.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 46 Package: pepStat Version: 1.41.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: d9d212c1dee6c80bd62c1a700fa15ab0 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: devel git_last_commit: 42e8f77 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pepStat_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pepStat_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pepStat_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pepStat_1.41.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 64 Package: pepXMLTab Version: 1.41.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 7f25228aa3ff2f5610b1678d1f62b31c NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: devel git_last_commit: 6c9f5e2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pepXMLTab_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pepXMLTab_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pepXMLTab_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pepXMLTab_1.41.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: periodicDNA Version: 1.17.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE MD5sum: dd5471fd6c8ba8bb2648ade9a4d836c3 NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: devel git_last_commit: 75b48a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/periodicDNA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/periodicDNA_1.17.0.zip vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 88 Package: pfamAnalyzeR Version: 1.7.0 Depends: R (>= 4.3.0), readr, stringr, dplyr Imports: utils, tibble, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 61e05c3f8192fb73363aec580d3d00f3 NeedsCompilation: no Title: Identification of domain isotypes in pfam data Description: Protein domains is one of the most import annoation of proteins we have with the Pfam database/tool being (by far) the most used tool. This R package enables the user to read the pfam prediction from both webserver and stand-alone runs into R. We have recently shown most human protein domains exist as multiple distinct variants termed domain isotypes. Different domain isotypes are used in a cell, tissue, and disease-specific manner. Accordingly, we find that domain isotypes, compared to each other, modulate, or abolish the functionality of a protein domain. This R package enables the identification and classification of such domain isotypes from Pfam data. biocViews: AlternativeSplicing, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Annotation, FunctionalPrediction, GenePrediction, DataImport Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ) Maintainer: Kristoffer Vitting-Seerup VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/pfamAnalyzeR/issues git_url: https://git.bioconductor.org/packages/pfamAnalyzeR git_branch: devel git_last_commit: f426e74 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pfamAnalyzeR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pfamAnalyzeR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pfamAnalyzeR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pfamAnalyzeR_1.7.0.tgz vignettes: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.html vignetteTitles: pfamAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.R dependencyCount: 35 Package: pgca Version: 1.31.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: ea1413ca8dd1506e6274a9720c1fb075 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: devel git_last_commit: ec5b4ff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pgca_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pgca_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pgca_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pgca_1.31.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: pgxRpi Version: 1.3.0 Depends: R (>= 4.2) Imports: utils, methods, grDevices, graphics, circlize, httr, dplyr, attempt, lubridate, survival, survminer, ggplot2, GenomicRanges, SummarizedExperiment, S4Vectors, yaml, parallel, future, future.apply Suggests: BiocStyle, rmarkdown, knitr, testthat License: Artistic-2.0 MD5sum: 16e6c4632b6789d73740f03eec6ab32d NeedsCompilation: no Title: R wrapper for Progenetix Description: The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix. biocViews: CopyNumberVariation, GenomicVariation, DataImport, Software Author: Hangjia Zhao [aut, cre] (ORCID: ), Michael Baudis [aut] (ORCID: ) Maintainer: Hangjia Zhao URL: https://github.com/progenetix/pgxRpi VignetteBuilder: knitr BugReports: https://github.com/progenetix/pgxRpi/issues git_url: https://git.bioconductor.org/packages/pgxRpi git_branch: devel git_last_commit: b85b31a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/pgxRpi_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pgxRpi_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pgxRpi_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pgxRpi_1.3.0.tgz vignettes: vignettes/pgxRpi/inst/doc/Introduction_1_loadmetadata.html, vignettes/pgxRpi/inst/doc/Introduction_2_loadvariants.html, vignettes/pgxRpi/inst/doc/Introduction_3_loadfrequency.html, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.html vignetteTitles: Introduction_1_loadmetadata, Introduction_2_loadvariants, Introduction_3_loadfrequency, Introduction_4_process_pgxseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgxRpi/inst/doc/Introduction_1_loadmetadata.R, vignettes/pgxRpi/inst/doc/Introduction_2_loadvariants.R, vignettes/pgxRpi/inst/doc/Introduction_3_loadfrequency.R, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.R dependencyCount: 137 Package: phantasus Version: 1.27.0 Depends: R (>= 4.3) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, edgeR, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, data.table, curl, apeglm, tidyr, config (>= 0.3.2), rhdf5client (>= 1.25.1), yaml, fs, phantasusLite, XML Suggests: testthat, BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db License: MIT + file LICENSE MD5sum: 251ef56c68e9b599d3f5a3471ba8714a NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Maxim Kleverov [aut], Daria Zenkova [aut], Vladislav Kamenev [aut], Margarita Sablina [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://alserglab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: devel git_last_commit: 57b2ec9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phantasus_1.27.0.tar.gz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 165 Package: phantasusLite Version: 1.5.0 Depends: R (>= 4.2) Imports: data.table, rhdf5client(>= 1.25.1), httr, stringr, stats, utils, Biobase, methods Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, rhdf5, GEOquery License: MIT + file LICENSE MD5sum: fd8d806e071afd34e789ec317d6eacf2 NeedsCompilation: no Title: Loading and annotation RNA-seq counts matrices Description: PhantasusLite – a lightweight package with helper functions of general interest extracted from phantasus package. In parituclar it simplifies working with public RNA-seq datasets from GEO by providing access to the remote HSDS repository with the precomputed gene counts from ARCHS4 and DEE2 projects. biocViews: GeneExpression, Transcriptomics, RNASeq Author: Rita Sablina [aut], Maxim Kleverov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/phantasusLite/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasusLite/issues git_url: https://git.bioconductor.org/packages/phantasusLite git_branch: devel git_last_commit: 13aa8ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phantasusLite_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phantasusLite_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phantasusLite_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phantasusLite_1.5.0.tgz vignettes: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.html vignetteTitles: phantasusLite tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.R importsMe: phantasus dependencyCount: 43 Package: PharmacoGx Version: 3.11.0 Depends: R (>= 3.6), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, RColorBrewer, magicaxis, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, checkmate, boot, coop LinkingTo: Rcpp Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown, BiocStyle, R.utils License: GPL (>= 3) MD5sum: 77c099ec07fef81cbf07d6067ec38843 NeedsCompilation: yes Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Christopher Eeles [aut], Jermiah Joseph [aut], Zhaleh Safikhani [aut], Mark Freeman [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: devel git_last_commit: 18017f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PharmacoGx_3.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PharmacoGx_3.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PharmacoGx_3.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PharmacoGx_3.11.0.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.html, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.html, vignettes/PharmacoGx/inst/doc/PharmacoGx.html vignetteTitles: Creating a PharmacoSet Object, Detecting Drug Synergy and Antagonism with PharmacoGx 3.0+, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: gDRimport, Xeva suggestsMe: ToxicoGx dependencyCount: 155 Package: PhenoGeneRanker Version: 1.15.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: d67d1cb5eca340dadfaa929569d52ac2 NeedsCompilation: no Title: PhenoGeneRanker: A gene and phenotype prioritization tool Description: This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155. biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork, Network, NetworkInference, Pathways, Software, SystemsBiology Author: Cagatay Dursun [aut, cre] Maintainer: Cagatay Dursun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: devel git_last_commit: 9dbd9f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PhenoGeneRanker_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhenoGeneRanker_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhenoGeneRanker_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhenoGeneRanker_1.15.0.tgz vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html vignetteTitles: PhenoGeneRanker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R dependencyCount: 31 Package: phenomis Version: 1.9.0 Depends: SummarizedExperiment Imports: Biobase, biodb, biodbChebi, data.table, futile.logger, ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets, igraph, limma, methods, MultiAssayExperiment, MultiDataSet, PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble, tidyr, utils, VennDiagram Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 3e030601a777810746bece09d6b18892 NeedsCompilation: no Title: Postprocessing and univariate analysis of omics data Description: The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics). biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry, Metabolomics, Normalization, Proteomics, QualityControl, Sequencing, StatisticalMethod, Transcriptomics Author: Etienne A. Thevenot [aut, cre] (ORCID: ), Natacha Lenuzza [ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb], Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb] Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1038/s41597-021-01095-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenomis git_branch: devel git_last_commit: b15f3f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phenomis_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenomis_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenomis_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenomis_1.9.0.tgz vignettes: vignettes/phenomis/inst/doc/phenomis-vignette.html vignetteTitles: phenomis-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenomis/inst/doc/phenomis-vignette.R suggestsMe: ropls dependencyCount: 155 Package: phenopath Version: 1.31.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: ae11f42533ed419a97c605c801e6509d NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: devel git_last_commit: e66e4a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phenopath_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenopath_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenopath_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenopath_1.31.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 71 Package: phenoTest Version: 1.55.0 Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: ad7433b2a2569ca5236d13dfdd842412 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: devel git_last_commit: 689f101 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phenoTest_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phenoTest_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phenoTest_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phenoTest_1.55.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 146 Package: PhenStat Version: 2.43.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: c670458808ee1712479274c77d7fcc45 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: devel git_last_commit: 59cb12c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PhenStat_2.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhenStat_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhenStat_2.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhenStat_2.43.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf vignetteTitles: PhenStat Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R dependencyCount: 118 Package: philr Version: 1.33.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: ae358b71a0587942eb951bfa5aef03a1 NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman [aut, cre], Leo Lahti [ctb] (ORCID: ) Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: devel git_last_commit: 9061ebc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/philr_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/philr_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/philr_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/philr_1.33.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R dependencyCount: 64 Package: PhIPData Version: 1.15.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE MD5sum: 736836e503fa9bf41b12d19dcf0fe4a3 NeedsCompilation: no Title: Container for PhIP-Seq Experiments Description: PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: devel git_last_commit: 3dd9454 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PhIPData_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhIPData_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhIPData_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhIPData_1.15.0.tgz vignettes: vignettes/PhIPData/inst/doc/PhIPData.html vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R dependsOnMe: beer dependencyCount: 72 Package: phosphonormalizer Version: 1.31.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: e953f26c9a6c2b642be2d7dea0c7c8e9 NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: devel git_last_commit: b1d7f27 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/phosphonormalizer_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/phosphonormalizer_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phosphonormalizer_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/phosphonormalizer_1.31.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.17.0 Depends: R (>= 4.2.0) Imports: ruv, e1071, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils, SummarizedExperiment, methods, S4Vectors, BiocGenerics, ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext, stringi Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown, org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle, stringr, calibrate License: GPL-3 + file LICENSE MD5sum: 8c51a271394cc0f3a2baa7c6bafd887f NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, Proteomics Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Hani Jieun Kim [aut] Maintainer: Taiyun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: devel git_last_commit: 1e4d7a4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PhosR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhosR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhosR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhosR_1.17.0.tgz vignettes: vignettes/PhosR/inst/doc/PhosR.html vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R dependencyCount: 139 Package: PhyloProfile Version: 1.99.1 Depends: R (>= 4.4.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, dplyr, DT, energy, ExperimentHub, extrafont, fastcluster, ggplot2, gridExtra, pbapply, plotly, RColorBrewer, RCurl, Rfast, scattermore, shiny, shinyBS, shinycssloaders, shinyFiles, shinyjs, stringr, tsne, umap, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat, OmaDB License: MIT + file LICENSE MD5sum: 00e32418764eb1aba2bb7f5261d0a71a NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction, DimensionReduction Author: Vinh Tran [aut, cre] (ORCID: ), Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: devel git_last_commit: 6a63c13 git_last_commit_date: 2024-12-20 Date/Publication: 2024-12-20 source.ver: src/contrib/PhyloProfile_1.99.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/PhyloProfile_1.99.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PhyloProfile_1.99.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PhyloProfile_1.99.1.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 151 Package: piano Version: 2.23.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: 727d3e0215591903753a9c0a496a1b42 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: devel git_last_commit: f5e29d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/piano_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/piano_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/piano_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/piano_2.23.0.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx, PDATK suggestsMe: cosmosR, BloodCancerMultiOmics2017 dependencyCount: 103 Package: pickgene Version: 1.79.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 6bbda0aa1e802ed3c0edea51085e4675 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: devel git_last_commit: 681c91a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pickgene_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pickgene_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pickgene_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pickgene_1.79.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: PICS Version: 2.51.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 Archs: x64 MD5sum: b49c50d8c987aad52b362f8d3f1aaf63 NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPseq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud URL: https://github.com/SRenan/PICS VignetteBuilder: knitr BugReports: https://github.com/SRenan/PICS/issues git_url: https://git.bioconductor.org/packages/PICS git_branch: devel git_last_commit: 59be92f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PICS_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PICS_2.51.0.zip vignettes: vignettes/PICS/inst/doc/PICS.html vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING dependencyCount: 52 Package: Pigengene Version: 1.33.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.28.0) Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI, DOSE Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy License: GPL (>=2) MD5sum: 050912e566a8158668fdefa7d4e0c260 NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short, Isha Mehta, Neda Emami, and Sogand Sajedi Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: devel git_last_commit: 25bba85 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Pigengene_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pigengene_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Pigengene_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Pigengene_1.33.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R importsMe: iNETgrate dependencyCount: 185 Package: PING Version: 2.51.0 Depends: R(>= 3.5.0) Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 MD5sum: f1657891f0d0c8e351e0c7d98c2cf868 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: devel git_last_commit: 025f8b7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PING_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PING_2.51.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 174 Package: pipeComp Version: 1.17.0 Depends: R (>= 4.1) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle, rmarkdown License: GPL Archs: x64 MD5sum: 4d2a416922c1e854599a3a8e7f3d1941 NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Anthony Sonrel [aut] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ) Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: devel git_last_commit: be750ef git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pipeComp_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pipeComp_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pipeComp_1.17.0.tgz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 219 Package: pipeFrame Version: 1.23.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 78dfa5108d8ab4bec68e3689ea0e46b8 NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: devel git_last_commit: f9587ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/pipeFrame_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pipeFrame_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pipeFrame_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pipeFrame_1.23.0.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: esATAC dependencyCount: 85 Package: PIPETS Version: 1.3.0 Depends: R (>= 4.4.0) Imports: dplyr, utils, stats, GenomicRanges, BiocGenerics, methods Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: b62e90e81f9d8dfe85e90174e1923a57 NeedsCompilation: no Title: Poisson Identification of PEaks from Term-Seq data Description: PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks. biocViews: Sequencing, Transcription, GeneRegulation, PeakDetection, Genetics, Transcriptomics, Coverage Author: Quinlan Furumo [aut, cre] (ORCID: ) Maintainer: Quinlan Furumo URL: https://github.com/qfurumo/PIPETS VignetteBuilder: knitr BugReports: https://github.com/qfurumo/PIPETS/issues git_url: https://git.bioconductor.org/packages/PIPETS git_branch: devel git_last_commit: 611f676 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PIPETS_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PIPETS_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PIPETS_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PIPETS_1.3.0.tgz vignettes: vignettes/PIPETS/inst/doc/PIPETS.html vignetteTitles: PIPETS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PIPETS/inst/doc/PIPETS.R dependencyCount: 39 Package: Pirat Version: 1.1.0 Depends: R (>= 4.4.0) Imports: basilisk, reticulate, progress, ggplot2, MASS, invgamma, grDevices, stats, graphics, SummarizedExperiment, S4Vectors Suggests: knitr, BiocStyle License: GPL-2 MD5sum: e553be7913a5dec9229358fbecae7d97 NeedsCompilation: no Title: Precursor or Peptide Imputation under Random Truncation Description: Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements. biocViews: Proteomics, MassSpectrometry, Preprocessing, Software Author: Lucas Etourneau [aut], Laura Fancello [aut], Samuel Wieczorek [cre, aut] (ORCID: ), Nelle Varoquaux [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/Pirat/issues git_url: https://git.bioconductor.org/packages/Pirat git_branch: devel git_last_commit: d00d343 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Pirat_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pirat_1.1.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Pirat_1.1.0.tgz vignettes: vignettes/Pirat/inst/doc/Pirat.html vignetteTitles: Pirat-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pirat/inst/doc/Pirat.R dependencyCount: 79 Package: PIUMA Version: 1.3.0 Depends: R (>= 4.3), ggplot2 Imports: cluster, umap, tsne, kernlab, vegan, dbscan, igraph, scales, Hmisc, patchwork, grDevices, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-3 + file LICENSE MD5sum: 66ddb6542f3cedc09413f19990d74783 NeedsCompilation: no Title: Phenotypes Identification Using Mapper from topological data Analysis Description: The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data. biocViews: Clustering, GraphAndNetwork, DimensionReduction, Network, Classification Author: Mattia Chiesa [aut, cre] (ORCID: ), Arianna Dagliati [aut] (ORCID: ), Alessia Gerbasi [aut] (ORCID: ), Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini [aut] (ORCID: ) Maintainer: Mattia Chiesa URL: https://github.com/BioinfoMonzino/PIUMA VignetteBuilder: knitr BugReports: https://github.com/BioinfoMonzino/PIUMA/issues git_url: https://git.bioconductor.org/packages/PIUMA git_branch: devel git_last_commit: 223a516 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PIUMA_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PIUMA_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PIUMA_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PIUMA_1.3.0.tgz vignettes: vignettes/PIUMA/inst/doc/PIUMA_vignette.html vignetteTitles: PIUMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PIUMA/inst/doc/PIUMA_vignette.R dependencyCount: 115 Package: planet Version: 1.15.0 Depends: R (>= 4.3) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: de5f658379c638846c5722771a890267 NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to predict biological variables to from placnetal DNA methylation data generated from infinium arrays. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. biocViews: Software, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, DNAMethylation, CpGIsland Author: Victor Yuan [aut, cre], Wendy P. Robinson [ctb] Maintainer: Victor Yuan URL: https://victor.rbind.io/planet, http://github.com/wvictor14/planet VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: devel git_last_commit: e49c969 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/planet_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/planet_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/planet_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/planet_1.15.0.tgz vignettes: vignettes/planet/inst/doc/planet.html vignetteTitles: planet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planet/inst/doc/planet.R importsMe: methylclock suggestsMe: eoPredData dependencyCount: 20 Package: planttfhunter Version: 1.7.0 Depends: R (>= 4.2.0) Imports: Biostrings, SummarizedExperiment, utils, methods Suggests: BiocStyle, covr, sessioninfo, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 95f64f35cbe8f43c06bb0412c0f0324f NeedsCompilation: no Title: Identification and classification of plant transcription factors Description: planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies. biocViews: Software, Transcription, FunctionalPrediction, GenomeAnnotation, FunctionalGenomics, HiddenMarkovModel, Sequencing, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/planttfhunter SystemRequirements: HMMER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/planttfhunter git_url: https://git.bioconductor.org/packages/planttfhunter git_branch: devel git_last_commit: b3714d5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/planttfhunter_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/planttfhunter_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/planttfhunter_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/planttfhunter_1.7.0.tgz vignettes: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.html vignetteTitles: Genome-wide identification and classification of transcription factors in plant genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.R dependencyCount: 38 Package: plasmut Version: 1.5.0 Depends: R (>= 4.3.0) Imports: tibble, stats, dplyr Suggests: knitr, rmarkdown, tidyverse, ggrepel, magrittr, qpdf, BiocStyle, biocViews, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 39e0441b62e666be7c8ff0d024314f35 NeedsCompilation: no Title: Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic Description: A Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin. biocViews: Bayesian, SomaticMutation, GermlineMutation, Sequencing Author: Adith Arun [aut, cre], Robert Scharpf [aut] Maintainer: Adith Arun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/plasmut git_branch: devel git_last_commit: 129bd1d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plasmut_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plasmut_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plasmut_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plasmut_1.5.0.tgz vignettes: vignettes/plasmut/inst/doc/plasmut.html vignetteTitles: Modeling the origin of mutations in a liquid biopsy: cancer or clonal hematopoiesis? hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plasmut/inst/doc/plasmut.R dependencyCount: 21 Package: plgem Version: 1.79.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: abbbf59b5ea8ba40f0ac9db31d87054c NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: devel git_last_commit: 4123c5b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plgem_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plgem_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plgem_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plgem_1.79.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 9 Package: plier Version: 1.77.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: 9193f6732b7c7b8addfe830fcababd32 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: devel git_last_commit: f4e9849 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plier_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plier_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plier_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plier_1.77.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 13 Package: plotgardener Version: 1.13.0 Depends: R (>= 4.1.0) Imports: curl, data.table, dplyr, GenomeInfoDb, GenomicRanges, glue, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rhdf5, rlang, stats, strawr, tools, utils, withr LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, pdftools, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene License: MIT + file LICENSE MD5sum: 69bb01123c35ec6a4f6947bf3eb01465 NeedsCompilation: yes Title: Coordinate-Based Genomic Visualization Package for R Description: Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. biocViews: Visualization, GenomeAnnotation, FunctionalGenomics, GenomeAssembly, HiC Author: Nicole Kramer [aut, cre], Eric S. Davis [aut], Craig Wenger [aut], Sarah Parker [ctb], Erika Deoudes [art], Michael Love [ctb], Douglas H. Phanstiel [aut, cre, cph] Maintainer: Nicole Kramer , Douglas Phanstiel URL: https://phanstiellab.github.io/plotgardener, https://github.com/PhanstielLab/plotgardener VignetteBuilder: knitr BugReports: https://github.com/PhanstielLab/plotgardener/issues git_url: https://git.bioconductor.org/packages/plotgardener git_branch: devel git_last_commit: bc6f006 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plotgardener_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plotgardener_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plotgardener_1.13.0.tgz vignettes: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html vignetteTitles: Introduction to plotgardener hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R importsMe: DegCre, mariner, Ularcirc suggestsMe: nullranges dependencyCount: 100 Package: plotGrouper Version: 1.25.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: adf99f4b400e11c66ba6cc495b60bd90 NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: devel git_last_commit: 6987e2a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plotGrouper_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plotGrouper_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plotGrouper_1.25.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 133 Package: PLPE Version: 1.67.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: c4145d9517d53e227f50c2c4be01a6bf NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: devel git_last_commit: 014198f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PLPE_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PLPE_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PLPE_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PLPE_1.67.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 10 Package: PLSDAbatch Version: 1.3.0 Depends: R (>= 4.3.0) Imports: mixOmics, scales, Rdpack, ggplot2, gridExtra, ggpubr, lmerTest, performance, grid, stats, pheatmap, vegan, Biobase, BiocStyle, TreeSummarizedExperiment Suggests: knitr, rmarkdown, testthat, badger License: GPL-3 Archs: x64 MD5sum: 5904dd74ec8c6f1632c6bbcca6d3a371 NeedsCompilation: no Title: PLSDA-batch Description: A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings. biocViews: StatisticalMethod, DimensionReduction, PrincipalComponent, Classification, Microbiome, BatchEffect, Normalization, Visualization Author: Yiwen (Eva) Wang [aut, cre] (ORCID: ), Kim-Anh Le Cao [aut] Maintainer: Yiwen (Eva) Wang URL: https://github.com/EvaYiwenWang/PLSDAbatch VignetteBuilder: knitr BugReports: https://github.com/EvaYiwenWang/PLSDAbatch/issues/ git_url: https://git.bioconductor.org/packages/PLSDAbatch git_branch: devel git_last_commit: 758afa3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PLSDAbatch_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PLSDAbatch_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PLSDAbatch_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PLSDAbatch_1.3.0.tgz vignettes: vignettes/PLSDAbatch/inst/doc/brief_vignette.html vignetteTitles: PLSDA-batch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLSDAbatch/inst/doc/brief_vignette.R dependencyCount: 163 Package: plyinteractions Version: 1.5.0 Depends: R (>= 4.3.0) Imports: InteractionSet, GenomeInfoDb, BiocGenerics, GenomicRanges, plyranges, IRanges, S4Vectors, rlang, dplyr, tibble, tidyselect, methods, utils Suggests: tidyverse, BSgenome.Mmusculus.UCSC.mm10, Biostrings, BiocParallel, scales, HiContactsData, rtracklayer, BiocStyle, covr, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), RefManageR License: Artistic-2.0 MD5sum: fd2780d22463a7397c06e7ebf4790daa NeedsCompilation: no Title: Extending tidy verbs to genomic interactions Description: Operate on `GInteractions` objects as tabular data using `dplyr`-like verbs. The functions and methods in `plyinteractions` provide a grammatical approach to manipulate `GInteractions`, to facilitate their integration in genomic analysis workflows. biocViews: Software, Infrastructure Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/plyinteractions VignetteBuilder: knitr BugReports: https://github.com/js2264/plyinteractions/issues git_url: https://git.bioconductor.org/packages/plyinteractions git_branch: devel git_last_commit: ac2f117 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plyinteractions_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyinteractions_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plyinteractions_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plyinteractions_1.5.0.tgz vignettes: vignettes/plyinteractions/inst/doc/plyinteractions.html, vignettes/plyinteractions/inst/doc/process_pairs.html vignetteTitles: plyinteractions, HiCarithmetic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyinteractions/inst/doc/plyinteractions.R, vignettes/plyinteractions/inst/doc/process_pairs.R importsMe: OHCA dependencyCount: 76 Package: plyranges Version: 1.27.0 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: 1b2883ffbc37ad7e9a239cbef8039da0 NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut] (ORCID: ), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] (ORCID: ), Pierre-Paul Axisa [ctb], Michael Love [ctb, cre] Maintainer: Michael Love VignetteBuilder: knitr BugReports: https://github.com/tidyomics/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: devel git_last_commit: b1cbb5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plyranges_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyranges_1.27.0.zip vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BOBaFIT, BUSpaRse, cfDNAPro, Damsel, GenomicPlot, InPAS, katdetectr, mariner, methylCC, multicrispr, nearBynding, nullranges, plotgardener, plyinteractions, SARC, SingleMoleculeFootprinting, tidyomics, fluentGenomics, MOCHA suggestsMe: EpiCompare, extraChIPs, memes, svaNUMT, svaRetro, tidyCoverage, CTCF dependencyCount: 73 Package: plyxp Version: 1.1.0 Depends: R (>= 4.4.0) Imports: dplyr, purrr, rlang, SummarizedExperiment, tidyr, tidyselect, vctrs, tibble, pillar, cli, glue, S7, S4Vectors, utils, methods Suggests: devtools, knitr, rmarkdown, testthat, airway, IRanges, here License: MIT + file LICENSE MD5sum: 7d1e304690c2214ba3cb9415e7bb6671 NeedsCompilation: no Title: Data masks for SummarizedExperiment enabling dplyr-like manipulation Description: The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame. biocViews: Annotation, GenomeAnnotation, Transcriptomics Author: Justin Landis [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ) Maintainer: Justin Landis URL: https://github.com/jtlandis/plyxp, https://jtlandis.github.io/plyxp VignetteBuilder: knitr BugReports: https://www.github.com/jtlandis/plyxp/issues git_url: https://git.bioconductor.org/packages/plyxp git_branch: devel git_last_commit: 90c53d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/plyxp_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/plyxp_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/plyxp_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/plyxp_1.1.0.tgz vignettes: vignettes/plyxp/inst/doc/plyxp.html vignetteTitles: plyxp Usage Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plyxp/inst/doc/plyxp.R dependencyCount: 57 Package: pmm Version: 1.39.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: e2270507c716d3e4785926617dc26ff0 NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: devel git_last_commit: 0b45c51 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pmm_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pmm_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pmm_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pmm_1.39.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 18 Package: pmp Version: 1.19.0 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: 145f0039af37615c079899701089144f NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: devel git_last_commit: 7b8d496 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pmp_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pmp_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pmp_1.19.0.tgz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R suggestsMe: metabolomicsWorkbenchR, structToolbox dependencyCount: 80 Package: PoDCall Version: 1.15.1 Depends: R (>= 4.5) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: a5236ca04d384a2e70b63c32864d5df6 NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from 'QX Manager or QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall. biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation, CpGIsland, DNAMethylation, Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: devel git_last_commit: a74573a git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/PoDCall_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/PoDCall_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PoDCall_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PoDCall_1.15.1.tgz vignettes: vignettes/PoDCall/inst/doc/PoDCall.html vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation ddPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R dependencyCount: 91 Package: podkat Version: 1.39.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats (>= 4.3.0), graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) MD5sum: 01c5b204f945fe381aa8a79e6fafd662 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer [aut,cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: devel git_last_commit: ff139de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/podkat_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/podkat_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/podkat_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/podkat_1.39.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 61 Package: pogos Version: 1.27.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: ae9ade175e1e50290abb8da64e125d80 NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: devel git_last_commit: 753d9ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pogos_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pogos_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pogos_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pogos_1.27.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R dependencyCount: 136 Package: PolySTest Version: 1.1.0 Depends: R (>= 4.4.0) Imports: fdrtool (>= 1.2.15), limma (>= 3.61.3), matrixStats (>= 0.57.0), qvalue (>= 2.22.0), shiny (>= 1.5.0), SummarizedExperiment (>= 1.20.0), knitr (>= 1.33), plotly (>= 4.9.4), heatmaply (>= 1.1.1), circlize (>= 0.4.12), UpSetR (>= 1.4.0), gplots (>= 3.1.1), S4Vectors (>= 0.30.0), parallel (>= 4.1.0), grDevices (>= 4.1.0), graphics (>= 4.1.0), stats (>= 4.1.0), utils (>= 4.1.0) Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: 46542d80032d03380a3fd9f3f30e3f0b NeedsCompilation: no Title: PolySTest: Detection of differentially regulated features. Combined statistical testing for data with few replicates and missing values Description: The complexity of high-throughput quantitative omics experiments often leads to low replicates numbers and many missing values. We implemented a new test to simultaneously consider missing values and quantitative changes, which we combined with well-performing statistical tests for high confidence detection of differentially regulated features. The package contains functions to run the test and to visualize the results. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression Author: Veit Schwämmle [aut, cre] (ORCID: ) Maintainer: Veit Schwämmle URL: https://github.com/computproteomics/PolySTest VignetteBuilder: knitr BugReports: https://github.com/computproteomics/PolySTest/issues git_url: https://git.bioconductor.org/packages/PolySTest git_branch: devel git_last_commit: 43fe068 git_last_commit_date: 2024-11-22 Date/Publication: 2024-11-22 source.ver: src/contrib/PolySTest_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PolySTest_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PolySTest_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PolySTest_1.1.0.tgz vignettes: vignettes/PolySTest/inst/doc/StatisticalTest.html vignetteTitles: PolySTest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PolySTest/inst/doc/StatisticalTest.R dependencyCount: 141 Package: POMA Version: 1.17.6 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, fgsea, FSA, ggcorrplot, ggplot2, ggrepel, glmnet, grid, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, multcomp, msigdbr, purrr, randomForest, RankProd (>= 3.14), rlang, SummarizedExperiment, sva, tibble, tidyr, utils, uwot, vegan Suggests: BiocStyle, covr, ggraph, ggtext, knitr, patchwork, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 0f8cdd096c650e05bb1cb6b130ad6372 NeedsCompilation: no Title: Tools for Omics Data Analysis Description: The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) for more details. biocViews: BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, Regression, RNASeq, Software, StatisticalMethod, Visualization Author: Pol Castellano-Escuder [aut, cre] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: devel git_last_commit: 09bb015 git_last_commit_date: 2024-11-26 Date/Publication: 2024-11-29 source.ver: src/contrib/POMA_1.17.6.tar.gz vignettes: vignettes/POMA/inst/doc/POMA-normalization.html, vignettes/POMA/inst/doc/POMA-workflow.html vignetteTitles: Normalization Methods, Get Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-normalization.R, vignettes/POMA/inst/doc/POMA-workflow.R importsMe: PRONE suggestsMe: fobitools dependencyCount: 228 Package: powerTCR Version: 1.27.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 46d05d3c988bf7c7a5cdb8cbf17a3bc3 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: devel git_last_commit: 2bb8d0f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/powerTCR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/powerTCR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/powerTCR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/powerTCR_1.27.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R dependencyCount: 36 Package: POWSC Version: 1.15.0 Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST Imports: pheatmap, ggplot2, RColorBrewer, grDevices, SummarizedExperiment, limma Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: a8cd43bbcb478fa51243052d481aab73 NeedsCompilation: no Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: devel git_last_commit: ce35b26 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/POWSC_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/POWSC_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/POWSC_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/POWSC_1.15.0.tgz vignettes: vignettes/POWSC/inst/doc/POWSC.html vignetteTitles: The POWSC User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POWSC/inst/doc/POWSC.R dependencyCount: 78 Package: ppcseq Version: 1.15.0 Depends: R (>= 4.1.0), rstan (>= 2.18.1) Imports: benchmarkme, dplyr, edgeR, foreach, ggplot2, graphics, lifecycle, magrittr, methods, parallel, purrr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rlang, rstantools (>= 2.1.1), stats, tibble, tidybayes, tidyr (>= 0.8.3.9000), utils LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 Archs: x64 MD5sum: 382847209aa36d954475bc8f93ca0d28 NeedsCompilation: yes Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models Description: Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers. biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/ppcseq SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: devel git_last_commit: 403820a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ppcseq_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ppcseq_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ppcseq_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ppcseq_1.15.0.tgz vignettes: vignettes/ppcseq/inst/doc/introduction.html vignetteTitles: Overview of the ppcseq package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppcseq/inst/doc/introduction.R dependencyCount: 94 Package: PPInfer Version: 1.33.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: aa4af44aa93dd2991cb66c45cb74faeb NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: devel git_last_commit: d4d24bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PPInfer_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PPInfer_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PPInfer_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PPInfer_1.33.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 117 Package: pqsfinder Version: 2.23.0 Depends: R (>= 3.5.0), Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.78.0) Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE MD5sum: 0cbb6128c492db712c55c9630d778d0f NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: devel git_last_commit: acbc114 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pqsfinder_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pqsfinder_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pqsfinder_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pqsfinder_2.23.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 29 Package: pram Version: 1.23.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0), GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: 765ad2106222d172507d6ed779f7c844 NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq, BiologicalQuestion, GenePrediction, GenomeAnnotation, ResearchField, Transcriptomics Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: devel git_last_commit: a4a7d7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pram_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pram_1.23.0.zip vignettes: vignettes/pram/inst/doc/pram.html vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 60 Package: preciseTAD Version: 1.17.0 Depends: R (>= 4.1) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, gtools, rCGH Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: ce54edc02fd0738c76318d3cf8feb551 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: devel git_last_commit: 8d055cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/preciseTAD_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/preciseTAD_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/preciseTAD_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/preciseTAD_1.17.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 179 Package: PREDA Version: 1.53.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 51faf9b4e46d160c22b03c2c0a6f9411 NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: devel git_last_commit: 431f3f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PREDA_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PREDA_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PREDA_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PREDA_1.53.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 58 Package: preprocessCore Version: 1.69.0 Imports: stats License: LGPL (>= 2) MD5sum: 67dffc12c57539b7af1c55ed1f402b4c NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: devel git_last_commit: d2e67f1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/preprocessCore_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/preprocessCore_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/preprocessCore_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/preprocessCore_1.69.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, crlmm importsMe: affy, BloodGen3Module, bnbc, cn.farms, cypress, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PanomiR, PECA, PhosR, Pigengene, PRONE, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MiDA, noise, noisyr, oncoPredict, retriever, SMDIC, WGCNA suggestsMe: DAPAR, MsCoreUtils, multiClust, QFeatures, roastgsa, scp, splatter, tidytof, wateRmelon, aroma.affymetrix, aroma.core, glycanr, SCdeconR, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.25.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: abb8f9ca0b4f7415339dc9863e3154c8 NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: devel git_last_commit: dcff06d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/primirTSS_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/primirTSS_1.25.0.zip vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 196 Package: PrInCE Version: 1.23.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 1f79ec02bb0f7bc1a40dbc292c22c7c5 NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: devel git_last_commit: 92c3d03 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PrInCE_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PrInCE_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PrInCE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PrInCE_1.23.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 175 Package: proActiv Version: 1.17.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang, scales, S4Vectors, SummarizedExperiment, stats, tibble, txdbmaker Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: 77db3f2e44f6cce3f3c995ca7e9f9230 NeedsCompilation: no Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (ORCID: ), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: devel git_last_commit: f0775da git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/proActiv_1.17.0.tar.gz vignettes: vignettes/proActiv/inst/doc/proActiv.html vignetteTitles: Identifying Active and Alternative Promoters from RNA-Seq data with proActiv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/proActiv/inst/doc/proActiv.R dependencyCount: 125 Package: proBAMr Version: 1.41.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, txdbmaker, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: e60f0517e79ed7e8f95b3b997d570a7d NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: devel git_last_commit: 911ce04 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/proBAMr_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proBAMr_1.41.0.zip vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 102 Package: PROcess Version: 1.83.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 6aacc35c850924a9938ec4369469ebfc NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: devel git_last_commit: abf0d44 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PROcess_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROcess_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROcess_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROcess_1.83.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.35.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 42e2e19a611452dfcb35ad61fd4e8aef NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer [aut,cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: devel git_last_commit: 57dcf7b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/procoil_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/procoil_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/procoil_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/procoil_2.35.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 38 Package: proDA Version: 1.21.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 3bca55e262bdd78c44e90af360115493 NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Simon Anders [ths] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: devel git_last_commit: 217e7c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/proDA_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proDA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proDA_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proDA_1.21.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R importsMe: MatrixQCvis suggestsMe: protti dependencyCount: 39 Package: profileplyr Version: 1.23.0 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices, rlang, tiff, Rsamtools Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Cairo License: GPL (>= 3) MD5sum: c72c8b046fb887b7d65a1403617f6eeb NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: devel git_last_commit: 580fe41 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/profileplyr_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/profileplyr_1.23.0.zip vignettes: vignettes/profileplyr/inst/doc/profileplyr.html vignetteTitles: Visualization and annotation of read signal over genomic ranges with profileplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R suggestsMe: DiffBind dependencyCount: 206 Package: profileScoreDist Version: 1.35.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: x64 MD5sum: 57672ed4ebe5320940497f1f278bf60e NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: devel git_last_commit: f562e3f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/profileScoreDist_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/profileScoreDist_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/profileScoreDist_1.35.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 7 Package: progeny Version: 1.29.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra, decoupleR, reshape2 Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: ef34f73577f32f56e639edbd94662f35 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (ORCID: ), Christian H. Holland [ctb] (ORCID: ), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: devel git_last_commit: 22fe492 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/progeny_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/progeny_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/progeny_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/progeny_1.29.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: PROGENy pathway signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R importsMe: easier suggestsMe: autonomics dependencyCount: 64 Package: projectR Version: 1.23.0 Depends: R (>= 4.0.0) Imports: methods, cluster, stats, limma, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, fgsea, reshape2, viridis, scales, Matrix, MatrixModels, msigdbr, ggplot2, cowplot, ggrepel, umap, tsne Suggests: BiocStyle, CoGAPS, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap, gplots, SeuratObject License: GPL (==2) Archs: x64 MD5sum: 465358714c25c92f66a1b00378a77cff NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: devel git_last_commit: 6cc9b37 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/projectR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/projectR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/projectR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/projectR_1.23.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.html vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 102 Package: pRoloc Version: 1.47.1 Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.43.2), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.41.0), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick License: GPL-2 MD5sum: 41e5754efc5d3c5daca38cefc86fe698 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: devel git_last_commit: 11d5560 git_last_commit_date: 2024-11-23 Date/Publication: 2024-11-24 source.ver: src/contrib/pRoloc_1.47.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/pRoloc_1.47.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pRoloc_1.47.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pRoloc_1.47.1.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, Annotating spatial proteomics data, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: bandle, pRolocGUI suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 229 Package: pRolocGUI Version: 2.17.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, stats, grDevices, grid, BiocGenerics, shinydashboard Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: 639d599de1a6e98701f23bec3f33de2b NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut, cre] (ORCID: ), Thomas Naake [aut], Laurent Gatto [aut] (ORCID: ) Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRolocGUI VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: devel git_last_commit: e4978d7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pRolocGUI_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pRolocGUI_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pRolocGUI_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pRolocGUI_2.17.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 242 Package: PROMISE Version: 1.59.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: fe0812324d1bf3e89cf1caec04dde0eb NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: devel git_last_commit: 7962d03 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PROMISE_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROMISE_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROMISE_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROMISE_1.59.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 51 Package: PRONE Version: 1.1.0 Depends: R (>= 4.4.0), SummarizedExperiment Imports: dplyr, magrittr, data.table, RColorBrewer, ggplot2, S4Vectors, ComplexHeatmap, stringr, NormalyzerDE, tibble, limma, MASS, edgeR, matrixStats, preprocessCore, stats, gtools, methods, ROTS, ComplexUpset, tidyr, purrr, circlize, gprofiler2, plotROC, MSnbase, UpSetR, dendsort, vsn, Biobase, reshape2, POMA, ggtext, scales, DEqMS Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT License: GPL (>= 3) MD5sum: 486c01e08876c3a98b7bebecdac126aa NeedsCompilation: no Title: The PROteomics Normalization Evaluator Description: High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed. biocViews: Proteomics, Preprocessing, Normalization, DifferentialExpression, Visualization Author: Lis Arend [aut, cre] (ORCID: ) Maintainer: Lis Arend URL: https://github.com/lisiarend/PRONE VignetteBuilder: knitr BugReports: https://github.com/lisiarend/PRONE/issues git_url: https://git.bioconductor.org/packages/PRONE git_branch: devel git_last_commit: fd54f01 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PRONE_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PRONE_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PRONE_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PRONE_1.1.0.tgz vignettes: vignettes/PRONE/inst/doc/Differential_Expression.html, vignettes/PRONE/inst/doc/Imputation.html, vignettes/PRONE/inst/doc/Normalization.html, vignettes/PRONE/inst/doc/Preprocessing.html, vignettes/PRONE/inst/doc/PRONE.html, vignettes/PRONE/inst/doc/Spike_In_Data.html vignetteTitles: 5. Differential Expression Analysis, 4. Imputation, 3. Normalization, 2. Preprocessing, 1. Getting started with PRONE, 6. PRONE with Spike-In Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PRONE/inst/doc/Differential_Expression.R, vignettes/PRONE/inst/doc/Imputation.R, vignettes/PRONE/inst/doc/Normalization.R, vignettes/PRONE/inst/doc/Preprocessing.R, vignettes/PRONE/inst/doc/PRONE.R, vignettes/PRONE/inst/doc/Spike_In_Data.R dependencyCount: 280 Package: PROPER Version: 1.39.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL Archs: x64 MD5sum: c964bd3a2f3d364a914b15dfa6ee69ed NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: devel git_last_commit: 57f641a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PROPER_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROPER_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROPER_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROPER_1.39.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R importsMe: cypress dependencyCount: 11 Package: PROPS Version: 1.29.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: f4f9d580fa9e71316b91aa092bdb1004 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: devel git_last_commit: 0490c20 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-27 source.ver: src/contrib/PROPS_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PROPS_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PROPS_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PROPS_1.29.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 80 Package: Prostar Version: 1.39.0 Depends: R (>= 4.4.0) Imports: DAPAR (>= 1.35.1), DAPARdata (>= 1.30.0), rhandsontable, data.table, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, shinythemes, later, shinycssloaders, future, promises, shinyjqui, tibble, ggplot2, gplots, shinyjs, vioplot, Biobase, DT, R.utils, RColorBrewer, XML, colourpicker, gtools, markdown, rclipboard, sass, shinyTree, shinyWidgets Suggests: BiocStyle, BiocManager, testthat, knitr License: Artistic-2.0 MD5sum: acac777655eb0c2e2c802244e693ebfc NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, Software, GUI Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek [cre, aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: devel git_last_commit: 5e60bdd git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/Prostar_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Prostar_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Prostar_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Prostar_1.39.0.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.html vignetteTitles: Prostar User Manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R dependencyCount: 188 Package: proteinProfiles Version: 1.47.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 43c93507ef65d5ee526becd1b101b5cb NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: devel git_last_commit: 7292bf6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/proteinProfiles_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/proteinProfiles_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/proteinProfiles_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/proteinProfiles_1.47.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteoDisco Version: 1.13.0 Depends: R (>= 4.1.0), Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings (>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr (>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>= 1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0), methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6), rlang (>= 0.4.11), S4Vectors (>= 0.30.0), tibble (>= 3.1.2), tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>= 0.32.0), Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1), DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33), matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>= 3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>= 1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>= 1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>= 1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2) License: GPL-3 MD5sum: 3ff14a333982a899931da030ef695100 NeedsCompilation: no Title: Generation of customized protein variant databases from genomic variants, splice-junctions and manual sequences Description: ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings. biocViews: Software, Proteomics, RNASeq, SNP, Sequencing, VariantAnnotation, DataImport Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de Werken [ths] Maintainer: Job van Riet URL: https://github.com/ErasmusMC-CCBC/ProteoDisco VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: devel git_last_commit: 896da28 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ProteoDisco_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProteoDisco_1.13.0.zip vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html vignetteTitles: Overview_Proteodisco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R dependencyCount: 99 Package: ProteoMM Version: 1.25.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: b96b2bb4857aa4c65545fdd66ac728ff NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: devel git_last_commit: 19b533b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ProteoMM_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProteoMM_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ProteoMM_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ProteoMM_1.25.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R suggestsMe: mi4p dependencyCount: 91 Package: protGear Version: 1.11.0 Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>= 3.54.0) Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) , tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>= 2.4.0) Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) , ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>= 1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) , shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles (>= 0.9.1) , shinyFeedback (>= 0.3.0) License: GPL-3 MD5sum: 269870311b841582528ebec7f656cfe5 NeedsCompilation: no Title: Protein Micro Array Data Management and Interactive Visualization Description: A generic three-step pre-processing package for protein microarray data. This package contains different data pre-processing procedures to allow comparison of their performance.These steps are background correction, the coefficient of variation (CV) based filtering, batch correction and normalization. biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni [ctb] Maintainer: Kennedy Mwai URL: https://github.com/Keniajin/protGear VignetteBuilder: knitr BugReports: https://github.com/Keniajin/protGear/issues git_url: https://git.bioconductor.org/packages/protGear git_branch: devel git_last_commit: 97e8c52 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/protGear_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/protGear_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/protGear_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/protGear_1.11.0.tgz vignettes: vignettes/protGear/inst/doc/vignette.html vignetteTitles: protGear hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/protGear/inst/doc/vignette.R dependencyCount: 188 Package: ProtGenerics Version: 1.39.1 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 83db07b173eec65be964bf07f0aab092 NeedsCompilation: no Title: Generic infrastructure for Bioconductor mass spectrometry packages Description: S4 generic functions and classes needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: devel git_last_commit: 95f0f5d git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/ProtGenerics_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ProtGenerics_1.39.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ProtGenerics_1.39.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ProtGenerics_1.39.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MsExperiment, MSnbase, SpectraQL, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnID, MsQuality, mzID, mzR, PSMatch, QFeatures, Spectra, xcms dependencyCount: 1 Package: psichomics Version: 1.33.0 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE Archs: x64 MD5sum: 0ff34f114e25954b958b0a181bcd8454 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (ORCID: ), Nuno Luís Barbosa-Morais [aut, led, ths] (ORCID: ), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: devel git_last_commit: 55e72bb git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-13 source.ver: src/contrib/psichomics_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/psichomics_1.33.0.zip vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 210 Package: PSMatch Version: 1.11.0 Depends: S4Vectors Imports: utils, stats, igraph, methods, Matrix, BiocParallel, BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils Suggests: msdata, rpx, mzID, mzR, Spectra, SummarizedExperiment, BiocStyle, rmarkdown, knitr, factoextra, testthat License: Artistic-2.0 MD5sum: b54aaf81037c7ee6841d382974f940e8 NeedsCompilation: no Title: Handling and Managing Peptide Spectrum Matches Description: The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Samuel Wieczorek [ctb], Thomas Burger [ctb] Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/PSM VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PSM/issues git_url: https://git.bioconductor.org/packages/PSMatch git_branch: devel git_last_commit: e8f78a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PSMatch_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PSMatch_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PSMatch_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PSMatch_1.11.0.tgz vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html, vignettes/PSMatch/inst/doc/Fragments.html, vignettes/PSMatch/inst/doc/PSM.html vignetteTitles: Understanding protein groups with adjacency matrices, MS2 fragment ions, Working with PSM data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R, vignettes/PSMatch/inst/doc/Fragments.R, vignettes/PSMatch/inst/doc/PSM.R importsMe: MSnbase, omXplore, topdownr suggestsMe: MsDataHub dependencyCount: 117 Package: ptairMS Version: 1.15.0 Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat, foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra, Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5, rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData, ropls License: GPL-3 MD5sum: 8ff7a8ef9e447ce26604f01d75893990 NeedsCompilation: yes Title: Pre-processing PTR-TOF-MS Data Description: This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a singl VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: devel git_last_commit: 70fcc78 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ptairMS_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ptairMS_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ptairMS_1.15.0.tgz vignettes: vignettes/ptairMS/inst/doc/ptairMS.html vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R dependencyCount: 190 Package: puma Version: 3.49.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL Archs: x64 MD5sum: 7ef752f3191f2af949664db9fea58d85 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: devel git_last_commit: 1a4a178 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/puma_3.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/puma_3.49.0.zip vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 67 Package: PureCN Version: 2.13.0 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 MD5sum: 76e88eff149fbe6ec2c25703cf36706f NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (ORCID: ), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: devel git_last_commit: cb8d0f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PureCN_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PureCN_2.13.0.zip vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 108 Package: pvac Version: 1.55.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 28db7ae5b3626e451570ba4bcfad4ccd NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: devel git_last_commit: 372aad1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pvac_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pvac_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pvac_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pvac_1.55.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 13 Package: pvca Version: 1.47.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 52e80e3a883f32cb79df8b0669f3e1b1 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: devel git_last_commit: 74c5ee9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/pvca_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/pvca_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pvca_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pvca_1.47.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: ExpressionNormalizationWorkflow, statVisual dependencyCount: 53 Package: Pviz Version: 1.41.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 999aebafd833140d35394093abcf2479 NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: devel git_last_commit: 048901b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Pviz_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Pviz_1.41.0.zip vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 157 Package: pwalign Version: 1.3.1 Depends: BiocGenerics, S4Vectors, IRanges, Biostrings (>= 2.71.5) Imports: methods, utils LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit Enhances: Rmpi License: Artistic-2.0 MD5sum: 8bc30e4d7a0a3a22b39389b8bf06dadd NeedsCompilation: yes Title: Perform pairwise sequence alignments Description: The two main functions in the package are pairwiseAlignment() and stringDist(). The former solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems. The latter computes the Levenshtein edit distance or pairwise alignment score matrix for a set of strings. biocViews: Alignment, SequenceMatching, Sequencing, Genetics Author: Patrick Aboyoun [aut], Robert Gentleman [aut], Hervé Pagès [cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/pwalign BugReports: https://github.com/Bioconductor/pwalign/issues git_url: https://git.bioconductor.org/packages/pwalign git_branch: devel git_last_commit: 73697d4 git_last_commit_date: 2024-12-06 Date/Publication: 2024-12-06 source.ver: src/contrib/pwalign_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/pwalign_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/pwalign_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/pwalign_1.3.1.tgz vignettes: vignettes/pwalign/inst/doc/PairwiseAlignments.pdf vignetteTitles: Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwalign/inst/doc/PairwiseAlignments.R dependsOnMe: amplican, hiReadsProcessor, MethTargetedNGS, QSutils, R453Plus1Toolbox, sangeranalyseR, sangerseqR, CleanBSequences importsMe: ChIPpeakAnno, CNEr, crisprShiny, DominoEffect, enhancerHomologSearch, ggseqalign, girafe, GUIDEseq, IMMAN, LinTInd, methylscaper, motifbreakR, MSA2dist, openPrimeR, scanMiR, ShortRead, StructuralVariantAnnotation, svaNUMT, TFBSTools, XNAString, kmeRs suggestsMe: BiocGenerics, Biostrings, idpr, msa, RSVSim, dowser, seqtrie dependencyCount: 26 Package: PWMEnrich Version: 4.43.0 Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: ff9f616f6794f12dcc06a05b9a648720 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: devel git_last_commit: 23d8640 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/PWMEnrich_4.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/PWMEnrich_4.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/PWMEnrich_4.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/PWMEnrich_4.43.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 31 Package: qckitfastq Version: 1.23.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 MD5sum: 6bd30344a96d2bf6504adc4b186693e5 NeedsCompilation: yes Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: devel git_last_commit: 6d6381d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qckitfastq_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qckitfastq_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qckitfastq_1.23.0.tgz vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf vignetteTitles: Quality control analysis and visualization using qckitfastq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R dependencyCount: 48 Package: qcmetrics Version: 1.45.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown, markdown License: GPL-2 MD5sum: 7d66c42e45be3062eb8ce9d22a3472d8 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: devel git_last_commit: d4b8b9e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qcmetrics_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qcmetrics_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qcmetrics_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qcmetrics_1.45.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html vignetteTitles: Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R importsMe: MSstatsQC dependencyCount: 20 Package: QDNAseq Version: 1.43.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply (>= 1.8.1) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1), parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: 7abc85b32baec305b2df159070546dc3 NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: devel git_last_commit: 2221207 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/QDNAseq_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QDNAseq_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QDNAseq_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QDNAseq_1.43.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, cfdnakit dependencyCount: 59 Package: QFeatures Version: 1.17.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.35.1), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2), igraph, grDevices, plotly, tidyr, tidyselect, reshape2 Suggests: SingleCellExperiment, MsDataHub (>= 1.3.3), Matrix, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: 8f208ea61f23edb332b5de639719b1a7 NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Christophe Vanderaa [aut] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/QFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: devel git_last_commit: 4049f6f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/QFeatures_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QFeatures_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QFeatures_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QFeatures_1.17.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, Data visualization from a QFeatures object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R, vignettes/QFeatures/inst/doc/Visualization.R dependsOnMe: hdxmsqc, msqrob2, scp, scpdata importsMe: MetaboAnnotation, MsExperiment, mspms, PSMatch suggestsMe: MsDataHub dependencyCount: 107 Package: qmtools Version: 1.11.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils, stats, igraph, VIM, scales, grDevices, graphics, limma Suggests: Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute, imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: eda7ca7629203de48d56e0b34254e62b NeedsCompilation: no Title: Quantitative Metabolomics Data Processing Tools Description: The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data. biocViews: Metabolomics, Preprocessing, Normalization, DimensionReduction, MassSpectrometry Author: Jaehyun Joo [aut, cre], Blanca Himes [aut] Maintainer: Jaehyun Joo URL: https://github.com/HimesGroup/qmtools VignetteBuilder: knitr BugReports: https://github.com/HimesGroup/qmtools/issues git_url: https://git.bioconductor.org/packages/qmtools git_branch: devel git_last_commit: d02f681 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qmtools_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qmtools_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qmtools_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qmtools_1.11.0.tgz vignettes: vignettes/qmtools/inst/doc/qmtools.html vignetteTitles: Quantitative metabolomics data processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qmtools/inst/doc/qmtools.R dependencyCount: 163 Package: qpcrNorm Version: 1.65.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 966b13adfe8bf2a60ca8e5492a8a8051 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: devel git_last_commit: 434cba8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qpcrNorm_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qpcrNorm_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qpcrNorm_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qpcrNorm_1.65.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R dependencyCount: 15 Package: qpgraph Version: 2.41.0 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.5-0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: x64 MD5sum: 0908cdb5d46feea69b05dc5244bb5116 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: devel git_last_commit: 9b522d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qpgraph_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qpgraph_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qpgraph_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qpgraph_2.41.0.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, MOSClip, topologyGSA dependencyCount: 84 Package: qPLEXanalyzer Version: 1.25.0 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: patchwork, knitr, qPLEXdata, rmarkdown, statmod, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: 8727493ce796834804210d5d7c4391c2 NeedsCompilation: no Title: Tools for quantitative proteomics data analysis Description: Tools for TMT based quantitative proteomics data analysis. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: devel git_last_commit: 9825be7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qPLEXanalyzer_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qPLEXanalyzer_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qPLEXanalyzer_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qPLEXanalyzer_1.25.0.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html vignetteTitles: qPLEXanalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependsOnMe: qPLEXdata dependencyCount: 149 Package: qsea Version: 1.33.0 Depends: R (>= 4.3) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL-2 Archs: x64 MD5sum: b46f4621eb7c84d4bc88bf08afca86a0 NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard [aut, cre] (ORCID: ), Lukas Chavez [aut] (ORCID: ), Ralf Herwig [aut] (ORCID: ) Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: devel git_last_commit: c55425a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qsea_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsea_1.33.0.zip vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R dependencyCount: 66 Package: qsmooth Version: 1.23.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: GPL-3 Archs: x64 MD5sum: dcb908e2b2ed0f360c8d900932e4f14a NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: devel git_last_commit: 6a7968b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qsmooth_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsmooth_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qsmooth_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qsmooth_1.23.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R importsMe: CleanUpRNAseq dependencyCount: 128 Package: QSutils Version: 1.25.0 Depends: R (>= 3.5), Biostrings, pwalign, BiocGenerics, methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL-2 MD5sum: 12e80d52960292478c3a3ae9210bbaad NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (ORCID: ), Josep Gregori i Font [aut] (ORCID: ) Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: devel git_last_commit: 4274cf1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/QSutils_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QSutils_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QSutils_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QSutils_1.25.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R importsMe: longreadvqs dependencyCount: 37 Package: qsvaR Version: 1.11.1 Depends: R (>= 4.2), SummarizedExperiment Imports: dplyr, sva, stats, ggplot2, rlang, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3895d549969c89515fe4ffe01cbf9992 NeedsCompilation: no Title: Generate Quality Surrogate Variable Analysis for Degradation Correction Description: The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation. biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion, DifferentialExpression, Sequencing, Coverage Author: Joshua Stolz [aut] (ORCID: ), Hedia Tnani [ctb, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ), Nicholas J. Eagles [aut] (ORCID: ) Maintainer: Hedia Tnani URL: https://github.com/LieberInstitute/qsvaR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/qsvaR git_url: https://git.bioconductor.org/packages/qsvaR git_branch: devel git_last_commit: 55f8513 git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-11 source.ver: src/contrib/qsvaR_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/qsvaR_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qsvaR_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qsvaR_1.11.1.tgz vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html vignetteTitles: Introduction to qsvaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R dependencyCount: 98 Package: QTLExperiment Version: 1.5.0 Depends: SummarizedExperiment Imports: methods, rlang, checkmate, dplyr, collapse, vroom, tidyr, tibble, utils, stats, ashr, S4Vectors, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: efe18092707b6e0d128bb9e058d870a7 NeedsCompilation: no Title: S4 classes for QTL summary statistics and metadata Description: QLTExperiment defines an S4 class for storing and manipulating summary statistics from QTL mapping experiments in one or more states. It is based on the 'SummarizedExperiment' class and contains functions for creating, merging, and subsetting objects. 'QTLExperiment' also stores experiment metadata and has checks in place to ensure that transformations apply correctly. biocViews: FunctionalGenomics, DataImport, DataRepresentation, Infrastructure, Sequencing, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, ctb] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/QTLExperiment VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/QTLExperiment/issues git_url: https://git.bioconductor.org/packages/QTLExperiment git_branch: devel git_last_commit: 658a8b5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/QTLExperiment_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QTLExperiment_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QTLExperiment_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QTLExperiment_1.5.0.tgz vignettes: vignettes/QTLExperiment/inst/doc/QTLExperiment.html vignetteTitles: An introduction to the QTLExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QTLExperiment/inst/doc/QTLExperiment.R dependsOnMe: multistateQTL dependencyCount: 75 Package: Qtlizer Version: 1.21.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 1aee7fa5be6e6fb0615e2f046113a0d5 NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (ORCID: ), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: devel git_last_commit: c7fbceb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/Qtlizer_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Qtlizer_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Qtlizer_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Qtlizer_1.21.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 25 Package: quantiseqr Version: 1.15.0 Depends: R (>= 4.1.0) Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats, SummarizedExperiment, ggplot2, tidyr, rlang, utils Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery, knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat, tibble License: GPL-3 Archs: x64 MD5sum: ef4bdb9123538637df083941686b3237 NeedsCompilation: no Title: Quantification of the Tumor Immune contexture from RNA-seq data Description: This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. biocViews: GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray, Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification, StatisticalMethod, ExperimentHubSoftware, FlowCytometry Author: Federico Marini [aut, cre] (ORCID: ), Francesca Finotello [aut] (ORCID: ) Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: devel git_last_commit: d4608cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/quantiseqr_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantiseqr_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantiseqr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantiseqr_1.15.0.tgz vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html vignetteTitles: Using quantiseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R importsMe: easier dependencyCount: 74 Package: quantro Version: 1.41.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 46bad2499177025d4db46886efc27fcc NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: devel git_last_commit: 7ff2ff0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/quantro_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantro_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantro_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantro_1.41.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: extraChIPs, qsmooth dependencyCount: 156 Package: quantsmooth Version: 1.73.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 1b2cf6d3435441c532c9722da025636d NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: devel git_last_commit: eff14ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/quantsmooth_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/quantsmooth_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/quantsmooth_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/quantsmooth_1.73.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 14 Package: QuasR Version: 1.47.2 Depends: R (>= 4.4), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools (>= 2.13.1), GenomicFeatures, txdbmaker, ShortRead, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib (>= 1.99.1) Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: 7dc75e115d396865267612219b4b005c NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Adam Alexander Thil SMITH [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://bioconductor.org/packages/QuasR SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/QuasR/issues git_url: https://git.bioconductor.org/packages/QuasR git_branch: devel git_last_commit: 7cd39a6 git_last_commit_date: 2024-12-08 Date/Publication: 2024-12-08 source.ver: src/contrib/QuasR_1.47.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/QuasR_1.47.2.zip vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R importsMe: SingleMoleculeFootprinting suggestsMe: eisaR dependencyCount: 117 Package: QuaternaryProd Version: 1.41.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: 7da483446f7b4af3a2bb465eed3d3d3b NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: devel git_last_commit: 1675b8e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/QuaternaryProd_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QuaternaryProd_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QuaternaryProd_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QuaternaryProd_1.41.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 22 Package: QUBIC Version: 1.35.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 1c08b2f83cca81766f8808e122286c78 NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: devel git_last_commit: 51d595d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/QUBIC_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/QUBIC_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/QUBIC_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/QUBIC_1.35.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R importsMe: mosbi suggestsMe: runibic dependencyCount: 53 Package: qusage Version: 2.41.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: fbad1cdb63b58a208be98e40403623a1 NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: devel git_last_commit: 6d6b8f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qusage_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qusage_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qusage_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qusage_2.41.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R importsMe: mExplorer dependencyCount: 18 Package: qvalue Version: 2.39.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: x64 MD5sum: 24c24aa852b2231ebbd3d84102874b48 NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: devel git_last_commit: 6891905 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/qvalue_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/qvalue_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/qvalue_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/qvalue_2.39.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV, cp4p, isva, ReAD, STAREG importsMe: Anaquin, anota, clusterProfiler, CTSV, DegCre, derfinder, DOSE, edge, erccdashboard, EventPointer, FindIT2, fishpond, LimROTS, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, PolySTest, RiboDiPA, RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch, SpaceMarkers, subSeq, synapter, trigger, vsclust, webbioc, IHWpaper, AEenrich, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, medScan, MOCHA, NBPSeq, qch, SeqFeatR, sffdr, shinyExprPortal, ssizeRNA, TFactSR suggestsMe: biobroom, LBE, PREDA, RnBeads, swfdr, BootstrapQTL, dartR, dartR.base, dartR.popgen, DGEobj.utils, easylabel, familiar, jackstraw, mutoss, Rediscover, seqgendiff, volcano3D, wrMisc dependencyCount: 41 Package: R3CPET Version: 1.39.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) MD5sum: a45bcf5c51bec0897e1d3b42b72a14d7 NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: devel git_last_commit: adae88d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/R3CPET_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/R3CPET_1.39.0.zip vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 167 Package: r3Cseq Version: 1.53.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 Archs: x64 MD5sum: c54addc8bbf7b49ff181e4e1d7972ed8 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: devel git_last_commit: d021c8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/r3Cseq_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/r3Cseq_1.53.0.zip vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 106 Package: R453Plus1Toolbox Version: 1.57.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), pwalign, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 MD5sum: b599d16d4229f56efd20c02d53e4bd9b NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: devel git_last_commit: 8d481bd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/R453Plus1Toolbox_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/R453Plus1Toolbox_1.57.0.zip vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 117 Package: R4RNA Version: 1.35.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: dbd7c06435c71ffe1b4045086bd7602e NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: devel git_last_commit: 50cd94f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/R4RNA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/R4RNA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/R4RNA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/R4RNA_1.35.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R importsMe: ggmsa, rnaCrosslinkOO suggestsMe: rfaRm dependencyCount: 26 Package: RadioGx Version: 2.11.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander, markdown License: GPL-3 MD5sum: cc6b89140e21713716090da1cceb9733 NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: devel git_last_commit: 7921677 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RadioGx_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RadioGx_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RadioGx_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RadioGx_2.11.0.tgz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 154 Package: raer Version: 1.5.0 Imports: stats, methods, GenomicRanges, IRanges, Rsamtools, BSgenome, Biostrings, SummarizedExperiment, SingleCellExperiment, S4Vectors, GenomeInfoDb, GenomicAlignments, GenomicFeatures, BiocGenerics, BiocParallel, rtracklayer, Matrix, cli LinkingTo: Rhtslib Suggests: testthat (>= 3.0.0), knitr, DESeq2, edgeR, limma, rmarkdown, BiocStyle, ComplexHeatmap, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.NCBI.GRCh38, scater, scran, scuttle, AnnotationHub, covr, raerdata, txdbmaker License: MIT + file LICENSE MD5sum: 88f7ab6221a8420c8cc8ff17ba8e64fb NeedsCompilation: yes Title: RNA editing tools in R Description: Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources. biocViews: MultipleComparison, RNASeq, SingleCell, Sequencing, Coverage, Epitranscriptomics, FeatureExtraction, Annotation, Alignment Author: Kent Riemondy [aut, cre] (ORCID: ), Kristen Wells-Wrasman [aut] (ORCID: ), Ryan Sheridan [ctb] (ORCID: ), Jay Hesselberth [ctb] (ORCID: ), RNA Bioscience Initiative [cph, fnd] Maintainer: Kent Riemondy URL: https://rnabioco.github.io/raer, https://github.com/rnabioco/raer SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/rnabioco/raer/issues git_url: https://git.bioconductor.org/packages/raer git_branch: devel git_last_commit: 438c245 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/raer_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/raer_1.5.0.zip vignettes: vignettes/raer/inst/doc/raer.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/raer/inst/doc/raer.R dependencyCount: 80 Package: RaggedExperiment Version: 1.31.1 Depends: R (>= 4.2.0), GenomicRanges (>= 1.37.17) Imports: BiocBaseUtils, BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: 7eee9a9bd39878cd0d4589dcff661c50 NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut], Marcel Ramos [aut, cre] (ORCID: ), Lydia King [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: devel git_last_commit: 3c81b9a git_last_commit_date: 2024-11-22 Date/Publication: 2024-11-24 source.ver: src/contrib/RaggedExperiment_1.31.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/RaggedExperiment_1.31.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RaggedExperiment_1.31.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RaggedExperiment_1.31.1.tgz vignettes: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.html, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: ASCAT to RaggedExperiment, RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.R, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, SARC, curatedPCaData importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils, terraTCGAdata, MOCHA suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO, curatedTCGAData, SingleCellMultiModal dependencyCount: 38 Package: RAIDS Version: 1.5.0 Depends: R (>= 4.2.0), gdsfmt, SNPRelate, stats, utils, GENESIS Imports: S4Vectors, GenomicRanges, ensembldb, BSgenome, AnnotationDbi, methods, class, pROC, IRanges, AnnotationFilter, rlang, VariantAnnotation, MatrixGenerics, ggplot2, stringr Suggests: testthat, knitr, rmarkdown, BiocStyle, withr, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: Apache License (>= 2) MD5sum: ff975c34e652687ddf26bd951d89fc39 NeedsCompilation: no Title: Accurate Inference of Genetic Ancestry from Cancer Sequences Description: This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58. biocViews: Genetics, Software, Sequencing, WholeGenome, PrincipalComponent, GeneticVariability, DimensionReduction, BiocViews Author: Pascal Belleau [cre, aut] (ORCID: ), Astrid Deschênes [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Pascal Belleau URL: https://krasnitzlab.github.io/RAIDS/ VignetteBuilder: knitr BugReports: https://github.com/KrasnitzLab/RAIDS/issues git_url: https://git.bioconductor.org/packages/RAIDS git_branch: devel git_last_commit: bb243c9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RAIDS_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RAIDS_1.5.0.zip vignettes: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.html, vignettes/RAIDS/inst/doc/RAIDS.html, vignettes/RAIDS/inst/doc/Wrappers.html vignetteTitles: Population reference dataset GDS files, Accurate Inference of Genetic Ancestry from Cancer-derived Sequences, Using wrappper functionss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.R, vignettes/RAIDS/inst/doc/RAIDS.R, vignettes/RAIDS/inst/doc/Wrappers.R dependencyCount: 165 Package: rain Version: 1.41.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 6e1065268e4f94955884e1265313a2c9 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: devel git_last_commit: 8ce75e2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rain_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rain_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rain_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rain_1.41.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 17 Package: ramr Version: 1.15.1 Depends: R (>= 4.1) Imports: parallel, doParallel, foreach, doRNG, methods, data.table, matrixStats, GenomicRanges, IRanges, BiocGenerics, EnvStats, ExtDist, S4Vectors, gamlss, gamlss.dist Suggests: RUnit, knitr, rmarkdown, ggplot2, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 678cc0ff47ecb4e2500bde8c3c0c4e58 NeedsCompilation: no Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of epimutations (i.e., infrequent aberrant DNA methylation events) in large data sets obtained by methylation profiling using array or high-throughput methylation sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, MethylationArray, MethylSeq Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: devel git_last_commit: 4e3b57e git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/ramr_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/ramr_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ramr_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ramr_1.15.1.tgz vignettes: vignettes/ramr/inst/doc/ramr.html vignetteTitles: ramr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramr/inst/doc/ramr.R dependencyCount: 75 Package: ramwas Version: 1.31.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 MD5sum: 120929b566737999afdd30a5be91c7f3 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (ORCID: ), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: devel git_last_commit: d24c1d2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ramwas_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ramwas_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ramwas_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ramwas_1.31.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 103 Package: randPack Version: 1.53.0 Depends: methods Imports: Biobase License: Artistic 2.0 Archs: x64 MD5sum: 0a62eb37c9989f2247a1422dc65f5f41 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: devel git_last_commit: 4066dc4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/randPack_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/randPack_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/randPack_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/randPack_1.53.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 7 Package: randRotation Version: 1.19.0 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 Archs: x64 MD5sum: 5505ca6c5cc963c25feb28af16beef76 NeedsCompilation: no Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] (ORCID: ) Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues git_url: https://git.bioconductor.org/packages/randRotation git_branch: devel git_last_commit: f41d79e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/randRotation_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/randRotation_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/randRotation_1.19.0.tgz vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf vignetteTitles: Random Rotation Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R dependencyCount: 7 Package: RankProd Version: 3.33.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes Archs: x64 MD5sum: 2fbf3797ccfaabeab24de692df3cb738 NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: devel git_last_commit: 11e9e00 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RankProd_3.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RankProd_3.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RankProd_3.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RankProd_3.33.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: tRanslatome importsMe: mslp, POMA, synlet, INCATome suggestsMe: sigQC dependencyCount: 6 Package: RAREsim Version: 1.11.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0657e2ed67e885c409c847612c489bbf NeedsCompilation: no Title: Simulation of Rare Variant Genetic Data Description: Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation. biocViews: Genetics, Software, VariantAnnotation, Sequencing Author: Megan Null [aut], Ryan Barnard [cre] Maintainer: Ryan Barnard URL: https://github.com/meganmichelle/RAREsim VignetteBuilder: knitr BugReports: https://github.com/meganmichelle/RAREsim/issues git_url: https://git.bioconductor.org/packages/RAREsim git_branch: devel git_last_commit: a9cca8e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RAREsim_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RAREsim_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RAREsim_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RAREsim_1.11.0.tgz vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html vignetteTitles: RAREsim Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R dependencyCount: 1 Package: RareVariantVis Version: 2.35.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: 11798cf2aafceeda262c33ba99d75019 NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: devel git_last_commit: f38cd5b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RareVariantVis_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RareVariantVis_2.35.0.zip vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 109 Package: Rarr Version: 1.7.1 Depends: DelayedArray, BiocGenerics Imports: jsonlite, httr, stringr, R.utils, utils, paws.storage, methods Suggests: BiocStyle, covr, knitr, tinytest, mockery License: MIT + file LICENSE Archs: x64 MD5sum: 025f5b83769fd0d7e22e10a36f3cc749 NeedsCompilation: yes Title: Read Zarr Files in R Description: The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It's design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Rarr aims to implement this specifcation in R with minimal reliance on an external tools or libraries. biocViews: DataImport Author: Mike Smith [aut, cre] (ORCID: ) Maintainer: Mike Smith URL: https://github.com/grimbough/Rarr SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rarr/issues git_url: https://git.bioconductor.org/packages/Rarr git_branch: devel git_last_commit: 8f1c9da git_last_commit_date: 2024-11-25 Date/Publication: 2024-11-25 source.ver: src/contrib/Rarr_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rarr_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rarr_1.7.1.tgz vignettes: vignettes/Rarr/inst/doc/Rarr.html vignetteTitles: "Working with Zarr arrays in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rarr/inst/doc/Rarr.R dependencyCount: 48 Package: rawDiag Version: 1.3.4 Depends: R (>= 4.4) Imports: dplyr, ggplot2 (>= 3.4), grDevices, hexbin, htmltools, BiocManager, BiocParallel, rawrr (>= 1.15.5), rlang, reshape2, scales, shiny (>= 1.5), stats, utils Suggests: BiocStyle (>= 2.28), ExperimentHub, tartare, knitr, testthat License: GPL-3 MD5sum: cc1aa22c8bfa48736c59dc3895c13d5f NeedsCompilation: no Title: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful Description: Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) poses a nontrivial challenge. The rawDiag package facilitates rational method optimization by generating MS operator-tailored diagnostic plots of scan-level metadata. The package is designed for use on the R shell or as a Shiny application on the Orbitrap instrument PC. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software, ShinyApps Author: Christian Panse [aut, cre] (ORCID: ), Christian Trachsel [aut], Tobias Kockmann [aut] Maintainer: Christian Panse URL: https://github.com/fgcz/rawDiag/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawDiag/issues git_url: https://git.bioconductor.org/packages/rawDiag git_branch: devel git_last_commit: 21b3fa2 git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-12 source.ver: src/contrib/rawDiag_1.3.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/rawDiag_1.3.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rawDiag_1.3.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rawDiag_1.3.4.tgz vignettes: vignettes/rawDiag/inst/doc/rawDiag.html vignetteTitles: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawDiag/inst/doc/rawDiag.R dependencyCount: 79 Package: rawrr Version: 1.15.10 Depends: R (>= 4.1) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 Archs: x64 MD5sum: 04f12dd7e884f9a7158bead141243de9 NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the Thermo Fisher Scientic RawFileReader .NET 8.0 assembly. Within the R environment, spectra and chromatograms are represented by S3 objects. The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software Author: Christian Panse [aut, cre] (ORCID: ), Leonardo Schwarz [ctb] (ORCID: ), Tobias Kockmann [aut] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ SystemRequirements: .NET 8.0 (optional; required only if you want to compile and link the C# code) VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: devel git_last_commit: cd82c1d git_last_commit_date: 2024-12-04 Date/Publication: 2024-12-04 source.ver: src/contrib/rawrr_1.15.10.tar.gz win.binary.ver: bin/windows/contrib/4.5/rawrr_1.15.10.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rawrr_1.15.10.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rawrr_1.15.10.tgz vignettes: vignettes/rawrr/inst/doc/rawrr.html vignetteTitles: Direct Access to Orbitrap Data and Beyond hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawrr/inst/doc/rawrr.R importsMe: MsBackendRawFileReader, rawDiag dependencyCount: 4 Package: RbcBook1 Version: 1.75.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 2adbfcd1ce2f4ff11d33e1f4da3abb74 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: devel git_last_commit: fa0cc73 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RbcBook1_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RbcBook1_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RbcBook1_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RbcBook1_1.75.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 11 Package: Rbec Version: 1.15.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: df67d205e2c48f8210bca58fd130ec5a NeedsCompilation: yes Title: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities Description: Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples. biocViews: Sequencing, MicrobialStrain, Microbiome Author: Pengfan Zhang [aut, cre] Maintainer: Pengfan Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: devel git_last_commit: baf2f91 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rbec_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rbec_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbec_1.15.0.tgz vignettes: vignettes/Rbec/inst/doc/Rbec.html vignetteTitles: Rbec hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbec/inst/doc/Rbec.R dependencyCount: 107 Package: RBGL Version: 1.83.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 46de6022c5b9eef95b45ba25d6f21829 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey [aut], Li Long [aut], R. Gentleman [aut], Emmanuel Taiwo [ctb] (Converted RBGL vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RBGL git_branch: devel git_last_commit: 3ba1ecb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RBGL_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBGL_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBGL_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBGL_1.83.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.html vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, PerfMeas, SubpathwayLNCE importsMe: BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GenomicInteractionNodes, GOstats, NCIgraph, OrganismDbi, Streamer, VariantFiltering, BiDAG, clustNet, eff2, HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, SID suggestsMe: DEGraph, G4SNVHunter, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, maGUI dependencyCount: 9 Package: RBioFormats Version: 1.7.0 Imports: EBImage, methods, rJava (>= 0.9-6), S4Vectors, stats Suggests: BiocStyle, knitr, testthat, xml2 License: GPL-3 MD5sum: 113a5a60c00fad0b34f6cfa8121efcbb NeedsCompilation: no Title: R interface to Bio-Formats Description: An R package which interfaces the OME Bio-Formats Java library to allow reading of proprietary microscopy image data and metadata. biocViews: DataImport Author: Andrzej Oleś [aut, cre] (ORCID: ), John Lee [ctb] (ORCID: ) Maintainer: Andrzej Oleś URL: https://github.com/aoles/RBioFormats SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr BugReports: https://github.com/aoles/RBioFormats/issues git_url: https://git.bioconductor.org/packages/RBioFormats git_branch: devel git_last_commit: f6293b2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RBioFormats_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBioFormats_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBioFormats_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBioFormats_1.7.0.tgz vignettes: vignettes/RBioFormats/inst/doc/RBioFormats.html vignetteTitles: RBioFormats: an R interface to the Bio-Formats library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioFormats/inst/doc/RBioFormats.R importsMe: SpatialOmicsOverlay suggestsMe: SpatialFeatureExperiment, Voyager dependencyCount: 49 Package: RBioinf Version: 1.67.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 MD5sum: e0b0034deb31ffa1de10ba69d186b69b NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/RBioinf git_branch: devel git_last_commit: aca51a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RBioinf_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBioinf_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBioinf_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBioinf_1.67.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R dependencyCount: 8 Package: rBiopaxParser Version: 2.47.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: a7495cf4ae42e2f93020de3a4515bb4d NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: devel git_last_commit: dd61169 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rBiopaxParser_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rBiopaxParser_2.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rBiopaxParser_2.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rBiopaxParser_2.47.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: rBLAST Version: 1.3.0 Depends: Biostrings (>= 2.26.2) Imports: methods, utils, BiocFileCache Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: bae9c0d983601fa17e419129e40f92fb NeedsCompilation: no Title: R Interface for the Basic Local Alignment Search Tool Description: Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute. biocViews: Genetics, Sequencing, SequenceMatching, Alignment, DataImport Author: Michael Hahsler [aut, cre] (ORCID: ), Nagar Anurag [aut] Maintainer: Michael Hahsler URL: https://github.com/mhahsler/rBLAST SystemRequirements: ncbi-blast+ VignetteBuilder: knitr BugReports: https://github.com/mhahsler/rBLAST/issues git_url: https://git.bioconductor.org/packages/rBLAST git_branch: devel git_last_commit: 39a607f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rBLAST_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rBLAST_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rBLAST_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rBLAST_1.3.0.tgz vignettes: vignettes/rBLAST/inst/doc/blast.html vignetteTitles: rBLAST: R Interface for the Basic Local Alignment Search Tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rBLAST/inst/doc/blast.R dependencyCount: 57 Package: RBM Version: 1.39.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) Archs: x64 MD5sum: a9ebfb6ae5114a1fb1820269a0b7f74e NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: devel git_last_commit: 7acbaea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RBM_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RBM_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RBM_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RBM_1.39.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 8 Package: Rbowtie Version: 1.47.0 Imports: utils Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: fc8f3f0bd77c816b375eafff7c4c2e5a NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne [aut], Anita Lerch [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/Rbowtie SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rbowtie/issues git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: devel git_last_commit: 1ccecf4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rbowtie_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rbowtie_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie_1.47.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: crisprBowtie, multicrispr, seqpac suggestsMe: crisprDesign, eisaR dependencyCount: 1 Package: Rbowtie2 Version: 2.13.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 8b7028da65c5072bd0d80e370897ab46 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei [aut, cre], Wei Zhang [aut] Maintainer: Zheng Wei SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: devel git_last_commit: d83f9de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rbowtie2_2.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie2_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie2_2.13.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: CircSeqAlignTk, esATAC, UMI4Cats dependencyCount: 41 Package: rbsurv Version: 2.65.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: ead112938961c8ddaba83960e03bbb45 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: devel git_last_commit: 8f38276 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rbsurv_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rbsurv_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rbsurv_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rbsurv_2.65.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 13 Package: Rbwa Version: 1.11.0 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: 099a86b1b6d2cce0acd52917ce040491 NeedsCompilation: yes Title: R wrapper for BWA-backtrack and BWA-MEM aligners Description: Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines. biocViews: Sequencing, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/Rbwa SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/Rbwa/issues git_url: https://git.bioconductor.org/packages/Rbwa git_branch: devel git_last_commit: 441c698 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rbwa_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rbwa_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rbwa_1.11.0.tgz vignettes: vignettes/Rbwa/inst/doc/Rbwa.html vignetteTitles: An introduction to Rbwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R importsMe: crisprBwa suggestsMe: crisprDesign dependencyCount: 0 Package: RCAS Version: 1.33.0 Depends: R (>= 3.5.0), plotly (>= 4.5.2), DT (>= 0.2), data.table Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, txdbmaker, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, seqLogo, utils, ranger, gprofiler2 Suggests: testthat, covr, BiocManager License: Artistic-2.0 MD5sum: 3dc884fe331821233a3e869551a129d2 NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: devel git_last_commit: 44fd977 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/RCAS_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCAS_1.33.0.zip vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R importsMe: GenomicPlot dependencyCount: 162 Package: RCASPAR Version: 1.53.0 License: GPL (>=3) MD5sum: 83f4f3b7c15dc367660bfeac477766c0 NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: devel git_last_commit: 105038a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RCASPAR_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCASPAR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCASPAR_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCASPAR_1.53.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.29.0 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel, rmarkdown License: LGPL-3 + file LICENSE MD5sum: 1dcbf748fd2d10f0f7ad61e95bf7ce51 NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: devel git_last_commit: 6866a6f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rcellminer_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rcellminer_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rcellminer_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rcellminer_2.29.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 70 Package: rCGH Version: 1.37.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 0250c6588dfb60e936f14cebc9f1cde0 NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: devel git_last_commit: f2bf7cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rCGH_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rCGH_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rCGH_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rCGH_1.37.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R importsMe: preciseTAD dependencyCount: 132 Package: RcisTarget Version: 1.27.0 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, S4Vectors, utils, zoo Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, gplots, rtracklayer, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG License: GPL-3 MD5sum: b7e87937febf6d2eee990c0cb45107fc NeedsCompilation: no Title: RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Gert Hulselmans URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: devel git_last_commit: 21c0c48 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RcisTarget_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RcisTarget_1.27.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RcisTarget_1.27.0.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment, RcisTarget - on regions, RcisTarget - with background hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R dependencyCount: 125 Package: Rcollectl Version: 1.7.0 Imports: utils, ggplot2, lubridate, processx Suggests: knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: b962309dcef5a22e0410f25976e32e47 NeedsCompilation: no Title: Help use collectl with R in Linux, to measure resource consumption in R processes Description: Provide functions to obtain instrumentation data on processes in a unix environment. Parse output of a collectl run. Vizualize aspects of system usage over time, with annotation. biocViews: Software, Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ), Yubo Cheng [aut] Maintainer: Vincent Carey URL: https://github.com/vjcitn/Rcollectl SystemRequirements: collectl VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/Rcollectl git_url: https://git.bioconductor.org/packages/Rcollectl git_branch: devel git_last_commit: bd65277 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rcollectl_1.7.0.tar.gz vignettes: vignettes/Rcollectl/inst/doc/Rcollectl.html vignetteTitles: Rcollectl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcollectl/inst/doc/Rcollectl.R dependencyCount: 41 Package: Rcpi Version: 1.43.0 Depends: R (>= 3.0.2) Imports: Biostrings, GOSemSim, curl, doParallel, foreach, httr2, jsonlite, methods, rlang, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 | file LICENSE MD5sum: 6df0ceb2cdeaeb53bdc9546643f72e1e NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre] (ORCID: ), Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: devel git_last_commit: 88f2c1f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rcpi_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rcpi_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rcpi_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rcpi_1.43.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 64 Package: RCSL Version: 1.15.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2(>= 3.4.0), methods, pracma, umap, grDevices, graphics, stats, Rcpp (>= 0.11.0), MatrixGenerics, SingleCellExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown, mclust, tidyverse, tinytex License: Artistic-2.0 MD5sum: 0ca7919fd18328629a78938d14b4e130 NeedsCompilation: no Title: Rank Constrained Similarity Learning for single cell RNA sequencing data Description: A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Sequencing Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd] Maintainer: Qinglin Mei URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: devel git_last_commit: 8f4cc5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RCSL_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCSL_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCSL_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCSL_1.15.0.tgz vignettes: vignettes/RCSL/inst/doc/RCSL.html vignetteTitles: RCSL package manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCSL/inst/doc/RCSL.R dependencyCount: 80 Package: Rcwl Version: 1.23.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools, basilisk Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: 25730dd8dfdde7abf262a4794eaff510 NeedsCompilation: no Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: devel git_last_commit: dd1c6c5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rcwl_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rcwl_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rcwl_1.23.0.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: Rcwl: An R interface to the Common Workflow Language hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines importsMe: ReUseData dependencyCount: 111 Package: RcwlPipelines Version: 1.23.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 48be1b696c6184248f04ef5c98f69a06 NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: devel git_last_commit: 77e9e9a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RcwlPipelines_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RcwlPipelines_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RcwlPipelines_1.23.0.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: RcwlPipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R importsMe: ReUseData dependencyCount: 124 Package: RCX Version: 1.11.0 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE Archs: x64 MD5sum: f9d2bfb0f80e6f3bdaa48057050f517d NeedsCompilation: no Title: R package implementing the Cytoscape Exchange (CX) format Description: Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape. biocViews: Pathways, DataImport, Network Author: Florian Auer [aut, cre] (ORCID: ) Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/RCX VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/RCX/issues git_url: https://git.bioconductor.org/packages/RCX git_branch: devel git_last_commit: c707da4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RCX_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCX_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCX_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCX_1.11.0.tgz vignettes: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX from scratch, 03. Extending the RCX Data Model, 01. RCX - an R package implementing the Cytoscape Exchange (CX) format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R dependsOnMe: ndexr dependencyCount: 20 Package: RCy3 Version: 2.27.0 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, stringi, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer, gplots Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE Archs: x64 MD5sum: 822ab398d91dccc78a65d7469e96615d NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (ORCID: ), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: devel git_last_commit: 6d1a209 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RCy3_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCy3_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCy3_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCy3_2.27.0.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14. Jupyter Bridge and RCy3 ~10 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, enrichViewNet, fedup, GeneNetworkBuilder, MetaPhOR, MOGAMUN, NCIgraph, regutools, transomics2cytoscape, dendroNetwork, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, rScudo, tidysbml, sharp dependencyCount: 49 Package: RCyjs Version: 2.29.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 06a52938e4d4cc13cff9b6e6f6b4a9be NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: devel git_last_commit: d9fd4c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RCyjs_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RCyjs_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCyjs_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RCyjs_2.29.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 19 Package: Rdisop Version: 1.67.4 Depends: R (>= 2.10), Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, RUnit, testthat (>= 3.0.0) License: GPL-2 Archs: x64 MD5sum: e210973eed84948d8a7afdabc6e073ec NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin [aut], Steffen Neumann [aut, cre] (ORCID: ), Jan Lisec [ctb] (ORCID: ), Miao Yu [ctb] Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None VignetteBuilder: knitr BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: devel git_last_commit: 76423fd git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-10 source.ver: src/contrib/Rdisop_1.67.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rdisop_1.67.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rdisop_1.67.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rdisop_1.67.4.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.html vignetteTitles: Mass decomposition with the Rdisop package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: enviGCMS suggestsMe: adductomicsR, MSnbase, RforProteomics, CorMID, InterpretMSSpectrum dependencyCount: 3 Package: RDRToolbox Version: 1.57.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 6ddd29bbf96ec594036bd1a2c9533173 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: devel git_last_commit: fa6a727 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RDRToolbox_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RDRToolbox_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RDRToolbox_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RDRToolbox_1.57.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon dependencyCount: 36 Package: ReactomeGSA Version: 1.21.2 Imports: Biobase, BiocSingular, dplyr, ggplot2, gplots, httr, igraph, jsonlite, methods, progress, RColorBrewer, SummarizedExperiment, tidyr Suggests: devtools, knitr, ReactomeGSA.data, rmarkdown, scater, scran, scRNAseq, scuttle, Seurat (>= 3.0), SingleCellExperiment, testthat License: MIT + file LICENSE MD5sum: 771833d7894095791de20d6b68251802 NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: devel git_last_commit: 1612e12 git_last_commit_date: 2024-11-27 Date/Publication: 2024-11-29 source.ver: src/contrib/ReactomeGSA_1.21.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReactomeGSA_1.21.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReactomeGSA_1.21.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReactomeGSA_1.21.2.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Loading and re-analysing public data through ReactomeGSA, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data dependencyCount: 95 Package: ReactomePA Version: 1.51.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite, gson, yulab.utils (>= 0.1.5) Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: b359c61464f7447086da80538ac97176 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: devel git_last_commit: e581e3e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ReactomePA_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReactomePA_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReactomePA_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReactomePA_1.51.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R dependsOnMe: maEndToEnd importsMe: bioCancer, gINTomics, miRSM, miRspongeR, Pigengene, scTensor, ExpHunterSuite suggestsMe: CBNplot, ChIPseeker, CINdex, cola, GeDi, GRaNIE, scGPS dependencyCount: 133 Package: ReadqPCR Version: 1.53.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 Archs: x64 MD5sum: 58d2860c7bd23dd4c50048880883d8d8 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: devel git_last_commit: e70ef53 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ReadqPCR_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ReadqPCR_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReadqPCR_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReadqPCR_1.53.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 7 Package: REBET Version: 1.25.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 7ec2955f62ee9557e8296d465e1b8b29 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: devel git_last_commit: 28e6fc9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/REBET_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REBET_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/REBET_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/REBET_1.25.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 28 Package: rebook Version: 1.17.0 Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends, dir.expiry, filelock, BiocStyle Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown, rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows License: GPL-3 MD5sum: 5f6a821169049a126ac6324e39538525 NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: devel git_last_commit: 9f1bbd3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rebook_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rebook_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rebook_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rebook_1.17.0.tgz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependsOnMe: csawBook, OSCA, OSCA.intro, OSCA.workflows, SingleRBook dependencyCount: 44 Package: receptLoss Version: 1.19.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE MD5sum: 409a2b68c935d0d01d4c568cdd94ca2f NeedsCompilation: no Title: Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors Description: receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well. biocViews: GeneExpression, StatisticalMethod Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: devel git_last_commit: f57ea9f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/receptLoss_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/receptLoss_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/receptLoss_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/receptLoss_1.19.0.tgz vignettes: vignettes/receptLoss/inst/doc/receptLoss.html vignetteTitles: receptLoss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R dependencyCount: 70 Package: recount Version: 1.33.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, txdbmaker, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT, edgeR, ggplot2, RColorBrewer License: Artistic-2.0 MD5sum: db31035f911b83485f7c1847cd738d5c NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (ORCID: ), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (ORCID: ), Kasper Daniel Hansen [ctb] (ORCID: ), Ben Langmead [ctb] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: devel git_last_commit: 7570ca3 git_last_commit_date: 2024-12-12 Date/Publication: 2024-12-13 source.ver: src/contrib/recount_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recount_1.33.0.zip vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: recount3 dependencyCount: 167 Package: recount3 Version: 1.17.0 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, httr, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, interactiveDisplayBase, recount License: Artistic-2.0 MD5sum: 3fc6e64f3ef68704fa4ffe6c75b8bd3c NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: devel git_last_commit: 4c4249b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/recount3_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recount3_1.17.0.zip vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R suggestsMe: RNAseqQC dependencyCount: 94 Package: recountmethylation Version: 1.17.0 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, basilisk, reticulate, DelayedMatrixStats Suggests: minfiData, minfiDataEPIC, knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: 6fd3852c45bccf94508f91f4c9f0afbb NeedsCompilation: no Title: Access and analyze public DNA methylation array data compilations Description: Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (ORCID: ), Brian Walsh [aut] (ORCID: ), Kyle Ellrott [aut] (ORCID: ), Kasper D Hansen [aut] (ORCID: ), Reid F Thompson [aut] (ORCID: ), Abhinav Nellore [aut] (ORCID: ) Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues git_url: https://git.bioconductor.org/packages/recountmethylation git_branch: devel git_last_commit: 873438e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/recountmethylation_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recountmethylation_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/recountmethylation_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/recountmethylation_1.17.0.tgz vignettes: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.html, vignettes/recountmethylation/inst/doc/exporting_saving_data.html, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html, vignettes/recountmethylation/inst/doc/recountmethylation_glint.html, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.html, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.html, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Practical uses for CpG annotations, Working with DNAm data types, Data Analyses, Determine population ancestry from DNAm arrays, Power analysis for DNAm arrays, Nearest neighbors analysis for DNAm arrays, recountmethylation User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.R, vignettes/recountmethylation/inst/doc/exporting_saving_data.R, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R, vignettes/recountmethylation/inst/doc/recountmethylation_glint.R, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.R, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.R, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R dependencyCount: 155 Package: recoup Version: 1.35.0 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, Biostrings, circlize, GenomeInfoDb, GenomicFeatures, graphics, grDevices, httr, IRanges, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, txdbmaker, utils Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: 274d288281f9edee8e6291be30b0ad12 NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: devel git_last_commit: 8112b7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/recoup_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/recoup_1.35.0.zip vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 126 Package: RedeR Version: 3.3.0 Depends: R (>= 4.0), methods Imports: scales, igraph Suggests: knitr, rmarkdown, markdown, BiocStyle, TreeAndLeaf License: GPL-3 MD5sum: 0d90681342f9c3de58d4510a87d3dc70 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. biocViews: GUI, GraphAndNetwork, Network, NetworkEnrichment, NetworkInference, Software, SystemsBiology Author: Xin Wang [ctb], Florian Markowetz [ctb], Mauro Castro [aut, cre] (ORCID: ) Maintainer: Mauro Castro URL: https://doi.org/10.1186/gb-2012-13-4-r29 SystemRequirements: Java Runtime Environment (Java>= 11) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: devel git_last_commit: 7abfaa7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RedeR_3.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RedeR_3.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RedeR_3.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RedeR_3.3.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: nested networks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf suggestsMe: PathwaySpace dependencyCount: 25 Package: RedisParam Version: 1.9.0 Depends: R (>= 4.2.0), BiocParallel (>= 1.29.12) Imports: methods, redux, withr, futile.logger Suggests: rmarkdown, knitr, testthat, BiocStyle License: Artistic-2.0 MD5sum: 332aa4a4d5d14cdf382c5259ef64b7cb NeedsCompilation: no Title: Provide a 'redis' back-end for BiocParallel Description: This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The 'manager' distributes tasks to a 'worker' pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative 'load balancing' task distributions. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (ORCID: ), Jiefei Wang [aut] Maintainer: Martin Morgan SystemRequirements: hiredis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedisParam git_branch: devel git_last_commit: ce2bc74 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RedisParam_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RedisParam_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RedisParam_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RedisParam_1.9.0.tgz vignettes: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.html, vignettes/RedisParam/inst/doc/RedisParamUserGuide.html vignetteTitles: RedisParam for Developers, Using RedisParam hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.R, vignettes/RedisParam/inst/doc/RedisParamUserGuide.R dependencyCount: 20 Package: REDseq Version: 1.53.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) MD5sum: 4b27fda1c51a48ff696ce1200214f7e2 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: devel git_last_commit: c53b524 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/REDseq_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REDseq_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/REDseq_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/REDseq_1.53.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 134 Package: RegEnrich Version: 1.17.0 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr, BiocStyle Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: 41c3e81843e4f7c4376169a321cb0c4d NeedsCompilation: no Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: devel git_last_commit: 231e44e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RegEnrich_1.17.0.tar.gz vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html vignetteTitles: Gene regulator enrichment with RegEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R dependencyCount: 157 Package: regionalpcs Version: 1.5.0 Depends: R (>= 4.3.0) Imports: dplyr, PCAtools, tibble, GenomicRanges Suggests: knitr, rmarkdown, RMTstat, testthat (>= 3.0.0), BiocStyle, tidyr, minfiData, TxDb.Hsapiens.UCSC.hg19.knownGene, IRanges License: MIT + file LICENSE MD5sum: e13e89ca540cfac1138f2a0d5ef242c5 NeedsCompilation: no Title: Summarizing Regional Methylation with Regional Principal Components Analysis Description: Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data. biocViews: DNAMethylation, DifferentialMethylation, StatisticalMethod, Software, MethylationArray Author: Tiffany Eulalio [aut, cre] (ORCID: ) Maintainer: Tiffany Eulalio URL: https://github.com/tyeulalio/regionalpcs VignetteBuilder: knitr BugReports: https://github.com/tyeulalio/regionalpcs/issues git_url: https://git.bioconductor.org/packages/regionalpcs git_branch: devel git_last_commit: 78178d2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/regionalpcs_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regionalpcs_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regionalpcs_1.5.0.tgz vignettes: vignettes/regionalpcs/inst/doc/regionalpcs.html vignetteTitles: regionalpcs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regionalpcs/inst/doc/regionalpcs.R dependencyCount: 91 Package: RegionalST Version: 1.5.8 Depends: R (>= 4.3.0) Imports: stats, grDevices, utils, ggplot2, dplyr, scater, gridExtra, BiocStyle, BayesSpace, fgsea, magrittr, SingleCellExperiment, RColorBrewer, Seurat, S4Vectors, tibble, TOAST, assertthat, colorspace, shiny, SummarizedExperiment Suggests: knitr, rmarkdown, gplots, testthat (>= 3.0.0) License: GPL-3 MD5sum: d9794d34ef48ea7495f4c7d09ce92ff9 NeedsCompilation: no Title: Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data Description: This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application. biocViews: Spatial, Transcriptomics, Reactome, KEGG Author: Ziyi Li [aut, cre] Maintainer: Ziyi Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegionalST git_branch: devel git_last_commit: 0ddee1b git_last_commit_date: 2024-12-05 Date/Publication: 2024-12-05 source.ver: src/contrib/RegionalST_1.5.8.tar.gz win.binary.ver: bin/windows/contrib/4.5/RegionalST_1.5.8.zip vignettes: vignettes/RegionalST/inst/doc/RegionalST.html vignetteTitles: RegionalST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegionalST/inst/doc/RegionalST.R dependencyCount: 254 Package: regioneR Version: 1.39.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 4c72576e313fa0f0cab3b66ef50eb944 NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: devel git_last_commit: 0c8711e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/regioneR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regioneR_1.39.0.zip vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR, regioneReloaded importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RgnTX, UMI4Cats suggestsMe: CNVRanger, EpiMix, UPDhmm, MitoHEAR dependencyCount: 64 Package: regioneReloaded Version: 1.9.0 Depends: R (>= 4.2), regioneR Imports: stats, RColorBrewer, Rtsne, umap, ggplot2, ggrepel, reshape2, methods, scales, cluster, grid, grDevices Suggests: rmarkdown, BiocStyle, GenomeInfoDb, knitr, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 6b107eb96910a600c43ac9eccd4041b7 NeedsCompilation: no Title: RegioneReloaded: Multiple Association for Genomic Region Sets Description: RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation, Clustering, MultipleComparison Author: Roberto Malinverni [aut, cre] (ORCID: ), David Corujo [aut], Bernat Gel [aut] Maintainer: Roberto Malinverni URL: https://github.com/RMalinverni/regioneReload VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneReloaded git_branch: devel git_last_commit: 11e1a34 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/regioneReloaded_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regioneReloaded_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regioneReloaded_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regioneReloaded_1.9.0.tgz vignettes: vignettes/regioneReloaded/inst/doc/regioneReloaded.html vignetteTitles: regioneReloaded hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneReloaded/inst/doc/regioneReloaded.R dependencyCount: 107 Package: regsplice Version: 1.33.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 19dd2a9fa1dd90d143a674959fb38330 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: devel git_last_commit: 27b614a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/regsplice_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regsplice_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/regsplice_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/regsplice_1.33.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 52 Package: regutools Version: 1.19.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: fb0bf5d8129d2616f1bbd74621479d21 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (ORCID: ), Carmina Barberena-Jonas [aut] (ORCID: ), Jesus E. Sotelo-Fonseca [aut] (ORCID: ), Jose Alquicira-Hernandez [ctb] (ORCID: ), Heladia Salgado [ctb] (ORCID: ), Leonardo Collado-Torres [aut] (ORCID: ), Alejandro Reyes [aut] (ORCID: ) Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: devel git_last_commit: 6b11fd5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/regutools_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/regutools_1.19.0.zip vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 173 Package: REMP Version: 1.31.0 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: 40ff58a58ae60188430d2d0c872a3678 NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: devel git_last_commit: 6b87c46 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/REMP_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/REMP_1.31.0.zip vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 199 Package: Repitools Version: 1.53.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) MD5sum: b3fc35357dcfd7dd162b464c633cf30d NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: devel git_last_commit: b707049 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Repitools_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Repitools_1.53.0.zip vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R dependencyCount: 74 Package: ReportingTools Version: 2.47.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db, rmarkdown, markdown License: Artistic-2.0 MD5sum: 4fb3117eff39bbe73f9418abaa821750 NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, rmarkdown git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: devel git_last_commit: 30a7956 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ReportingTools_2.47.0.tar.gz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 184 Package: RepViz Version: 1.23.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: rmarkdown, knitr, testthat License: GPL-3 Archs: x64 MD5sum: dbc91943c40ad97646c089907d5f3a02 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: devel git_last_commit: 76973d5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RepViz_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RepViz_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RepViz_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RepViz_1.23.0.tgz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 82 Package: ResidualMatrix Version: 1.17.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: d73abb6733c3ebf028db4fff12602d43 NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: devel git_last_commit: bde62f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ResidualMatrix_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ResidualMatrix_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ResidualMatrix_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ResidualMatrix_1.17.0.tgz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: alabaster.matrix, BiocSingular, chihaya, scran dependencyCount: 23 Package: RESOLVE Version: 1.9.0 Depends: R (>= 4.1.0) Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, data.table, GenomeInfoDb, GenomicRanges, glmnet, ggplot2, gridExtra, IRanges, lsa, MutationalPatterns, nnls, parallel, reshape2, S4Vectors, RhpcBLASctl Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 5110c395174a10a28ae3e4f60db963cb NeedsCompilation: no Title: RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes Description: Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Luca De Sano [cre, aut] (ORCID: ) Maintainer: Luca De Sano URL: https://github.com/danro9685/RESOLVE VignetteBuilder: knitr BugReports: https://github.com/danro9685/RESOLVE/issues git_url: https://git.bioconductor.org/packages/RESOLVE git_branch: devel git_last_commit: 3575d7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RESOLVE_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RESOLVE_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RESOLVE_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RESOLVE_1.9.0.tgz vignettes: vignettes/RESOLVE/inst/doc/RESOLVE.html vignetteTitles: RESOLVE.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RESOLVE/inst/doc/RESOLVE.R dependencyCount: 136 Package: retrofit Version: 1.7.0 Depends: R (>= 4.2), Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, DescTools, ggplot2, corrplot, cowplot, grid, colorspace, png, reshape2, pals, RCurl License: GPL-3 MD5sum: a03cc8d8db67766db92067677dfb7004 NeedsCompilation: yes Title: RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics Description: RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression, DimensionReduction, FeatureExtraction, SingleCell Author: Adam Park [aut, cre], Roopali Singh [aut] (ORCID: ), Xiang Zhu [aut] (ORCID: ), Qunhua Li [aut] (ORCID: ) Maintainer: Adam Park URL: https://github.com/qunhualilab/retrofit VignetteBuilder: knitr BugReports: https://github.com/qunhualilab/retrofit/issues git_url: https://git.bioconductor.org/packages/retrofit git_branch: devel git_last_commit: a8142a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/retrofit_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/retrofit_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/retrofit_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/retrofit_1.7.0.tgz vignettes: vignettes/retrofit/inst/doc/ColonVignette.html, vignettes/retrofit/inst/doc/SimulationVignette.html vignetteTitles: Retrofit Colon Vignette, Retrofit Simulation Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/retrofit/inst/doc/ColonVignette.R, vignettes/retrofit/inst/doc/SimulationVignette.R dependencyCount: 3 Package: ReUseData Version: 1.7.0 Imports: Rcwl, RcwlPipelines, BiocFileCache, S4Vectors, stats, tools, utils, methods, jsonlite, yaml, basilisk Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: a85e9118e03ac054c845e1cf32b79e39 NeedsCompilation: no Title: Reusable and reproducible Data Management Description: ReUseData is an _R/Bioconductor_ software tool to provide a systematic and versatile approach for standardized and reproducible data management. ReUseData facilitates transformation of shell or other ad hoc scripts for data preprocessing into workflow-based data recipes. Evaluation of data recipes generate curated data files in their generic formats (e.g., VCF, bed). Both recipes and data are cached using database infrastructure for easy data management and reuse. Prebuilt data recipes are available through ReUseData portal ("https://rcwl.org/dataRecipes/") with full annotation and user instructions. Pregenerated data are available through ReUseData cloud bucket that is directly downloadable through "getCloudData()". biocViews: Software, Infrastructure, DataImport, Preprocessing, ImmunoOncology Author: Qian Liu [aut, cre] (ORCID: ) Maintainer: Qian Liu URL: https://github.com/rworkflow/ReUseData VignetteBuilder: knitr BugReports: https://github.com/rworkflow/ReUseData/issues git_url: https://git.bioconductor.org/packages/ReUseData git_branch: devel git_last_commit: 85fc756 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ReUseData_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ReUseData_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ReUseData_1.7.0.tgz vignettes: vignettes/ReUseData/inst/doc/ReUseData_data.html, vignettes/ReUseData/inst/doc/ReUseData_quickStart.html, vignettes/ReUseData/inst/doc/ReUseData_recipe.html vignetteTitles: ReUseDataData, ReUseDataQS, ReUseDataRecipes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReUseData/inst/doc/ReUseData_data.R, vignettes/ReUseData/inst/doc/ReUseData_quickStart.R, vignettes/ReUseData/inst/doc/ReUseData_recipe.R dependencyCount: 125 Package: rexposome Version: 1.29.0 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, ggridges, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, formatR, rmarkdown License: MIT + file LICENSE MD5sum: c5ee8d0c050892cf858b155380a9e660 NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: devel git_last_commit: 16d7082 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rexposome_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rexposome_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rexposome_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rexposome_1.29.0.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome suggestsMe: brgedata dependencyCount: 166 Package: rfaRm Version: 1.19.0 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors, jsonlite Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics, RUnit License: GPL-3 MD5sum: fcb01c2a9926079a4f36b54296e84f60 NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: devel git_last_commit: 7c62429 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rfaRm_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rfaRm_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rfaRm_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rfaRm_1.19.0.tgz vignettes: vignettes/rfaRm/inst/doc/rfaRm.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R dependencyCount: 46 Package: Rfastp Version: 1.17.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 3c15db628600bbcd771efb38f9a27416 NeedsCompilation: yes Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [aut] (ORCID: ), Ji-Dung Luo [ctb] (ORCID: ), Thomas Carroll [cre, aut] (ORCID: ) Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: devel git_last_commit: 0411058 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rfastp_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rfastp_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rfastp_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rfastp_1.17.0.tgz vignettes: vignettes/Rfastp/inst/doc/Rfastp.html vignetteTitles: Rfastp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R dependencyCount: 44 Package: rfPred Version: 1.45.0 Depends: R (>= 3.5.0), methods Imports: utils, GenomeInfoDb, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) MD5sum: c5ffc6986cbd93d12b6230ad08715a4e NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet git_url: https://git.bioconductor.org/packages/rfPred git_branch: devel git_last_commit: 5eac6f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rfPred_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rfPred_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rfPred_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rfPred_1.45.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 41 Package: rGADEM Version: 2.55.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 MD5sum: 559bc8c7899d2ab42685c6463135ac05 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit git_url: https://git.bioconductor.org/packages/rGADEM git_branch: devel git_last_commit: 0bdc9ac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rGADEM_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rGADEM_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGADEM_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGADEM_2.55.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: TCGAWorkflow dependencyCount: 61 Package: rGenomeTracks Version: 1.13.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 7332b173122aba65bd5433f741e5ab7e NeedsCompilation: no Title: Integerated visualization of epigenomic data Description: rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users. biocViews: Software, HiC, Visualization Author: Omar Elashkar [aut, cre] (ORCID: ) Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: devel git_last_commit: c6e4fb5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rGenomeTracks_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rGenomeTracks_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGenomeTracks_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGenomeTracks_1.13.0.tgz vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html vignetteTitles: rGenomeTracks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R dependencyCount: 83 Package: RgnTX Version: 1.9.0 Depends: R (>= 4.2.0) Imports: GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, graphics, IRanges, methods, regioneR, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: b09af6813a26d87f3c4880a3bf308ee3 NeedsCompilation: no Title: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity Description: RgnTX allows the integration of transcriptome annotations so as to model the complex alternative splicing patterns. It supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. It involves functions that do permutaion test for evaluating association between features and transcriptome regions. biocViews: AlternativeSplicing, Sequencing, RNASeq, MethylSeq, Transcription, SplicedAlignment Author: Yue Wang [aut, cre], Jia Meng [aut] Maintainer: Yue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RgnTX git_branch: devel git_last_commit: c0f1208 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RgnTX_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RgnTX_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RgnTX_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RgnTX_1.9.0.tgz vignettes: vignettes/RgnTX/inst/doc/RgnTX.html vignetteTitles: RgnTX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RgnTX/inst/doc/RgnTX.R dependencyCount: 101 Package: rgoslin Version: 1.11.0 Imports: Rcpp (>= 1.0.3), dplyr LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra, BiocManager, stringr, stringi, ggplot2, tibble, lipidr License: MIT + file LICENSE MD5sum: 54f2c94b77e8bc4316ae8d5687a5f247 NeedsCompilation: yes Title: Lipid Shorthand Name Parsing and Normalization Description: The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable. biocViews: Software, Lipidomics, Metabolomics, Preprocessing, Normalization, MassSpectrometry Author: Nils Hoffmann [aut, cre] (ORCID: ), Dominik Kopczynski [aut] (ORCID: ) Maintainer: Nils Hoffmann URL: https://github.com/lifs-tools/rgoslin VignetteBuilder: knitr BugReports: https://github.com/lifs-tools/rgoslin/issues git_url: https://git.bioconductor.org/packages/rgoslin git_branch: devel git_last_commit: f32d084 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rgoslin_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rgoslin_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rgoslin_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rgoslin_1.11.0.tgz vignettes: vignettes/rgoslin/inst/doc/introduction.html vignetteTitles: Using R Goslin to parse and normalize lipid nomenclature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rgoslin/inst/doc/introduction.R suggestsMe: MetMashR dependencyCount: 21 Package: RGraph2js Version: 1.35.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 795fb6d4dcc768fafa8d8c2b167403d2 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: devel git_last_commit: 2bd32df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RGraph2js_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RGraph2js_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RGraph2js_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RGraph2js_1.35.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 11 Package: Rgraphviz Version: 2.51.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 776e346e7b60c800357204f08667b7d4 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: devel git_last_commit: 86e5661 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rgraphviz_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rgraphviz_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rgraphviz_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rgraphviz_2.51.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, maEndToEnd, dlsem, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, bnem, chimeraviz, CytoML, dce, DEGraph, EnrichDO, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, mirIntegrator, MIRit, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, SGCP, TRONCO, abn, agena.ai, BCDAG, BiDAG, bnpa, bnRep, ceg, CePa, classGraph, cogmapr, ontologyPlot, SEMgraph, stablespec suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, OmnipathR, RBGL, RBioinf, rBiopaxParser, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, HEMDAG, iTOP, kpcalg, kst, lava, loon, maGUI, micd, multiplex, ParallelPC, pcalg, pchc, pks, psych, relations, rEMM, rPref, rSpectral, SCCI, sisal, textplot, tm, topologyGSA, tpc, unifDAG, zenplots dependencyCount: 10 Package: rGREAT Version: 2.9.0 Depends: R (>= 4.0.0), GenomicRanges, IRanges, methods Imports: graphics, rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats, GlobalOptions, shiny, DT, GenomicFeatures, digest, GO.db, progress, circlize, AnnotationDbi, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, RColorBrewer, S4Vectors, GenomeInfoDb, foreach, doParallel, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, rmarkdown, BiocManager, org.Mm.eg.db, msigdbr, KEGGREST, reactome.db Enhances: BioMartGOGeneSets, UniProtKeywords License: MIT + file LICENSE MD5sum: 12a82968fb19422ed94efeeb95ce7619 NeedsCompilation: yes Title: GREAT Analysis - Functional Enrichment on Genomic Regions Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: devel git_last_commit: a2abe62 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rGREAT_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rGREAT_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rGREAT_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rGREAT_2.9.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: The rGREAT package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 123 Package: RGSEA Version: 1.41.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 897801f8d580ee6c83e7bda7007e39f1 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: devel git_last_commit: f8d6d9d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RGSEA_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RGSEA_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RGSEA_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RGSEA_1.41.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 6 Package: rgsepd Version: 1.39.0 Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 118bef87bd40e58ee0c40dfd5aa5f974 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: devel git_last_commit: 42b2653 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rgsepd_1.39.0.tar.gz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 129 Package: rhdf5 Version: 2.51.1 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters (>= 1.15.5) LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, bench, dplyr, ggplot2, mockery, BiocParallel License: Artistic-2.0 MD5sum: 7e7a1d05fbb4c6fad5e67c5b8bddfec9 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut, cre] (ORCID: ), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: devel git_last_commit: 5bf9a52 git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-09 source.ver: src/contrib/rhdf5_2.51.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5_2.51.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5_2.51.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5_2.51.1.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, HDF5Array, HiCBricks, LoomExperiment, MuData, octad importsMe: alabaster.base, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.spatial, BayesSpace, BgeeCall, biomformat, bnbc, bsseq, chihaya, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HicAggR, HiCcompare, HiCExperiment, IONiseR, mariner, MOFA2, MoleculeExperiment, phantasus, plotgardener, ptairMS, PureCN, recountmethylation, ribor, scCB2, scMitoMut, scone, scRNAseqApp, signatureSearch, SpliceWiz, SpotClean, TENxIO, trackViewer, MafH5.gnomAD.v4.0.GRCh38, MethylSeqData, ptairData, scMultiome, signatureSearchData, bioRad, ebvcube, file2meco, karyotapR, LOMAR, NEONiso, ondisc, rDataPipeline suggestsMe: beachmat.hdf5, edgeR, HiCDOC, mia, phantasusLite, rhdf5filters, SCArray, scviR, slalom, SpatialFeatureExperiment, spatialHeatmap, Spectra, SummarizedExperiment, tximport, Voyager, zellkonverter, conos, CRMetrics, io, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX, SpatialDDLS dependencyCount: 3 Package: rhdf5client Version: 1.29.0 Depends: R (>= 3.6), methods, DelayedArray Imports: httr, rjson, utils, data.table Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 88bc7040834dce0d95d3b0552d408e1a NeedsCompilation: yes Title: Access HDF5 content from HDF Scalable Data Service Description: This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group. biocViews: DataImport, Software, Infrastructure Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], BJ Stubbs [aut], Alexey Sergushichev [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: devel git_last_commit: 65b8c5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rhdf5client_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5client_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5client_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5client_1.29.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: phantasus, phantasusLite dependencyCount: 33 Package: rhdf5filters Version: 1.19.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, tinytest, rhdf5 (>= 2.47.7) License: BSD_2_clause + file LICENSE MD5sum: 385e987cd992f8fb40364b35b71d04e1 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of additional compression filters for HDF5 datasets. The package is intended to provide seemless integration with rhdf5, however the compiled filters can also be used with external applications. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, cre] (ORCID: ) Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5filters git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: devel git_last_commit: 4db11d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rhdf5filters_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhdf5filters_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5filters_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhdf5filters_1.19.0.tgz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: HDF5Array, rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.29.0 Depends: R (>= 4.2.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 MD5sum: 990d5c9002dde1eb29fd22ca3426127f NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb, cre] (ORCID: ), The HDF Group [cph] Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: devel git_last_commit: d7c4f89 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rhdf5lib_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhdf5lib_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhdf5lib_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhdf5lib_1.29.0.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: epigraHMM, rhdf5 suggestsMe: mbkmeans linksToMe: alabaster.base, beachmat.hdf5, chihaya, CytoML, DropletUtils, epigraHMM, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, ondisc dependencyCount: 0 Package: rhinotypeR Version: 1.1.0 Depends: R (>= 4.4.0) Imports: Biostrings Suggests: knitr, rmarkdown, BiocManager, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: a0e3782802b3b62aa5c8d78de2f7133f NeedsCompilation: no Title: Rhinovirus genotyping Description: "rhinotypeR" is designed to automate the comparison of sequence data against prototype strains, streamlining the genotype assignment process. By implementing predefined pairwise distance thresholds, this package makes genotype assignment accessible to researchers and public health professionals. This tool enhances our epidemiological toolkit by enabling more efficient surveillance and analysis of rhinoviruses (RVs) and other viral pathogens with complex genomic landscapes. Additionally, "rhinotypeR" supports comprehensive visualization and analysis of single nucleotide polymorphisms (SNPs) and amino acid substitutions, facilitating in-depth genetic and evolutionary studies. biocViews: Sequencing, Genetics, Phylogenetics Author: Martha Luka [aut, cre] (ORCID: ), Ruth Nanjala [aut], Winfred Gatua [aut], Wafaa M. Rashed [aut], Olaitan Awe [aut] Maintainer: Martha Luka URL: https://github.com/omicscodeathon/rhinotypeR VignetteBuilder: knitr BugReports: https://github.com/omicscodeathon/rhinotypeR/issues git_url: https://git.bioconductor.org/packages/rhinotypeR git_branch: devel git_last_commit: 4fd8c88 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rhinotypeR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rhinotypeR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rhinotypeR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rhinotypeR_1.1.0.tgz vignettes: vignettes/rhinotypeR/inst/doc/rhinotypeR.html vignetteTitles: Introduction to rhinotypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhinotypeR/inst/doc/rhinotypeR.R dependencyCount: 26 Package: Rhisat2 Version: 1.23.0 Depends: R (>= 4.4.0) Imports: txdbmaker, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: b9bf564b09b72d8b6e7fc850790693f2 NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: devel git_last_commit: 695fc7d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rhisat2_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhisat2_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhisat2_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhisat2_1.23.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R importsMe: CircSeqAlignTk suggestsMe: eisaR, QuasR dependencyCount: 105 Package: Rhtslib Version: 3.3.1 Imports: tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) MD5sum: 50e0d6534ea073394fa9bb89f89a848a NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Hervé Pagès [aut, cre], Tomas Kalibera [ctb], Jeroen Ooms [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: devel git_last_commit: 9373c6f git_last_commit_date: 2024-11-26 Date/Publication: 2024-11-27 source.ver: src/contrib/Rhtslib_3.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rhtslib_3.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rhtslib_3.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rhtslib_3.3.1.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe linksToMe: bamsignals, csaw, deepSNV, DiffBind, diffHic, epialleleR, FLAMES, h5vc, maftools, methylKit, mitoClone2, podkat, QuasR, raer, Rfastp, Rsamtools, scPipe, ShortRead, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboCrypt Version: 1.13.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: bslib, BiocGenerics, BiocParallel, Biostrings, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, httr, IRanges, jsonlite, knitr, markdown, NGLVieweR, plotly, rlang, RCurl, shiny, shinycssloaders, shinyhelper, shinyjqui, stringr Suggests: testthat, rmarkdown, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 0acaf3184ace6789b54d50969d6bc811 NeedsCompilation: no Title: Interactive visualization in genomics Description: R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development. biocViews: Software, Sequencing, RiboSeq, RNASeq, Author: Michal Swirski [aut, cre, cph], Haakon Tjeldnes [aut, ctb], Kornel Labun [ctb] Maintainer: Michal Swirski URL: https://github.com/m-swirski/RiboCrypt VignetteBuilder: knitr BugReports: https://github.com/m-swirski/RiboCrypt/issues git_url: https://git.bioconductor.org/packages/RiboCrypt git_branch: devel git_last_commit: 148e091 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RiboCrypt_1.13.0.tar.gz vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.html, vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html vignetteTitles: RiboCrypt_app_tutorial.html, RiboCrypt_overview.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.R, vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R dependencyCount: 168 Package: RiboDiPA Version: 1.15.1 Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism, methods, S4Vectors, IRanges, GenomicRanges, matrixStats, reldist, doParallel, foreach, parallel, qvalue, DESeq2, ggplot2, BiocFileCache, BiocGenerics, txdbmaker LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: 2805394c91d7c8f3c7cabce394a0bbdb NeedsCompilation: yes Title: Differential pattern analysis for Ribo-seq data Description: This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization. biocViews: RiboSeq, GeneExpression, GeneRegulation, DifferentialExpression, Sequencing, Coverage, Alignment, RNASeq, ImmunoOncology, QualityControl, DataImport, Software, Normalization Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping Wang [aut, cre] Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: devel git_last_commit: 921e372 git_last_commit_date: 2024-12-05 Date/Publication: 2024-12-06 source.ver: src/contrib/RiboDiPA_1.15.1.tar.gz vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html vignetteTitles: RiboDiPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R dependencyCount: 150 Package: RiboProfiling Version: 1.37.0 Depends: R (>= 3.5.0), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 MD5sum: 2400ff8ebc8097aeb28e2b1c4438c231 NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: devel git_last_commit: c7c7bd6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RiboProfiling_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RiboProfiling_1.37.0.zip vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 167 Package: ribor Version: 1.19.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 5c9de7c95e2b68ac4858a2d37dc12b42 NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: devel git_last_commit: 390801c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ribor_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ribor_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ribor_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ribor_1.19.0.tgz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 52 Package: riboSeqR Version: 1.41.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, S4Vectors, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: 17e9dc88d0eff23073dc20bf77b89957 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/riboSeqR BugReports: https://github.com/samgg/riboSeqR/issues git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: devel git_last_commit: f585003 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/riboSeqR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/riboSeqR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/riboSeqR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/riboSeqR_1.41.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 49 Package: ribosomeProfilingQC Version: 1.19.0 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid, txdbmaker, ggExtra Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, edgeR, DESeq2, limma, ashr, testthat, rmarkdown, vsn, Biobase License: GPL (>=3) + file LICENSE Archs: x64 MD5sum: 9513abddef54beb2236eb7d8aa6e2629 NeedsCompilation: no Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (ORCID: ), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: devel git_last_commit: 153ace4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ribosomeProfilingQC_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ribosomeProfilingQC_1.19.0.zip vignettes: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html vignetteTitles: ribosomeProfilingQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R dependencyCount: 191 Package: rifi Version: 1.11.0 Depends: R (>= 4.2) Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, graphics, grDevices, grid, methods, nls2, nnet, rlang, S4Vectors, scales, stats, stringr, SummarizedExperiment, tibble, rtracklayer, reshape2, utils Suggests: DescTools, devtools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: eac8aac73731b331e011e45a080c0a33 NeedsCompilation: no Title: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq Description: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Regression, Microarray, Software Author: Loubna Youssar [aut, ctb], Walja Wanney [aut, ctb], Jens Georg [aut, cre] Maintainer: Jens Georg VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: devel git_last_commit: 73780f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rifi_1.11.0.tar.gz vignettes: vignettes/rifi/inst/doc/vignette.html vignetteTitles: Rifi for decay estimation,, based on high resolution microarray or RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifi/inst/doc/vignette.R dependencyCount: 123 Package: rifiComparative Version: 1.7.0 Depends: R (>= 4.2) Imports: cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, ggrepel, graphics, grDevices, grid, methods, nnet, rlang, S4Vectors, scales, stats, stringr, tibble, rtracklayer, utils, writexl, DTA, LSD, reshape2, devtools, SummarizedExperiment Suggests: DescTools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: 08644eebb0005e8118f6539e571fa90e NeedsCompilation: no Title: 'rifiComparative' compares the output of rifi from two different conditions. Description: 'rifiComparative' is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Microarray, Software Author: Loubna Youssar [aut, cre], Jens cre Georg [aut] Maintainer: Loubna Youssar VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifiComparative git_url: https://git.bioconductor.org/packages/rifiComparative git_branch: devel git_last_commit: 0e250a5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rifiComparative_1.7.0.tar.gz vignettes: vignettes/rifiComparative/inst/doc/rifiComparative.html vignetteTitles: rifiComparative hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifiComparative/inst/doc/rifiComparative.R dependencyCount: 173 Package: RImmPort Version: 1.35.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 029d1a61780f961d46c7d4f2cf3112b6 NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: devel git_last_commit: 804fce5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RImmPort_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RImmPort_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RImmPort_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RImmPort_1.35.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 42 Package: RITAN Version: 1.31.0 Depends: R (>= 4.0), Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86 Suggests: rmarkdown, BgeeDB License: file LICENSE MD5sum: aa61109d4ea33ca850cf01276973ce46 NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics, GraphAndNetwork Author: Michael Zimmermann [aut, cre] Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: devel git_last_commit: e6e8dc4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RITAN_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RITAN_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RITAN_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RITAN_1.31.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 162 Package: RIVER Version: 1.31.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) Archs: x64 MD5sum: e455c047833de584732e5c246f2763b2 NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: devel git_last_commit: 6afdc8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RIVER_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RIVER_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RIVER_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RIVER_1.31.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 48 Package: RJMCMCNucleosomes Version: 1.31.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: ab53922049ec8e02e25204f9f518a852 NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: devel git_last_commit: aa06559 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RJMCMCNucleosomes_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RJMCMCNucleosomes_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RJMCMCNucleosomes_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RJMCMCNucleosomes_1.31.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 69 Package: RLassoCox Version: 1.15.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 Archs: x64 MD5sum: 3d6e3bbc9bd11855e67f99224cf2736f NeedsCompilation: no Title: A reweighted Lasso-Cox by integrating gene interaction information Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. biocViews: Survival, Regression, GeneExpression, GenePrediction, Network Author: Wei Liu [cre, aut] (ORCID: ) Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: devel git_last_commit: 9f0003e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RLassoCox_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RLassoCox_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RLassoCox_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RLassoCox_1.15.0.tgz vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf vignetteTitles: RLassoCox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R dependencyCount: 26 Package: RLMM Version: 1.69.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) Archs: x64 MD5sum: 4697d703ea82a89c35496b149a7ccfa3 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: devel git_last_commit: b3e0ae7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RLMM_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RLMM_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RLMM_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RLMM_1.69.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: Rmagpie Version: 1.63.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 755e0cc6baa62303eb2f9d03a0026ecd NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: devel git_last_commit: cc7cbb8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rmagpie_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rmagpie_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rmagpie_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rmagpie_1.63.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 20 Package: RMassBank Version: 3.17.0 Depends: Rcpp Imports: assertthat, Biobase, ChemmineR, data.table, digest, dplyr, enviPat, glue, httr, httr2, logger, methods, MSnbase, mzR, purrr, R.utils, rcdk, readJDX, readr, rjson, S4Vectors, tibble, tidyselect, webchem, XML, yaml Suggests: BiocStyle, CAMERA, gplots, knitr, magick, rmarkdown, RMassBankData (>= 1.33.1), RUnit, xcms (>= 1.37.1) License: Artistic-2.0 Archs: x64 MD5sum: 1196508cd910156327ea198e8060680c NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, Paul Stahlhofen, Tobias Schulze with contributions of Hendrik Treutler Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: devel git_last_commit: 8e633a1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RMassBank_3.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RMassBank_3.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RMassBank_3.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RMassBank_3.17.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R suggestsMe: RMassBankData dependencyCount: 174 Package: rmelting Version: 1.23.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.9-8) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 Archs: x64 MD5sum: 54b5cca8150dea68ab242948cb25c8e8 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (ORCID: ), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/rmelting/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: devel git_last_commit: ae2bfcf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rmelting_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rmelting_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rmelting_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rmelting_1.23.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.25.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, apeglm, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: e305b16a5953d79a876f5b5862583606 NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: devel git_last_commit: 46d2936 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rmmquant_1.25.0.tar.gz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 186 Package: RNAAgeCalc Version: 1.19.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: b4c6ea7739e6d0bb926896ac37bc554d NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: devel git_last_commit: d15a7d3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-13 source.ver: src/contrib/RNAAgeCalc_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAAgeCalc_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAAgeCalc_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAAgeCalc_1.19.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 170 Package: RNAdecay Version: 1.27.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 Archs: x64 MD5sum: e535ef177169ff43cabf060201d21261 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: devel git_last_commit: 42841c3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAdecay_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAdecay_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAdecay_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAdecay_1.27.0.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 44 Package: rnaEditr Version: 1.17.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: ca3e9611b6bdad96750076d5e6d19ba9 NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: devel git_last_commit: a074827 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rnaEditr_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rnaEditr_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rnaEditr_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rnaEditr_1.17.0.tgz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 137 Package: RNAmodR Version: 1.21.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, txdbmaker, GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 Archs: x64 MD5sum: e09dee3b914edbe5c7d6a1f6a017a2b3 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: devel git_last_commit: 83a9c61 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAmodR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR_1.21.0.zip vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 167 Package: RNAmodR.AlkAnilineSeq Version: 1.21.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, RNAmodR.Data License: Artistic-2.0 Archs: x64 MD5sum: cd8ef38e9f6a7595b7680f8171b885d9 NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: devel git_last_commit: 4d75977 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 168 Package: RNAmodR.ML Version: 1.21.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: 5ebadd980bd152ed8cf31f668f6c6d45 NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: devel git_last_commit: d85435c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAmodR.ML_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.ML_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.ML_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.ML_1.21.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 169 Package: RNAmodR.RiboMethSeq Version: 1.21.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, RNAmodR.Data License: Artistic-2.0 MD5sum: ae5b931ff08580629739d5a72824f888 NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: devel git_last_commit: e2f22fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 168 Package: RNAsense Version: 1.21.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: c328859a40ff2f7be5ea695524eda791 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: devel git_last_commit: e99ce3a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAsense_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAsense_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAsense_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAsense_1.21.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 71 Package: rnaseqcomp Version: 1.37.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: f4e5509d8f416be7fe43d4d167c8f60e NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: devel git_last_commit: b6cc833 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rnaseqcomp_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rnaseqcomp_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rnaseqcomp_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rnaseqcomp_1.37.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R dependencyCount: 2 Package: RNAseqCovarImpute Version: 1.5.0 Depends: R (>= 4.3.0) Imports: Biobase, BiocGenerics, BiocParallel, stats, limma, dplyr, magrittr, rlang, edgeR, foreach, mice Suggests: BiocStyle, knitr, PCAtools, rmarkdown, tidyr, stringr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 88d8a6ca5855aad7053fd1394019e3e9 NeedsCompilation: no Title: Impute Covariate Data in RNA Sequencing Studies Description: The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR). biocViews: RNASeq, GeneExpression, DifferentialExpression, Sequencing Author: Brennan Baker [aut, cre] (ORCID: ), Sheela Sathyanarayana [aut], Adam Szpiro [aut], James MacDonald [aut], Alison Paquette [aut] Maintainer: Brennan Baker URL: https://github.com/brennanhilton/RNAseqCovarImpute VignetteBuilder: knitr BugReports: https://github.com/brennanhilton/RNAseqCovarImpute/issues git_url: https://git.bioconductor.org/packages/RNAseqCovarImpute git_branch: devel git_last_commit: f490cdd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNAseqCovarImpute_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNAseqCovarImpute_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNAseqCovarImpute_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNAseqCovarImpute_1.5.0.tgz vignettes: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.html, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.html vignetteTitles: Example Data for RNAseqCovarImpute, Impute Covariate Data in RNA-sequencing Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.R, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.R dependencyCount: 82 Package: RNASeqPower Version: 1.47.0 License: LGPL (>=2) MD5sum: 6f2c7179a3708f6443a8567b04c92a63 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: devel git_last_commit: 82e3a96 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RNASeqPower_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RNASeqPower_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RNASeqPower_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RNASeqPower_1.47.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R suggestsMe: DGEobj.utils dependencyCount: 0 Package: RnaSeqSampleSize Version: 2.17.0 Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, Rcpp (>= 0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 34c58dfc73b93fc753e81ffdac678954 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo [aut], Quanhu Sheng [aut], Yu Shyr [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: devel git_last_commit: 9a5f28d git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-13 source.ver: src/contrib/RnaSeqSampleSize_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RnaSeqSampleSize_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RnaSeqSampleSize_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RnaSeqSampleSize_2.17.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 203 Package: RnBeads Version: 2.25.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, grid, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19, RnBeads.mm9, RnBeads.hg38, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame License: GPL-3 Archs: x64 MD5sum: dec334a172fd9aa648e1eadcc41b07cb NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: devel git_last_commit: 3ab7825 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RnBeads_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RnBeads_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RnBeads_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RnBeads_2.25.0.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R dependsOnMe: MAGAR suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5 dependencyCount: 171 Package: Rnits Version: 1.41.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: 65ac011aeb41bd6e6a3538aa48c9de4a NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: devel git_last_commit: 4df67b1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rnits_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rnits_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rnits_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rnits_1.41.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 54 Package: roar Version: 1.43.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: e92d2d421e1561343cc66b1136ac2965 NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: devel git_last_commit: 9a977ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/roar_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/roar_1.43.0.zip vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R dependencyCount: 59 Package: roastgsa Version: 1.5.0 Depends: R (>= 4.3.0) Imports: parallel, grDevices, graphics, utils, stats, methods, grid, RColorBrewer, gplots, ggplot2, limma, Biobase Suggests: BiocStyle, knitr, rmarkdown, GSEABenchmarkeR, EnrichmentBrowser, preprocessCore, DESeq2 License: GPL-3 MD5sum: 61d52cacb7adcf9d9ec99d90125ac5e0 NeedsCompilation: no Title: Rotation based gene set analysis Description: This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations. biocViews: Microarray, Preprocessing, Normalization, GeneExpression, Survival, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, Regression, RNASeq, MicroRNAArray, mRNAMicroarray, FunctionalGenomics, SystemsBiology, ImmunoOncology, DifferentialExpression, GeneSetEnrichment, BatchEffect, MultipleComparison, QualityControl, TimeCourse, Metabolomics, Proteomics, Epigenetics, Cheminformatics, ExonArray, OneChannel, TwoChannel, ProprietaryPlatforms, CellBiology, BiomedicalInformatics, AlternativeSplicing, DifferentialSplicing, DataImport, Pathways Author: Adria Caballe [aut, cre] (ORCID: ) Maintainer: Adria Caballe VignetteBuilder: knitr BugReports: https://github.com/adricaba/roastgsa/issues git_url: https://git.bioconductor.org/packages/roastgsa git_branch: devel git_last_commit: 5f94c99 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/roastgsa_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/roastgsa_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/roastgsa_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/roastgsa_1.5.0.tgz vignettes: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.html, vignettes/roastgsa/inst/doc/roastgsaExample_main.html, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.html vignetteTitles: roastgsa vignette (gene set collections), roastgsa vignette (main), roastgsa vignette (RNAseq) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.R, vignettes/roastgsa/inst/doc/roastgsaExample_main.R, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.R dependencyCount: 46 Package: ROC Version: 1.83.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase, BiocStyle License: Artistic-2.0 MD5sum: f65d1a4b2534cba1235dde3172ba2dfe NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: devel git_last_commit: 1857a19 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ROC_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROC_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROC_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROC_1.83.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter dependencyCount: 10 Package: ROCpAI Version: 1.19.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 6519a4feae05766b493b437a7c950d01 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: devel git_last_commit: 15f6984 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-08 source.ver: src/contrib/ROCpAI_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROCpAI_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROCpAI_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROCpAI_1.19.0.tgz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 44 Package: RolDE Version: 1.11.0 Depends: R (>= 4.2.0) Imports: stats, methods, ROTS, matrixStats, foreach, parallel, doParallel, doRNG, rngtools, SummarizedExperiment, nlme, qvalue, grDevices, graphics, utils Suggests: knitr, printr, rmarkdown, testthat License: GPL-3 MD5sum: 9b9d0bf581c0cfc4165d86d6bcd26aa0 NeedsCompilation: no Title: RolDE: Robust longitudinal Differential Expression Description: RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings. biocViews: StatisticalMethod, Software, TimeCourse, Regression, Proteomics, DifferentialExpression Author: Tommi Valikangas [aut], Medical Bioinformatics Centre [cre] Maintainer: Medical Bioinformatics Centre URL: https://github.com/elolab/RolDE VignetteBuilder: knitr BugReports: https://github.com/elolab/RolDE/issues git_url: https://git.bioconductor.org/packages/RolDE git_branch: devel git_last_commit: d2ff8d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RolDE_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RolDE_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RolDE_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RolDE_1.11.0.tgz vignettes: vignettes/RolDE/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RolDE/inst/doc/Introduction.R dependencyCount: 78 Package: rols Version: 3.3.0 Depends: methods Imports: httr2, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: aacd3dea9724f1cb0875b06ccab1e760 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre] (ORCID: ), Tiage Chedraoui Silva [ctb], Andrew Clugston [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.io/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: devel git_last_commit: 7d005fb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rols_3.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rols_3.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rols_3.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rols_3.3.0.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R importsMe: OmicsMLRepoR, struct suggestsMe: MSnbase, spatialHeatmap, RforProteomics dependencyCount: 23 Package: ROntoTools Version: 2.35.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE Archs: x64 MD5sum: ec394cb63a389cef7779b508f9ade8f4 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Sorin Draghici git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: devel git_last_commit: d2b826d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ROntoTools_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROntoTools_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROntoTools_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROntoTools_2.35.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA suggestsMe: RCPA dependencyCount: 36 Package: ropls Version: 1.39.0 Depends: R (>= 3.5.0) Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, phenomis, rmarkdown, testthat License: CeCILL MD5sum: 4d289e5493420191b812db27ce914b28 NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: devel git_last_commit: e7c2554 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ropls_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ropls_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ropls_1.39.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: ropls-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R importsMe: ASICS, biosigner, lipidr, MultiBaC, phenomis suggestsMe: autonomics, ptairMS, structToolbox, MetabolomicsBasics dependencyCount: 105 Package: ROSeq Version: 1.19.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 7e4fca2a827f8717c1cb9c7dd5451621 NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: devel git_last_commit: f033b59 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ROSeq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROSeq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROSeq_1.19.0.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 1.35.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 0a15d44466f4d10a6324ad16d69c8a92 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: devel git_last_commit: 4873e43 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ROTS_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ROTS_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ROTS_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ROTS_1.35.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, PRONE, RolDE suggestsMe: LimROTS, wrProteo dependencyCount: 8 Package: rprimer Version: 1.11.0 Depends: R (>= 4.1) Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods, patchwork, reshape2, S4Vectors, shiny, shinycssloaders, shinyFeedback Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler, testthat (>= 3.0.0) License: GPL-3 MD5sum: a97ca648e8ffff1a4ca43e7a979a6a77 NeedsCompilation: no Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment Description: Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette. biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment, SequenceMatching, qPCR Author: Sofia Persson [aut, cre] (ORCID: ) Maintainer: Sofia Persson URL: https://github.com/sofpn/rprimer VignetteBuilder: knitr BugReports: https://github.com/sofpn/rprimer/issues git_url: https://git.bioconductor.org/packages/rprimer git_branch: devel git_last_commit: 8d5946a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rprimer_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rprimer_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rprimer_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rprimer_1.11.0.tgz vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html vignetteTitles: Instructions for use hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R dependencyCount: 94 Package: RProtoBufLib Version: 2.19.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: 8f0335d0f6de6903b2d03a073cbb9439 NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: devel git_last_commit: 1d936fb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RProtoBufLib_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RProtoBufLib_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RProtoBufLib_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RProtoBufLib_2.19.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: rpx Version: 2.15.0 Depends: R (>= 3.5.0), methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, tibble, rmarkdown License: GPL-2 MD5sum: f91238814bda39318921714c95d86c0c NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: devel git_last_commit: 5be79a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rpx_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rpx_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rpx_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rpx_2.15.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MsExperiment, MSnbase, PSMatch, RforProteomics dependencyCount: 49 Package: Rqc Version: 1.41.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 63bfd0476d416c03ea7594937c3d2dd5 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: devel git_last_commit: beb1258 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rqc_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rqc_1.41.0.zip vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 160 Package: rqt Version: 1.33.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 9b1294cd5c100666fddf564946205c66 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: Ilya Zhbannikov [aut, cre], Konstantin Arbeev [aut], Anatoliy Yashin [aut] Maintainer: Ilya Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: devel git_last_commit: 4366457 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rqt_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rqt_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rqt_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rqt_1.33.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 155 Package: rqubic Version: 1.53.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 59a86d704299c0aa8db365f120c6637b NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] (ORCID: ) Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: devel git_last_commit: 0a9a982 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rqubic_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rqubic_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rqubic_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rqubic_1.53.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM suggestsMe: RcmdrPlugin.BiclustGUI dependencyCount: 53 Package: rRDP Version: 1.41.0 Depends: Biostrings (>= 2.26.2) Imports: rJava, utils Suggests: rRDPData, knitr, rmarkdown License: GPL-2 + file LICENSE Archs: x64 MD5sum: 23c31bcb96d9c306c2d25e10710ffd95 NeedsCompilation: no Title: Interface to the RDP Classifier Description: This package installs and interfaces the naive Bayesian classifier for 16S rRNA sequences developed by the Ribosomal Database Project (RDP). With this package the classifier trained with the standard training set can be used or a custom classifier can be trained. biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology, Alignment, SequenceMatching, DataImport, Bayesian Author: Michael Hahsler [aut, cre] (ORCID: ), Nagar Anurag [aut] Maintainer: Michael Hahsler URL: https://github.com/mhahsler/rRDP/ SystemRequirements: Java JDK 1.4 or higher VignetteBuilder: knitr BugReports: https://github.com/mhahsler/rRDP/issues git_url: https://git.bioconductor.org/packages/rRDP git_branch: devel git_last_commit: 8b3502c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rRDP_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rRDP_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rRDP_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rRDP_1.41.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.html vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 27 Package: RRHO Version: 1.47.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: afeddd5993538e91afdddffe4833cbc6 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: devel git_last_commit: 5fc347a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RRHO_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RRHO_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RRHO_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RRHO_1.47.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 8 Package: rrvgo Version: 1.19.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods, umap Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: d2f69ff215e2f36060a5cdb4185f159d NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre], Sara Elmeligy [ctb] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: devel git_last_commit: b7ae543 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rrvgo_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rrvgo_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rrvgo_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rrvgo_1.19.0.tgz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R suggestsMe: genekitr, scDiffCom dependencyCount: 112 Package: Rsamtools Version: 2.23.1 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6), R (>= 3.5.0) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 3.3.1), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle, knitr License: Artistic-2.0 | file LICENSE MD5sum: 3e389a59ca230f6e8438a5e054252c84 NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan [aut], Hervé Pagès [aut], Valerie Obenchain [aut], Nathaniel Hayden [aut], Busayo Samuel [ctb] (Converted Rsamtools vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: devel git_last_commit: 21a5945 git_last_commit_date: 2024-11-26 Date/Publication: 2024-11-27 source.ver: src/contrib/Rsamtools_2.23.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rsamtools_2.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rsamtools_2.23.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rsamtools_2.23.1.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.html vignetteTitles: An Introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: CODEX, CoverageView, esATAC, FRASER, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, RepViz, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, systemPipeR, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook importsMe: alabaster.files, alabaster.vcf, AllelicImbalance, AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R, ASpli, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BSgenome, CAGEr, casper, CellBarcode, cellbaseR, CexoR, cfdnakit, cfDNAPro, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromVAR, CircSeqAlignTk, CleanUpRNAseq, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, Damsel, DegNorm, derfinder, DiffBind, diffHic, DNAfusion, easyRNASeq, EDASeq, ensembldb, epigenomix, epigraHMM, eudysbiome, extraChIPs, FilterFFPE, FLAMES, gcapc, gDNAx, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenomicPlot, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, icetea, IMAS, INSPEcT, karyoploteR, MADSEQ, magpie, MDTS, metagene2, metaseqR2, methylKit, mosaics, motifmatchr, MotifPeeker, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICS, plyranges, pram, profileplyr, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, raer, ramwas, Rbowtie2, recoup, Repitools, rfPred, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, Rqc, rtracklayer, scDblFinder, scPipe, scRNAseqApp, scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, soGGi, SplicingGraphs, strandCheckR, tadar, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, ZygosityPredictor, chipseqDBData, gDNAinRNAseqData, LungCancerLines, raerdata, GenoPop, hoardeR, iimi, kibior, MAAPER, MicroSEC, NIPTeR, noisyr, PlasmaMutationDetector, PlasmaMutationDetector2, revert, scPloidy, Signac, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, BSgenomeForge, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, HIBAG, igvShiny, IRanges, ldblock, MOSim, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, SigFuge, similaRpeak, Streamer, TENxIO, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, systemPipeRdata, chipseqDB, karyotapR, polyRAD, seqmagick dependencyCount: 39 Package: rsbml Version: 2.65.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: 891ce47c563fb5033c04d64a6ce882e4 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: devel git_last_commit: b523cb6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rsbml_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rsbml_2.65.0.zip vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR, seeds dependencyCount: 8 Package: rScudo Version: 1.23.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 Archs: x64 MD5sum: fb0aeb530b3312b3d1b485b3c9f5c10e NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: devel git_last_commit: 7918e62 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rScudo_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rScudo_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rScudo_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rScudo_1.23.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 48 Package: rsemmed Version: 1.17.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 6ca8be5762f62ea6d410090d226b4422 NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] (ORCID: ) Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: devel git_last_commit: 7e5464b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rsemmed_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rsemmed_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rsemmed_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rsemmed_1.17.0.tgz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 29 Package: RSeqAn Version: 1.27.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: ff90931ef09984e6a7fb2742085eee49 NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: devel git_last_commit: 65879b2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RSeqAn_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RSeqAn_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RSeqAn_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RSeqAn_1.27.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R importsMe: qckitfastq linksToMe: qckitfastq dependencyCount: 3 Package: Rsubread Version: 2.21.1 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: d6c8e5db587fe6a3d63e28a8ee35fe32 NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: devel git_last_commit: 120f70e git_last_commit_date: 2024-10-31 Date/Publication: 2024-11-01 source.ver: src/contrib/Rsubread_2.21.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rsubread_2.21.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rsubread_2.21.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rsubread_2.21.1.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, CleanUpRNAseq, Damsel, dupRadar, FRASER, ribosomeProfilingQC, scPipe, scruff suggestsMe: autonomics, icetea, singleCellTK, SpliceWiz, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.47.0 Depends: R (>= 3.5.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer, pwalign License: LGPL-3 MD5sum: d077a175a5a9c2d40e5faf851a330845 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: devel git_last_commit: ad6b2ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RSVSim_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RSVSim_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RSVSim_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RSVSim_1.47.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 64 Package: rSWeeP Version: 1.19.1 Depends: foreach, doParallel, parallel, Biostrings, methods, utils Imports: tools, stringi, Suggests: Rtsne, ape, Seurat, knitr, rmarkdown, tictoc, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: d66f0e4fb04ed1f5785fdad8d2850277 NeedsCompilation: no Title: Spaced Words Projection (SWeeP) Description: "Spaced Words Projection (SWeeP)" is a method for representing biological sequences using vectors preserving inter-sequence comparability. Author: Camila Pereira Perico [com, cre, aut, cph] (ORCID: ), Danrley Rafael Fernandes [aut], Mariane Gonçalves Kulik [aut] (ORCID: ), Júlia Formighieri Varaschin [aut], Camilla Reginatto de Pierri [aut] (ORCID: ), Ricardo Assunção Vialle [aut] (ORCID: ), Roberto Tadeu Raittz [aut, cph] (ORCID: ) Maintainer: Camila P Perico VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: devel git_last_commit: 51429a6 git_last_commit_date: 2024-11-25 Date/Publication: 2024-11-26 source.ver: src/contrib/rSWeeP_1.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/rSWeeP_1.19.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rSWeeP_1.19.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rSWeeP_1.19.1.tgz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 32 Package: RTCA Version: 1.59.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 148b27482fa6435e9a34a6cee40bdbd9 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: devel git_last_commit: 1c8301c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTCA_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCA_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCA_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCA_1.59.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTCGA Version: 1.37.0 Depends: R (>= 3.3.0) Imports: XML, RCurl, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales, rmarkdown, htmltools Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, magrittr, tidyr License: GPL-2 Archs: x64 MD5sum: 29b7a979de36840d494a516e147d816f NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq, Survival, DNAMethylation, PrincipalComponent, Visualization Author: Marcin Kosinski [aut, cre], Przemyslaw Biecek [ctb], Witold Chodor [ctb] Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: devel git_last_commit: e6e4091 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTCGA_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCGA_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCGA_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCGA_1.37.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGA/inst/doc/RTCGA_Workflow.R dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA importsMe: TDbasedUFEadv dependencyCount: 128 Package: RTCGAToolbox Version: 2.37.2 Depends: R (>= 4.3.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, methods, RaggedExperiment, RCurl, RJSONIO, rvest, S4Vectors, stats, stringr, SummarizedExperiment, TCGAutils, utils Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: GPL-2 MD5sum: 51d174604abfe5d4a9e1b7c9a9457782 NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre] (ORCID: ), Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: devel git_last_commit: 010a548 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-19 source.ver: src/contrib/RTCGAToolbox_2.37.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTCGAToolbox_2.37.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTCGAToolbox_2.37.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTCGAToolbox_2.37.2.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData, TCGAWorkflow suggestsMe: TCGAutils dependencyCount: 108 Package: RTN Version: 2.31.0 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: ebcaf079780459340d47c45752df3fa0 NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: devel git_last_commit: 84b09ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTN_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTN_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTN_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTN_2.31.0.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 137 Package: RTNduals Version: 1.31.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: c883c1a7ddb2f04ec02154efc7b6d3c1 NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: devel git_last_commit: 2df8a76 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTNduals_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTNduals_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTNduals_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTNduals_1.31.0.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 138 Package: RTNsurvival Version: 1.31.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9bbe8d4a04eedea402bce7ca0f3b63f9 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: devel git_last_commit: d6d7932 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTNsurvival_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTNsurvival_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTNsurvival_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTNsurvival_1.31.0.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 142 Package: RTopper Version: 1.53.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE Archs: x64 MD5sum: 4078c0d792cb94cdada8f03f4c254d0d NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: devel git_last_commit: 3a32b4a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RTopper_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RTopper_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RTopper_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RTopper_1.53.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 18 Package: Rtpca Version: 1.17.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown License: GPL-3 MD5sum: b3b2845f8e664bcd06dd2528fc418222 NeedsCompilation: no Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: devel git_last_commit: 28c7a10 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rtpca_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rtpca_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rtpca_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rtpca_1.17.0.tgz vignettes: vignettes/Rtpca/inst/doc/Rtpca.html vignetteTitles: Introduction to Rtpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R dependencyCount: 50 Package: rtracklayer Version: 1.67.0 Depends: R (>= 3.5.0), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, curl, httr, Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO, tools, restfulr (>= 0.0.13) LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE Archs: x64 MD5sum: 3c266720f7edeab3457dba383e6d6a27 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: devel git_last_commit: 1ab3e92 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rtracklayer_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rtracklayer_1.67.0.zip vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CAGEfightR, CoverageView, CSSQ, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro importsMe: AnnotationHubData, annotatr, APAlyzer, ATACseqQC, ATACseqTFEA, ballgown, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, branchpointer, BSgenomeForge, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, circRNAprofiler, cliProfiler, CNEr, consensusSeekeR, conumee, crisprDesign, customProDB, derfinder, DEScan2, diffHic, DMCFB, DMCHMM, dmrseq, DuplexDiscovereR, easylift, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions, erma, esATAC, extraChIPs, factR, fcScan, FindIT2, FLAMES, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, GenomicPlot, ggbio, gmapR, gmoviz, goseq, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HicAggR, hicVennDiagram, HiTC, icetea, igvR, INSPEcT, karyoploteR, m6Aboost, MADSEQ, magpie, maser, MEDIPS, metagene2, metaseqR2, methrix, methylKit, mobileRNA, Moonlight2R, motifbreakR, MotifDb, MotifPeeker, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, raer, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RiboProfiling, ribosomeProfilingQC, rifi, rifiComparative, RNAmodR, roar, scanMiRApp, scDblFinder, scPipe, scRNAseqApp, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, signeR, SigsPack, sitadela, soGGi, SpliceWiz, syntenet, TEKRABber, TFBSTools, tidyCoverage, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, txdbmaker, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, raerdata, spatialLIBD, seqpac, SingscoreAMLMutations, crispRdesignR, GALLO, geneHapR, kibior, locuszoomr, PlasmaMutationDetector, PlasmaMutationDetector2, valr suggestsMe: alabaster.files, AnnotationHub, autonomics, BiocFileCache, biovizBase, BREW3R.r, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, crisprViz, DAMEfinder, DiffBind, eisaR, epistack, epivizrChart, epivizrData, FRASER, G4SNVHunter, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, GenomicRanges, gwascat, HiCExperiment, HiContacts, igvShiny, InPAS, interactiveDisplay, megadepth, methylumi, miRBaseConverter, motifTestR, MutationalPatterns, NanoMethViz, OrganismDbi, PICS, PING, pipeFrame, plotgardener, plyinteractions, pqsfinder, ProteoDisco, R453Plus1Toolbox, RcisTarget, rGADEM, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, similaRpeak, systemPipeR, TAPseq, TCGAutils, transmogR, triplex, tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, excluderanges, FDb.FANTOM4.promoters.hg19, fourDNData, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, systemPipeRdata, chipseqDB, gkmSVM, MOCHA, Rgff, RTIGER, Seurat, Signac dependencyCount: 58 Package: Rtreemix Version: 1.69.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: d87f0f499a0f0d4fb0a4b561ecd79408 NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: devel git_last_commit: 45c317f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rtreemix_1.69.0.tar.gz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R dependencyCount: 78 Package: rTRM Version: 1.45.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: methods, AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9029f178dc4dca2ca2be81736537e9e1 NeedsCompilation: no Title: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: devel git_last_commit: 241c6db git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rTRM_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rTRM_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rTRM_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rTRM_1.45.0.tgz vignettes: vignettes/rTRM/inst/doc/Introduction.html vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/Introduction.R importsMe: rTRMui dependencyCount: 51 Package: rTRMui Version: 1.45.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 8261be29e8701fd4cecb0b6fead07a2f NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: devel git_last_commit: e21de74 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rTRMui_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rTRMui_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rTRMui_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rTRMui_1.45.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 103 Package: runibic Version: 1.29.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE MD5sum: 3bb6297d3f146fd1827d0c4743862522 NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: devel git_last_commit: c55baab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/runibic_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/runibic_1.29.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: mosbi dependencyCount: 92 Package: RUVcorr Version: 1.39.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 Archs: x64 MD5sum: 71c7c69f04bfe8c160f8a295abbcdb83 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: devel git_last_commit: 1f72b15 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RUVcorr_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVcorr_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVcorr_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVcorr_1.39.0.tgz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 42 Package: RUVnormalize Version: 1.41.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 Archs: x64 MD5sum: 4322c131c11e0c9eb066821553c7e0c1 NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: devel git_last_commit: 83c9967 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RUVnormalize_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVnormalize_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVnormalize_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVnormalize_1.41.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 8 Package: RUVSeq Version: 1.41.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: c419115f4bb03e3c06fe33497e7aa8ae NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: devel git_last_commit: bb9c230 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RUVSeq_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RUVSeq_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RUVSeq_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RUVSeq_1.41.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.html vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: octad, rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone, standR suggestsMe: DEScan2, NanoTube dependencyCount: 122 Package: Rvisdiff Version: 1.5.0 Depends: R (>= 4.3.0) Imports: edgeR, utils Suggests: knitr, rmarkdown, DESeq2, limma, SummarizedExperiment, airway, BiocStyle, matrixTests, BiocManager License: GPL-2 | GPL-3 MD5sum: 91f4e65d23330fd180eaad907f9486a5 NeedsCompilation: no Title: Interactive Graphs for Differential Expression Description: Creates a muti-graph web page which allows the interactive exploration of differential expression results. The graphical web interface presents results as a table which is integrated with five interactive graphs: MA-plot, volcano plot, box plot, lines plot and cluster heatmap. Graphical aspect and information represented in the graphs can be customized by means of user controls. Final graphics can be exported as PNG format. biocViews: Software, Visualization, RNASeq, DataRepresentation, DifferentialExpression Author: Carlos Prieto [aut] (ORCID: ), David Barrios [cre, aut] (ORCID: ) Maintainer: David Barrios URL: https://github.com/BioinfoUSAL/Rvisdiff/ VignetteBuilder: knitr BugReports: https://github.com/BioinfoUSAL/Rvisdiff/issues/ git_url: https://git.bioconductor.org/packages/Rvisdiff git_branch: devel git_last_commit: 5134d02 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Rvisdiff_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Rvisdiff_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Rvisdiff_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Rvisdiff_1.5.0.tgz vignettes: vignettes/Rvisdiff/inst/doc/Rvisdiff.html vignetteTitles: Visualize Differential Expression results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rvisdiff/inst/doc/Rvisdiff.R dependencyCount: 11 Package: RVS Version: 1.29.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils, R.utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 2071741707b26df490c2cb738fdd824c NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Alexandre Bureau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: devel git_last_commit: b6690b7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/RVS_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/RVS_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RVS_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/RVS_1.29.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 62 Package: rWikiPathways Version: 1.27.0 Imports: httr, utils, XML, rjson, data.table, RCurl, dplyr, tidyr, readr, stringr, purrr, lubridate Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 0ab514c1aae8ccbe3bd371efb305a7d2 NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (ORCID: ), Alex Pico [aut] (ORCID: ) Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: devel git_last_commit: 75875b0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/rWikiPathways_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/rWikiPathways_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/rWikiPathways_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/rWikiPathways_1.27.0.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: famat, RVA suggestsMe: TRONCO dependencyCount: 51 Package: S4Arrays Version: 1.7.1 Depends: R (>= 4.3.0), methods, Matrix, abind, BiocGenerics (>= 0.45.2), S4Vectors, IRanges Imports: stats, crayon LinkingTo: S4Vectors Suggests: BiocParallel, SparseArray (>= 0.0.4), DelayedArray, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 9cd020f5465114161c34fed172e1d496 NeedsCompilation: yes Title: Foundation of array-like containers in Bioconductor Description: The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Jacques Serizay [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/S4Arrays VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/S4Arrays/issues git_url: https://git.bioconductor.org/packages/S4Arrays git_branch: devel git_last_commit: 7081384 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/S4Arrays_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/S4Arrays_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/S4Arrays_1.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/S4Arrays_1.7.1.tgz vignettes: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.html vignetteTitles: A quick overview of the S4Arrays package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.R dependsOnMe: DelayedArray, SparseArray importsMe: alabaster.matrix, DelayedTensor, dreamlet, HDF5Array, scran, scuttle, SummarizedExperiment suggestsMe: BiocGenerics dependencyCount: 15 Package: S4Vectors Version: 0.45.2 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.53.2) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 6aa813bf92975045e46151c5fa74b0de NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. 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FlowSorted.Blood.EPIC, fourDNData, HCATonsilData, HighlyReplicatedRNASeq, HMP16SData, homosapienDEE2CellScore, imcdatasets, leeBamViews, LegATo, MerfishData, MetaGxPancreas, MethylSeqData, MicrobiomeBenchmarkData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scMultiome, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, TransOmicsData, tuberculosis, GeoMxWorkflows, seqpac, crispRdesignR, DR.SC, driveR, genBaRcode, geno2proteo, hoardeR, imcExperiment, karyotapR, LoopRig, MetAlyzer, microbial, MOCHA, multimedia, NIPTeR, oncoPredict, PlasmaMutationDetector, PlasmaMutationDetector2, restfulr, rliger, rnaCrosslinkOO, rsolr, SC.MEB, SCRIP, scROSHI, Signac, SpatialDDLS, TaxaNorm, toxpiR suggestsMe: AlpsNMR, ANCOMBC, BiocGenerics, chihaya, dearseq, epiregulon.extra, epivizrChart, GeoTcgaData, globalSeq, GWASTools, GWENA, gypsum, hca, koinar, maftools, martini, MicrobiotaProcess, MsQuality, MungeSumstats, RTCGA, scFeatures, SpectraQL, SPOTlight, TFEA.ChIP, TFutils, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, BioPlex, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, gkmSVM, grandR, LorMe, MARVEL, pmartR, polyRAD, RCPA, Rgff, Seurat, SNPassoc, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, S4Arrays, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: safe Version: 3.47.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: 0c8ab09aca4cf66fe15fa1133cc07479 NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/safe git_branch: devel git_last_commit: 540d0be git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/safe_3.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/safe_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/safe_3.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/safe_3.47.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R importsMe: EGSEA, EnrichmentBrowser suggestsMe: ReporterScore dependencyCount: 47 Package: sagenhaft Version: 1.77.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: 2ff10079b2ff6cd3fd84649f68c06ab0 NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: devel git_last_commit: ac3f6cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sagenhaft_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sagenhaft_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sagenhaft_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sagenhaft_1.77.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAIGEgds Version: 2.7.1 Depends: R (>= 3.5.0), gdsfmt (>= 1.28.0), SeqArray (>= 1.43.7), Rcpp Imports: methods, stats, utils, Matrix, RcppParallel, CompQuadForm, survey LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, markdown, rmarkdown, crayon, SNPRelate, RUnit, knitr, ggmanh, BiocGenerics License: GPL-3 MD5sum: 4aab8529d6eff7422953064aca35cf20 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package. biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation Author: Xiuwen Zheng [aut, cre] (ORCID: ), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: devel git_last_commit: 1457cda git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/SAIGEgds_2.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SAIGEgds_2.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SAIGEgds_2.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SAIGEgds_2.7.1.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial (single variant tests) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 44 Package: sampleClassifier Version: 1.31.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: 83e50c291d4ab2de44617d8eccfab3d6 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: devel git_last_commit: 22a67e8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sampleClassifier_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sampleClassifier_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sampleClassifier_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sampleClassifier_1.31.0.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R dependencyCount: 94 Package: SamSPECTRAL Version: 1.61.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: 5d40547650b7b01fdcd313ba7c2a32f4 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: devel git_last_commit: 3146807 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SamSPECTRAL_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SamSPECTRAL_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SamSPECTRAL_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SamSPECTRAL_1.61.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.17.0 Depends: R (>= 4.0.0), stringr, ape, Biostrings, pwalign, DECIPHER, parallel, reshape2, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle, logger Suggests: testthat (>= 2.1.0) License: GPL-2 MD5sum: 7f682a2696f551bd26b301e5c190e362 NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: devel git_last_commit: dca5e3f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sangeranalyseR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sangeranalyseR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sangeranalyseR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sangeranalyseR_1.17.0.tgz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 123 Package: sangerseqR Version: 1.43.0 Depends: R (>= 3.5.0), Biostrings, pwalign, stringr Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 23890fe8f11aeea3bce8d904a614c929 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: devel git_last_commit: 1d424d9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sangerseqR_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sangerseqR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sangerseqR_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sangerseqR_1.43.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.html vignetteTitles: Using the sangerseqR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.R dependsOnMe: sangeranalyseR importsMe: scifer suggestsMe: CrispRVariants dependencyCount: 56 Package: SANTA Version: 2.43.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: 46356e3b0def6b3bf686f2188e26abe8 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: devel git_last_commit: 95b5b44 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SANTA_2.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SANTA_2.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SANTA_2.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SANTA_2.43.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 17 Package: SARC Version: 1.5.0 Depends: R (>= 4.3), RaggedExperiment, GenomicRanges Imports: tidyverse, utils, reshape2, DescTools, metap, multtest, plyranges, data.table, scales, RColorBrewer, grid, gtable, gridExtra, GenomicFeatures, stats, ggplot2, plotly, IRanges Suggests: knitr, kableExtra, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene, Homo.sapiens, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, GenomicAlignments License: GPL-3 Archs: x64 MD5sum: fa87be9e1a66c1c40d0cc98443573eb9 NeedsCompilation: no Title: Statistical Analysis of Regions with CNVs Description: Imports a cov/coverage file (normalised read coverages from BAM files) and a cnv file (list of CNVs - similiar to a BED file) from WES/ WGS CNV (copy number variation) detection pipelines and utilises several metrics to weigh the likelihood of a sample containing a detected CNV being a true CNV or a false positive. Highly useful for diagnostic testing to filter out false positives to provide clinicians with fewer variants to interpret. SARC uniquely only used cov and csv (similiar to BED file) files which are the common CNV pipeline calling filetypes, and can be used as to supplement the Interactive Genome Browser (IGV) to generate many figures automatedly, which can be especially helpful in large cohorts with 100s-1000s of patients. biocViews: Software, CopyNumberVariation, Visualization, DNASeq, Sequencing Author: Krutik Patel [aut, cre] (ORCID: ) Maintainer: Krutik Patel URL: https://github.com/Krutik6/SARC/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/SARC/issues git_url: https://git.bioconductor.org/packages/SARC git_branch: devel git_last_commit: 4be56bd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SARC_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SARC_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SARC_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SARC_1.5.0.tgz vignettes: vignettes/SARC/inst/doc/SARC_guide.html vignetteTitles: SARC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SARC/inst/doc/SARC_guide.R dependencyCount: 205 Package: sarks Version: 1.19.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE MD5sum: ec8b6b869c4c55806f16bae283cffbf5 NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] (ORCID: ) Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: devel git_last_commit: 8b05f9a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sarks_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sarks_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sarks_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sarks_1.19.0.tgz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 29 Package: satuRn Version: 1.15.0 Depends: R (>= 4.1) Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply, ggplot2, boot, Matrix, stats, methods, graphics Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub, ensembldb, edgeR, DEXSeq, stageR, DelayedArray License: Artistic-2.0 MD5sum: c538b068466c14b0f421b4a907a18d89 NeedsCompilation: no Title: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications Description: satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. biocViews: Regression, ExperimentalDesign, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb] Maintainer: Jeroen Gilis URL: https://github.com/statOmics/satuRn VignetteBuilder: knitr BugReports: https://github.com/statOmics/satuRn/issues git_url: https://git.bioconductor.org/packages/satuRn git_branch: devel git_last_commit: 6656389 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/satuRn_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/satuRn_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/satuRn_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/satuRn_1.15.0.tgz vignettes: vignettes/satuRn/inst/doc/Vignette.html vignetteTitles: satuRn - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/satuRn/inst/doc/Vignette.R dependencyCount: 78 Package: SBGNview Version: 1.21.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 Archs: x64 MD5sum: e80d51f565493e454e073c268e3db930 NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: devel git_last_commit: 248ac8b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SBGNview_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SBGNview_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SBGNview_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SBGNview_1.21.0.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 89 Package: SBMLR Version: 2.3.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: a56487b538dee1225e21dd31a3355e7a NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: devel git_last_commit: 7e71177 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SBMLR_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SBMLR_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SBMLR_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SBMLR_2.3.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.35.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater, BiocStyle License: GPL-3 MD5sum: 5bc336f780e90a61635688a015b222a1 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: devel git_last_commit: 9b5ac20 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SC3_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SC3_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SC3_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SC3_1.35.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R importsMe: FEAST suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs dependencyCount: 109 Package: Scale4C Version: 1.29.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: feeca4ff7235ffecec6226e825efccc1 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: devel git_last_commit: c88a0fa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Scale4C_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Scale4C_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Scale4C_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Scale4C_1.29.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 38 Package: ScaledMatrix Version: 1.15.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular, DelayedMatrixStats License: GPL-3 MD5sum: ce1828b0aa19967ba5db44f3e28a5da2 NeedsCompilation: no Title: Creating a DelayedMatrix of Scaled and Centered Values Description: Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. biocViews: Software, DataRepresentation Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ScaledMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ScaledMatrix/issues git_url: https://git.bioconductor.org/packages/ScaledMatrix git_branch: devel git_last_commit: 5ea7d67 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ScaledMatrix_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ScaledMatrix_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ScaledMatrix_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ScaledMatrix_1.15.0.tgz vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html vignetteTitles: Using the ScaledMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R importsMe: batchelor, BiocSingular, mumosa, scPCA suggestsMe: scran dependencyCount: 23 Package: SCAN.UPC Version: 2.49.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 28452d3f033b4e90e8fc17dcfefb1a67 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: devel git_last_commit: 5f6ff63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCAN.UPC_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCAN.UPC_2.49.0.zip vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 120 Package: scanMiR Version: 1.13.0 Depends: R (>= 4.0) Imports: Biostrings, pwalign, GenomicRanges, IRanges, data.table, BiocParallel, methods, GenomeInfoDb, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, seqLogo, cowplot Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 7c15a0c7ef7e45612fa26ac6b60c9a41 NeedsCompilation: no Title: scanMiR Description: A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment). biocViews: miRNA, SequenceMatching, Alignment Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: devel git_last_commit: 9306dd6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/scanMiR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scanMiR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scanMiR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scanMiR_1.13.0.tgz vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html, vignettes/scanMiR/inst/doc/scanning.html vignetteTitles: 2_Kdmodels, 1_scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R dependsOnMe: scanMiRApp importsMe: scanMiRData dependencyCount: 71 Package: scanMiRApp Version: 1.13.0 Depends: R (>= 4.0), scanMiR Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiRData, shiny, shinycssloaders, shinydashboard, shinyjqui, stats, utils, txdbmaker, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 Archs: x64 MD5sum: fc63d657b2d1a08345045f3d5c62fa03 NeedsCompilation: no Title: scanMiR shiny application Description: A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs. biocViews: miRNA, SequenceMatching, GUI, ShinyApps Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiRApp git_branch: devel git_last_commit: 09a6f91 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scanMiRApp_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scanMiRApp_1.13.0.zip vignettes: vignettes/scanMiRApp/inst/doc/IndexedFST.html, vignettes/scanMiRApp/inst/doc/scanMiRApp.html vignetteTitles: IndexedFst, scanMiRApp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scanMiRApp/inst/doc/IndexedFST.R, vignettes/scanMiRApp/inst/doc/scanMiRApp.R dependencyCount: 161 Package: scAnnotatR Version: 1.13.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE Archs: x64 MD5sum: e462b9df07b0b2f50a12071af007c17a NeedsCompilation: no Title: Pretrained learning models for cell type prediction on single cell RNA-sequencing data Description: The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs. biocViews: SingleCell, Transcriptomics, GeneExpression, SupportVectorMachine, Classification, Software Author: Vy Nguyen [aut] (ORCID: ), Johannes Griss [cre] (ORCID: ) Maintainer: Johannes Griss URL: https://github.com/grisslab/scAnnotatR VignetteBuilder: knitr BugReports: https://github.com/grisslab/scAnnotatR/issues/new git_url: https://git.bioconductor.org/packages/scAnnotatR git_branch: devel git_last_commit: 108d23b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scAnnotatR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scAnnotatR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scAnnotatR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scAnnotatR_1.13.0.tgz vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html, vignettes/scAnnotatR/inst/doc/training-basic-model.html, vignettes/scAnnotatR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R, vignettes/scAnnotatR/inst/doc/training-basic-model.R, vignettes/scAnnotatR/inst/doc/training-child-model.R suggestsMe: scAnnotatR.models dependencyCount: 215 Package: SCANVIS Version: 1.21.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 6084e5a2a753e55a9132919ecf8cb73d NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: devel git_last_commit: 2740017 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCANVIS_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCANVIS_1.21.0.zip vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 60 Package: SCArray Version: 1.15.1 Depends: R (>= 3.5.0), gdsfmt (>= 1.36.0), methods, DelayedArray (>= 0.31.5) Imports: S4Vectors, utils, Matrix, SparseArray (>= 1.5.13), BiocParallel, DelayedMatrixStats, SummarizedExperiment, SingleCellExperiment, BiocSingular Suggests: BiocGenerics, scater, scuttle, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 MD5sum: f48a81d0afe749ccdf5203c09bd59859 NeedsCompilation: yes Title: Large-scale single-cell omics data manipulation with GDS files Description: Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language. biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: devel git_last_commit: 19daafd git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/SCArray_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCArray_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCArray_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCArray_1.15.1.tgz vignettes: vignettes/SCArray/inst/doc/Overview.html, vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: Overview, Single-cell RNA-seq data manipulation using GDS files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray/inst/doc/SCArray.R dependsOnMe: SCArray.sat dependencyCount: 58 Package: SCArray.sat Version: 1.7.0 Depends: methods, SCArray (>= 1.13.1), SeuratObject (>= 5.0), Seurat (>= 5.0) Imports: S4Vectors, utils, stats, BiocGenerics, BiocParallel, gdsfmt, DelayedArray, BiocSingular, SummarizedExperiment, Matrix Suggests: future, RUnit, knitr, markdown, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 288ad9bf606def40bb2f92ba8fb56f4b NeedsCompilation: no Title: Large-scale single-cell RNA-seq data analysis using GDS files and Seurat Description: Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets. biocViews: DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ), Seurat contributors [ctb] (for the classes and methods defined in Seurat) Maintainer: Xiuwen Zheng VignetteBuilder: knitr BugReports: https://github.com/AbbVie-ComputationalGenomics/SCArray/issues git_url: https://git.bioconductor.org/packages/SCArray.sat git_branch: devel git_last_commit: 8c756c1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCArray.sat_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCArray.sat_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCArray.sat_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCArray.sat_1.7.0.tgz vignettes: vignettes/SCArray.sat/inst/doc/SCArray.sat.html vignetteTitles: scRNA-seq data analysis with GDS files and Seurat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray.sat/inst/doc/SCArray.sat.R dependencyCount: 187 Package: scater Version: 1.35.0 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, MatrixGenerics, SparseArray, DelayedArray, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, uwot, pheatmap, ggrepel, ggrastr Suggests: BiocStyle, DelayedMatrixStats, snifter, densvis, cowplot, biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat, Biobase, scattermore License: GPL-3 MD5sum: 09a5b13a790a0c8ae4deb5fa9154bdea NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M. Ernst [ctb], Alan O'Callaghan [ctb, cre], Yun Peng [ctb], Leo Lahti [ctb] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: devel git_last_commit: 7211ada git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scater_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scater_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scater_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scater_1.35.0.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: netSmooth, OSCA.intro, OSCA.workflows, SingleRBook importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS, ChromSCape, decontX, distinct, epiregulon.extra, FLAMES, M3Drop, MEB, mia, miaViz, muscat, peco, pipeComp, RegionalST, scDblFinder, scDotPlot, scMerge, scTreeViz, scviR, singleCellTK, Spaniel, tricycle, VAExprs, spatialLIBD, CAESAR.Suite, PRECAST, SC.MEB suggestsMe: APL, Banksy, batchelor, bluster, ccImpute, CellTrails, Cepo, CiteFuse, concordexR, corral, dittoSeq, dreamlet, epiregulon, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, InteractiveComplexHeatmap, iSEE, iSEEfier, iSEEhex, iSEEpathways, iSEEtree, iSEEu, MAST, mbkmeans, MGnifyR, miloR, miQC, monocle, MOSim, MuData, mumosa, Nebulosa, raer, ReactomeGSA, SC3, SCArray, scDiagnostics, scds, schex, scHOT, scone, scp, scPipe, scran, scRepertoire, SingleCellAlleleExperiment, sketchR, slalom, smartid, smoothclust, SpatialFeatureExperiment, spatialHeatmap, speckle, splatter, SPOTlight, StabMap, standR, tidySingleCellExperiment, tidySpatialExperiment, UCell, velociraptor, Voyager, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, HCATonsilData, MerfishData, MouseAgingData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, spicyWorkflow, Canek, ProFAST, SCdeconR, scellpam, SuperCell dependencyCount: 106 Package: scatterHatch Version: 1.13.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 7c64fffdb26739df415c44844c248892 NeedsCompilation: no Title: Creates hatched patterns for scatterplots Description: The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness. biocViews: Visualization, SingleCell, CellBiology, Software, Spatial Author: Atul Deshpande [aut, cre] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/FertigLab/scatterHatch VignetteBuilder: knitr BugReports: https://github.com/FertigLab/scatterHatch/issues git_url: https://git.bioconductor.org/packages/scatterHatch git_branch: devel git_last_commit: f2d495c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scatterHatch_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scatterHatch_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scatterHatch_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scatterHatch_1.13.0.tgz vignettes: vignettes/scatterHatch/inst/doc/vignette.html vignetteTitles: Creating a Scatterplot with Texture hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scatterHatch/inst/doc/vignette.R dependencyCount: 43 Package: scBFA Version: 1.21.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: d8f6cc5045e79cced845b1bef9f3deae NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: devel git_last_commit: 37ab3ec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scBFA_1.21.0.tar.gz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 205 Package: SCBN Version: 1.25.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown,BiocStyle,BiocManager License: GPL-2 MD5sum: aefc94cf587de91eeb7c5f7172e2beb2 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: devel git_last_commit: 4d4f8cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCBN_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCBN_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCBN_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCBN_1.25.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R importsMe: TEKRABber dependencyCount: 1 Package: scBubbletree Version: 1.9.0 Depends: R (>= 4.2.0) Imports: reshape2, BiocParallel, ape, scales, Seurat, ggplot2, ggtree, patchwork, proxy, methods, stats, base, utils, dplyr Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment License: GPL-3 + file LICENSE MD5sum: cbadcf538e531597a80351398a7046d9 NeedsCompilation: no Title: Quantitative visual exploration of scRNA-seq data Description: scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation. biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/scBubbletree SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2) VignetteBuilder: knitr BugReports: https://github.com/snaketron/scBubbletree/issues git_url: https://git.bioconductor.org/packages/scBubbletree git_branch: devel git_last_commit: 2950075 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scBubbletree_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scBubbletree_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scBubbletree_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scBubbletree_1.9.0.tgz vignettes: vignettes/scBubbletree/inst/doc/User_manual.html vignetteTitles: User Manual: scBubbletree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBubbletree/inst/doc/User_manual.R dependencyCount: 172 Package: scCB2 Version: 1.17.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 Archs: x64 MD5sum: f95ec46c474c5963b0e81da3426ae1d7 NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues git_url: https://git.bioconductor.org/packages/scCB2 git_branch: devel git_last_commit: 1e577d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scCB2_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scCB2_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scCB2_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scCB2_1.17.0.tgz vignettes: vignettes/scCB2/inst/doc/scCB2.html vignetteTitles: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scCB2/inst/doc/scCB2.R dependencyCount: 198 Package: scClassify Version: 1.19.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 22338f860870f7ed4e4dc74d794265a3 NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: devel git_last_commit: e235107 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scClassify_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scClassify_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scClassify_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scClassify_1.19.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 157 Package: sccomp Version: 1.11.0 Depends: R (>= 4.2.0) Imports: instantiate (>= 0.2.3), callr, fs, stats, SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr, glue, withr, digest Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), markdown, knitr, loo, prettydoc, tidyseurat, tidySingleCellExperiment, bayesplot, posterior License: GPL-3 MD5sum: 9d39eb5a4cb3ef247af5222e03a0b121 NeedsCompilation: no Title: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data Description: A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further. biocViews: Bayesian, Regression, DifferentialExpression, SingleCell Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/MangiolaLaboratory/sccomp SystemRequirements: CmdStan (https://mc-stan.org/users/interfaces/cmdstan) VignetteBuilder: knitr BugReports: https://github.com/MangiolaLaboratory/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: devel git_last_commit: 4f2a961 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sccomp_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sccomp_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sccomp_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sccomp_1.11.0.tgz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: sccomp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 104 Package: scDataviz Version: 1.17.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown License: GPL-3 MD5sum: 87777491c89c144781e80d08c1ec3379 NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: devel git_last_commit: e17aeed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDataviz_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDataviz_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDataviz_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDataviz_1.17.0.tgz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 178 Package: scDblFinder Version: 1.21.0 Depends: R (>= 4.0), SingleCellExperiment Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans License: GPL-3 + file LICENSE MD5sum: 73d33039841b4526a4410773928cd80d NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder, https://plger.github.io/scDblFinder/ VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: devel git_last_commit: 4c44445 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDblFinder_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDblFinder_1.21.0.zip vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/findDoubletClusters.html, vignettes/scDblFinder/inst/doc/introduction.html, vignettes/scDblFinder/inst/doc/recoverDoublets.html, vignettes/scDblFinder/inst/doc/scATAC.html, vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R, vignettes/scDblFinder/inst/doc/findDoubletClusters.R, vignettes/scDblFinder/inst/doc/introduction.R, vignettes/scDblFinder/inst/doc/recoverDoublets.R, vignettes/scDblFinder/inst/doc/scATAC.R, vignettes/scDblFinder/inst/doc/scDblFinder.R importsMe: singleCellTK dependencyCount: 130 Package: scDD Version: 1.31.0 Depends: R (>= 3.5.0) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: da18e9ffd54ba302df3666210dfd93b6 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] (ORCID: ) Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: devel git_last_commit: 11f58e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDD_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDD_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDD_1.31.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 129 Package: scDDboost Version: 1.9.0 Depends: R (>= 4.2), ggplot2 Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0),EBSeq, BiocParallel, mclust, SingleCellExperiment, cluster, Oscope, SummarizedExperiment, stats, methods LinkingTo: Rcpp, RcppEigen, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: x64 MD5sum: cd366977f81041598cd5771e8c8c31d7 NeedsCompilation: yes Title: A compositional model to assess expression changes from single-cell rna-seq data Description: scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions. biocViews: SingleCell, Software, Clustering, Sequencing, GeneExpression, DifferentialExpression, Bayesian Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb] Maintainer: Xiuyu Ma URL: https://github.com/wiscstatman/scDDboost SystemRequirements: c++11 VignetteBuilder: knitr BugReports: https://github.com/wiscstatman/scDDboost/issues git_url: https://git.bioconductor.org/packages/scDDboost git_branch: devel git_last_commit: 73c0bbe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDDboost_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDDboost_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDDboost_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDDboost_1.9.0.tgz vignettes: vignettes/scDDboost/inst/doc/scDDboost.html vignetteTitles: scDDboost Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDDboost/inst/doc/scDDboost.R dependencyCount: 101 Package: scde Version: 2.35.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 MD5sum: 4554a1c26ddfeb2ab117fe5c3368f14f NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut], Evan Biederstedt [aut] Maintainer: Evan Biederstedt URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: devel git_last_commit: 9d12a81 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scde_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scde_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scde_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scde_2.35.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 50 Package: scDesign3 Version: 1.5.0 Depends: R (>= 4.3.0) Imports: dplyr, tibble, stats, methods, mgcv, gamlss, gamlss.dist, SummarizedExperiment, SingleCellExperiment, mclust, mvtnorm, parallel, pbmcapply, rvinecopulib, umap, ggplot2, irlba, viridis, BiocParallel, matrixStats, Matrix, sparseMVN, coop Suggests: mvnfast, igraph, knitr, rmarkdown, testthat (>= 3.0.0), RefManageR, sessioninfo, BiocStyle License: MIT + file LICENSE MD5sum: 6b9a176ba9c22956d7fd65f45711ea53 NeedsCompilation: no Title: A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics Description: We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. biocViews: Software, SingleCell, Sequencing, GeneExpression, Spatial Author: Dongyuan Song [aut, cre] (ORCID: ), Qingyang Wang [aut] (ORCID: ) Maintainer: Dongyuan Song URL: https://github.com/SONGDONGYUAN1994/scDesign3 VignetteBuilder: knitr BugReports: https://github.com/SONGDONGYUAN1994/scDesign3/issues git_url: https://git.bioconductor.org/packages/scDesign3 git_branch: devel git_last_commit: 27eedaa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDesign3_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDesign3_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDesign3_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDesign3_1.5.0.tgz vignettes: vignettes/scDesign3/inst/doc/scDesign3.html vignetteTitles: scDesign3-quickstart-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDesign3/inst/doc/scDesign3.R dependencyCount: 105 Package: scDiagnostics Version: 1.1.0 Depends: R (>= 4.4.0) Imports: SingleCellExperiment, methods, isotree, ggplot2, ggridges, SummarizedExperiment, ranger, transport, speedglm, cramer, rlang, bluster, patchwork Suggests: AUCell, BiocStyle, knitr, Matrix, rmarkdown, scran, scRNAseq, SingleR, celldex, scuttle, scater, dplyr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 7f1c17da6149872ceb83a8bcfb876ab7 NeedsCompilation: no Title: Cell type annotation diagnostics Description: The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes. biocViews: Annotation, Classification, Clustering, GeneExpression, RNASeq, SingleCell, Software, Transcriptomics Author: Anthony Christidis [aut, cre] (ORCID: ), Andrew Ghazi [aut], Smriti Chawla [aut], Nitesh Turaga [ctb], Ludwig Geistlinger [aut], Robert Gentleman [aut] Maintainer: Anthony Christidis URL: https://github.com/ccb-hms/scDiagnostics VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/scDiagnostics/issues git_url: https://git.bioconductor.org/packages/scDiagnostics git_branch: devel git_last_commit: 1b71ee2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDiagnostics_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDiagnostics_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDiagnostics_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDiagnostics_1.1.0.tgz vignettes: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.html, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.html, vignettes/scDiagnostics/inst/doc/scDiagnostics.html, vignettes/scDiagnostics/inst/doc/VisualizationTools.html vignetteTitles: 4. Detection and Analysis of Annotation Anomalies, 3. Evaluation of Dataset and Marker Gene Alignment, 1. Getting Started with scDiagnostics, 2. Visualization of Cell Type Annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.R, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.R, vignettes/scDiagnostics/inst/doc/scDiagnostics.R, vignettes/scDiagnostics/inst/doc/VisualizationTools.R dependencyCount: 92 Package: scDotPlot Version: 1.1.0 Depends: R (>= 4.4.0) Imports: aplot, BiocGenerics, cli, dplyr, ggplot2, ggsci, ggtree, grDevices, magrittr, purrr, rlang, scales, scater, Seurat, SingleCellExperiment, stats, stringr, tibble, tidyr Suggests: AnnotationDbi, BiocStyle, knitr, rmarkdown, scran, scRNAseq, scuttle, SeuratObject, testthat, vdiffr License: Artistic-2.0 MD5sum: 6b989d5ad3d71a1e510d4d6ba3b79030 NeedsCompilation: no Title: Cluster a Single-cell RNA-seq Dot Plot Description: Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing, Clustering Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/scDotPlot VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/scDotPlot/issues git_url: https://git.bioconductor.org/packages/scDotPlot git_branch: devel git_last_commit: 4a5ac12 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scDotPlot_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scDotPlot_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scDotPlot_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scDotPlot_1.1.0.tgz vignettes: vignettes/scDotPlot/inst/doc/scDotPlot.html vignetteTitles: scDotPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDotPlot/inst/doc/scDotPlot.R dependencyCount: 207 Package: scds Version: 1.23.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown License: MIT + file LICENSE MD5sum: 1408862544c35f219290f2da5bd6c6bc NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: devel git_last_commit: f0cf1fa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scds_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scds_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scds_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scds_1.23.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 57 Package: SCFA Version: 1.17.0 Depends: R (>= 4.0) Imports: matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph, Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown, BiocStyle License: LGPL MD5sum: a425b1187a41420d936c4cdf685db605 NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: devel git_last_commit: 694c1c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCFA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCFA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCFA_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCFA_1.17.0.tgz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 55 Package: scFeatureFilter Version: 1.27.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 978810e068f76bc355055e1f50fc9b2e NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: devel git_last_commit: ef82e3e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scFeatureFilter_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scFeatureFilter_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scFeatureFilter_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scFeatureFilter_1.27.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 38 Package: scFeatures Version: 1.7.0 Depends: R (>= 4.2.0) Imports: DelayedArray, DelayedMatrixStats, EnsDb.Hsapiens.v79, EnsDb.Mmusculus.v79, GSVA, ape, glue, dplyr, ensembldb, gtools, msigdbr, proxyC, reshape2, spatstat.explore, spatstat.geom, tidyr, AUCell, BiocParallel, rmarkdown, methods, stats, cli, SingleCellSignalR, MatrixGenerics, Seurat, DT Suggests: knitr, S4Vectors, survival, survminer, BiocStyle, ClassifyR, org.Hs.eg.db, clusterProfiler License: GPL-3 Archs: x64 MD5sum: 2c6d43d9dff58491e83780ce49a5f167 NeedsCompilation: no Title: scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction Description: scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor. biocViews: CellBasedAssays, SingleCell, Spatial, Software, Transcriptomics Author: Yue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut], Pengyi Yang [aut], Jean Yee Hwa Yang [aut] Maintainer: Yue Cao URL: https://sydneybiox.github.io/scFeatures/ https://github.com/SydneyBioX/scFeatures/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scFeatures/issues git_url: https://git.bioconductor.org/packages/scFeatures git_branch: devel git_last_commit: e9d6bd3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scFeatures_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scFeatures_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scFeatures_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scFeatures_1.7.0.tgz vignettes: vignettes/scFeatures/inst/doc/scFeatures_overview.html vignetteTitles: Overview of scFeatures with case studies hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scFeatures/inst/doc/scFeatures_overview.R dependencyCount: 252 Package: scGPS Version: 1.21.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq2, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 MD5sum: 6d8d86cde9b2ee1ed82819967ccbf37b NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: devel git_last_commit: 03c5e17 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scGPS_1.21.0.tar.gz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 126 Package: schex Version: 1.21.0 Depends: SingleCellExperiment (>= 1.7.4), ggplot2 (>= 3.2.1) Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, grid, rlang, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, Seurat, shinydashboard, iSEE, igraph, scran, tibble, scuttle License: GPL-3 MD5sum: 044df0cb0bbe69fe40429dd858b2238d NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization, ImmunoOncology, DataImport Author: Saskia Freytag [aut, cre], Wancheng Tang [ctb], Zimo Peng [ctb], Jingxiu Huang [ctb] Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: devel git_last_commit: 6af9fd5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/schex_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/schex_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/schex_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/schex_1.21.0.tgz vignettes: vignettes/schex/inst/doc/Seurat_to_SCE.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: Seurat_to_SCE, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/Seurat_to_SCE.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF dependencyCount: 88 Package: scHOT Version: 1.19.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 MD5sum: 49f5b07b0f96b076772af584a8a02ce2 NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: devel git_last_commit: 07071f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scHOT_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scHOT_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scHOT_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scHOT_1.19.0.tgz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 84 Package: scider Version: 1.5.0 Depends: R (>= 4.3) Imports: SpatialExperiment, SummarizedExperiment, spatstat.geom, spatstat.explore, sf, lwgeom, ggplot2, stats, pheatmap, plotly, shiny, igraph, janitor, knitr, methods, utils, rlang, isoband, S4Vectors, grDevices Suggests: edgeR, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: f58ec649c8e2dadf2c182fac32cf39aa NeedsCompilation: no Title: Spatial cell-type inter-correlation by density in R Description: scider is a user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis. biocViews: Spatial, Transcriptomics Author: Ning Liu [aut] (ORCID: ), Mengbo Li [aut] (ORCID: ), Yunshun Chen [aut, cre] (ORCID: ) Maintainer: Yunshun Chen URL: https://github.com/ChenLaboratory/scider, https://chenlaboratory.github.io/scider/ VignetteBuilder: knitr BugReports: https://github.com/ChenLaboratory/scider/issues git_url: https://git.bioconductor.org/packages/scider git_branch: devel git_last_commit: 5152c7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scider_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scider_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scider_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scider_1.5.0.tgz vignettes: vignettes/scider/inst/doc/scider_userGuide.html vignetteTitles: scider_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scider/inst/doc/scider_userGuide.R dependencyCount: 143 Package: scifer Version: 1.9.0 Imports: dplyr, rmarkdown, data.table, Biostrings, stats, plyr, knitr, ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra, tibble, scales, rlang, flowCore, methods, basilisk, reticulate, here, utils, basilisk.utils Suggests: BiocBaseUtils, fs, BiocStyle, testthat (>= 3.0.0) Enhances: parallel License: MIT + file LICENSE MD5sum: 6a43fd8241705a2b7a5dc8c4a66c255e NeedsCompilation: no Title: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences Description: Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports. biocViews: Preprocessing, QualityControl, SangerSeq, Sequencing, Software, FlowCytometry, SingleCell Author: Rodrigo Arcoverde Cerveira [aut, cre, cph] (ORCID: ), Marcel Martin [ctb], Matthew James Hinchcliff [ctb], Sebastian Ols [aut, dtc] (ORCID: ), Karin Loré [dtc, ths, fnd] (ORCID: ) Maintainer: Rodrigo Arcoverde Cerveira URL: https://github.com/rodrigarc/scifer VignetteBuilder: knitr BugReports: https://github.com/rodrigarc/scifer/issues git_url: https://git.bioconductor.org/packages/scifer git_branch: devel git_last_commit: 5aa0572 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scifer_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scifer_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scifer_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scifer_1.9.0.tgz vignettes: vignettes/scifer/inst/doc/scifer_walkthrough.html vignetteTitles: Using scifer to filter single-cell sorted B cell receptor (BCR) sanger sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scifer/inst/doc/scifer_walkthrough.R dependencyCount: 116 Package: scmap Version: 1.29.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 9a8d6bb902f0fbbfd400e2b8ee5af2d0 NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: devel git_last_commit: 4a84aef git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scmap_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scmap_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scmap_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scmap_1.29.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 77 Package: scMerge Version: 1.23.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, BiocNeighbors, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), proxyC, ruv, cvTools, scater, batchelor, scran, methods, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, proxy, testthat, badger License: GPL-3 MD5sum: e7a0023da7a2fbb704ed0d162d419a6c NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: devel git_last_commit: d99a8f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scMerge_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMerge_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMerge_1.23.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html, vignettes/scMerge/inst/doc/scMerge2.html vignetteTitles: scMerge, scMerge2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R, vignettes/scMerge/inst/doc/scMerge2.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 188 Package: scMET Version: 1.9.0 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS, logitnorm, ggplot2, matrixStats, assertthat, viridis, coda, BiocStyle, cowplot, stats, SummarizedExperiment, SingleCellExperiment, Matrix, dplyr, S4Vectors LinkingTo: BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>= 2.21.0.7) Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: a833076ef534b5fe2f6aa4dd17ee2263 NeedsCompilation: yes Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity Description: High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, DifferentialExpression, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, Bayesian, Sequencing, Coverage, SingleCell Author: Andreas C. Kapourani [aut, cre] (ORCID: ), John Riddell [ctb] Maintainer: Andreas C. Kapourani SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/andreaskapou/scMET/issues git_url: https://git.bioconductor.org/packages/scMET git_branch: devel git_last_commit: e01d7d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scMET_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMET_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMET_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMET_1.9.0.tgz vignettes: vignettes/scMET/inst/doc/scMET_vignette.html vignetteTitles: scMET analysis using synthetic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMET/inst/doc/scMET_vignette.R dependencyCount: 118 Package: scmeth Version: 1.27.0 Depends: R (>= 3.5.0) Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics, ggplot2, ggthemes License: GPL-2 MD5sum: 7cb1722a8f19208aa152b7c7f596625c NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: devel git_last_commit: 6017084 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scmeth_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scmeth_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scmeth_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scmeth_1.27.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 163 Package: scMitoMut Version: 1.3.1 Depends: R (>= 4.3.0) Imports: data.table, Rcpp, magrittr, plyr, stringr, utils, stats, methods, ggplot2, pheatmap, RColorBrewer, rhdf5, readr, parallel, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, VGAM, R.utils License: Artistic-2.0 MD5sum: a4ba77792586a892ce8c9394eb286dfe NeedsCompilation: yes Title: Single-cell Mitochondrial Mutation Analysis Tool Description: This package is designed for calling lineage-informative mitochondrial mutations using single-cell sequencing data, such as scRNASeq and scATACSeq (preferably the latter due to RNA editing issues). It includes functions for mutation calling and visualization. Mutation calling is done using beta-binomial distribution. biocViews: Preprocessing, Sequencing, SingleCell Author: Wenjie Sun [cre, aut] (ORCID: ), Leila Perie [ctb] Maintainer: Wenjie Sun URL: http://github.com/wenjie1991/scMitoMut VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/scMitoMut/issues git_url: https://git.bioconductor.org/packages/scMitoMut git_branch: devel git_last_commit: 1dd76eb git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-16 source.ver: src/contrib/scMitoMut_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMitoMut_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMitoMut_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMitoMut_1.3.1.tgz vignettes: vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.html vignetteTitles: CRC_dataset_demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.R dependencyCount: 59 Package: scMultiSim Version: 1.3.0 Depends: R (>= 4.4.0) Imports: foreach, rlang, dplyr, ggplot2, Rtsne, ape, MASS, matrixStats, phytools, KernelKnn, gplots, zeallot, crayon, assertthat, igraph, methods, grDevices, graphics, stats, utils, markdown, SummarizedExperiment, BiocParallel Suggests: knitr, rmarkdown, roxygen2, shiny, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 5b8532b29e531eb8ea1bbc1068baeda0 NeedsCompilation: no Title: Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions Description: scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments. biocViews: SingleCell, Transcriptomics, GeneExpression, Sequencing, ExperimentalDesign Author: Hechen Li [aut, cre] (ORCID: ), Xiuwei Zhang [aut], Ziqi Zhang [aut], Michael Squires [aut] Maintainer: Hechen Li URL: https://zhanglabgt.github.io/scMultiSim/ VignetteBuilder: knitr BugReports: https://github.com/ZhangLabGT/scMultiSim/issues git_url: https://git.bioconductor.org/packages/scMultiSim git_branch: devel git_last_commit: 2e6a4fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scMultiSim_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scMultiSim_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scMultiSim_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scMultiSim_1.3.0.tgz vignettes: vignettes/scMultiSim/inst/doc/basics.html, vignettes/scMultiSim/inst/doc/options.html, vignettes/scMultiSim/inst/doc/spatialCCI.html, vignettes/scMultiSim/inst/doc/workflow.html vignetteTitles: 2. Simulating Multimodal Single-cell Datasets, 4. Parameter Guide, 3. Simulating Spatial Cell-Cell Interactions, 1. Getting Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMultiSim/inst/doc/basics.R, vignettes/scMultiSim/inst/doc/options.R, vignettes/scMultiSim/inst/doc/spatialCCI.R, vignettes/scMultiSim/inst/doc/workflow.R dependencyCount: 109 Package: SCnorm Version: 1.29.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 91701ccf6ba680496222e68c17368ddc NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: devel git_last_commit: d1d79f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCnorm_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCnorm_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCnorm_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCnorm_1.29.0.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 82 Package: scone Version: 1.31.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment, DelayedMatrixStats, sparseMatrixStats Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, batchtools, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 Archs: x64 MD5sum: 751c18d0742b04c85224ca8b4c42671f NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: devel git_last_commit: 92a6cd8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/scone_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scone_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scone_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scone_1.31.0.tgz vignettes: vignettes/scone/inst/doc/PsiNorm.html, vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: PsiNorm normalization, Introduction to SCONE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/PsiNorm.R, vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 188 Package: Sconify Version: 1.27.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 3d10923ef2e5d24dfbbaa1bb70ab47b6 NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: devel git_last_commit: e6f83c2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Sconify_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Sconify_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Sconify_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Sconify_1.27.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 62 Package: SCOPE Version: 1.19.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: 410561e9ddb7d42d90f04fcc30497657 NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: devel git_last_commit: 8ca1afd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SCOPE_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SCOPE_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SCOPE_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SCOPE_1.19.0.tgz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 113 Package: scoreInvHap Version: 1.29.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 6ac61ec8a2f2613d2bb579579d06104e NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: devel git_last_commit: a171cc8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scoreInvHap_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scoreInvHap_1.29.0.zip vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 83 Package: scoup Version: 1.1.1 Depends: R (>= 4.4), Matrix Imports: Biostrings, methods Suggests: BiocManager, BiocStyle, bookdown, htmltools, knitr, testthat (>= 3.0.0), yaml License: GPL (>= 2) MD5sum: 61b1b128ceb2c309c93bd6da92913ca6 NeedsCompilation: no Title: Simulate Codons with Darwinian Selection Modelled as an OU Process Description: An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model was built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enabled realistic evolutionary process of living organisms to be reproduced seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and mutation on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and deterministic fitness landscape update techniques are also available. biocViews: Alignment, Classification, ComparativeGenomics, DataImport, Genetics, MathematicalBiology, ResearchField, Sequencing, SequenceMatching, Software, StatisticalMethod, WorkflowStep Author: Hassan Sadiq [aut, cre, cph] (ORCID: ) Maintainer: Hassan Sadiq URL: https://github.com/thsadiq/scoup VignetteBuilder: knitr BugReports: https://github.com/thsadiq/scoup/issues git_url: https://git.bioconductor.org/packages/scoup git_branch: devel git_last_commit: 3cc9acd git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/scoup_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/scoup_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scoup_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scoup_1.1.1.tgz vignettes: vignettes/scoup/inst/doc/scoup.html vignetteTitles: scoup Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scoup/inst/doc/scoup.R dependencyCount: 29 Package: scp Version: 1.17.0 Depends: R (>= 4.3.0), QFeatures (>= 1.13.5) Imports: IHW, ggplot2, ggrepel, matrixStats, metapod, methods, MsCoreUtils, MultiAssayExperiment, nipals, RColorBrewer, S4Vectors, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, MsDataHub (>= 1.3.3), impute, knitr, patchwork, preprocessCore, rmarkdown, scater, scpdata, sva, testthat, vdiffr, vsn, uwot License: Artistic-2.0 MD5sum: 8f7e74352660b480831a9106adc12405 NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ) Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: devel git_last_commit: aa22264 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scp_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scp_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scp_1.17.0.tgz vignettes: vignettes/scp/inst/doc/advanced.html, vignettes/scp/inst/doc/QFeatures_nutshell.html, vignettes/scp/inst/doc/read_scp.html, vignettes/scp/inst/doc/reporting_missing_values.html, vignettes/scp/inst/doc/scp_data_modelling.html, vignettes/scp/inst/doc/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, Reporting missing values, Single Cell Proteomics data modelling, Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/advanced.R, vignettes/scp/inst/doc/QFeatures_nutshell.R, vignettes/scp/inst/doc/read_scp.R, vignettes/scp/inst/doc/reporting_missing_values.R, vignettes/scp/inst/doc/scp_data_modelling.R, vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 116 Package: scPCA Version: 1.21.0 Depends: R (>= 4.0.0) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: 3b2ece9f0b747f98e32655d3daf78efd NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (ORCID: ), Nima Hejazi [aut] (ORCID: ), Sandrine Dudoit [ctb, ths] (ORCID: ) Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: devel git_last_commit: 5acfbe2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scPCA_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scPCA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scPCA_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scPCA_1.21.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependsOnMe: OSCA.workflows dependencyCount: 73 Package: scPipe Version: 2.7.1 Depends: R (>= 4.2.0), SingleCellExperiment Imports: AnnotationDbi, basilisk, BiocGenerics, biomaRt, Biostrings, data.table, dplyr, DropletUtils, flexmix, GenomicRanges, GenomicAlignments, GGally, ggplot2, glue (>= 1.3.0), grDevices, graphics, hash, IRanges, magrittr, MASS, Matrix (>= 1.5.0), mclust, methods, MultiAssayExperiment, org.Hs.eg.db, org.Mm.eg.db, purrr, Rcpp (>= 0.11.3), reshape, reticulate, Rhtslib, rlang, robustbase, Rsamtools, Rsubread, rtracklayer, SummarizedExperiment, S4Vectors, scales, stats, stringr, tibble, tidyr, tools, utils, vctrs (>= 0.5.2) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), testthat Suggests: BiocStyle, DT, GenomicFeatures, grid, igraph, kableExtra, knitr, locStra, plotly, rmarkdown, RColorBrewer, readr, reshape2, RANN, shiny, scater (>= 1.11.0), testthat, xml2, umap License: GPL (>= 2) MD5sum: 408c9f014761019b16875f53782b86d0 NeedsCompilation: yes Title: Pipeline for single cell multi-omic data pre-processing Description: A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport Author: Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew Ritchie [ctb] Maintainer: Shian Su URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: devel git_last_commit: 53eacce git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 source.ver: src/contrib/scPipe_2.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/scPipe_2.7.1.zip vignettes: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.html, vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: a flexible data preprocessing pipeline for scATAC-seq data, scPipe: a flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.R, vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 174 Package: scran Version: 1.35.0 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, MatrixGenerics, S4Arrays, DelayedArray, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, DelayedMatrixStats, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, pheatmap, scater License: GPL-3 MD5sum: b50399d58c2d37c06c5ac5788f8fe733 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun URL: https://github.com/MarioniLab/scran/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/scran/issues git_url: https://git.bioconductor.org/packages/scran git_branch: devel git_last_commit: f18e781 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scran_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scran_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scran_1.35.0.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R dependsOnMe: OSCA.intro, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, BatchQC, BayesSpace, BioTIP, celda, ChromSCape, CiteFuse, Dino, epiregulon, epiregulon.extra, FLAMES, lute, MOSim, MPAC, msImpute, mumosa, pipeComp, scDblFinder, scDD, scMerge, scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, SpaNorm, mixhvg, SC.MEB, SpatialDDLS suggestsMe: APL, Banksy, batchelor, bluster, CellTrails, clusterExperiment, decontX, destiny, dittoSeq, escape, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, glmGamPoi, iSEEu, miloR, Nebulosa, nnSVG, PCAtools, raer, ReactomeGSA, scDiagnostics, scDotPlot, schex, scone, scuttle, SingleCellAlleleExperiment, sketchR, smoothclust, spatialHeatmap, splatter, SPOTlight, StabMap, tidySingleCellExperiment, tpSVG, transformGamPoi, TSCAN, velociraptor, Voyager, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, Canek, SCdeconR dependencyCount: 74 Package: scrapper Version: 1.1.7 Imports: methods, Rcpp, beachmat (>= 2.21.6), DelayedArray, BiocNeighbors (>= 1.99.0), igraph, parallel LinkingTo: Rcpp, assorthead, beachmat, BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, MatrixGenerics, sparseMatrixStats, Matrix, scRNAseq License: MIT + file LICENSE MD5sum: 864ac11a6c480f5a78f958dc8232691d NeedsCompilation: yes Title: Bindings to C++ Libraries for Single-Cell Analysis Description: Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows. biocViews: Normalization, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, QualityControl, DifferentialExpression, FeatureExtraction, PrincipalComponent, Clustering Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scrapper git_branch: devel git_last_commit: 4b62324 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/scrapper_1.1.7.tar.gz win.binary.ver: bin/windows/contrib/4.5/scrapper_1.1.7.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scrapper_1.1.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scrapper_1.1.7.tgz vignettes: vignettes/scrapper/inst/doc/userguide.html vignetteTitles: Using scrapper to analyze single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scrapper/inst/doc/userguide.R suggestsMe: SingleR dependencyCount: 37 Package: scReClassify Version: 1.13.0 Depends: R (>= 4.1) Imports: randomForest, e1071, stats, SummarizedExperiment, SingleCellExperiment, methods Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr License: GPL-3 + file LICENSE MD5sum: 2f647d2eaabd77bb0c240072065d3dc6 NeedsCompilation: no Title: scReClassify: post hoc cell type classification of single-cell RNA-seq data Description: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier. biocViews: Software, Transcriptomics, SingleCell, Classification, SupportVectorMachine Author: Pengyi Yang [aut] (ORCID: ), Taiyun Kim [aut, cre] (ORCID: ) Maintainer: Taiyun Kim URL: https://github.com/SydneyBioX/scReClassify, http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scReClassify/issues git_url: https://git.bioconductor.org/packages/scReClassify git_branch: devel git_last_commit: 5cefdbe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scReClassify_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scReClassify_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scReClassify_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scReClassify_1.13.0.tgz vignettes: vignettes/scReClassify/inst/doc/scReClassify.html vignetteTitles: An introduction to scReClassify package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R dependencyCount: 43 Package: scRecover Version: 1.23.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: 80b84472e0274c37e7684ff512c635b1 NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: devel git_last_commit: b5db975 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scRecover_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scRecover_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scRecover_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scRecover_1.23.0.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 46 Package: screenCounter Version: 1.7.1 Depends: S4Vectors, SummarizedExperiment Imports: Rcpp, BiocParallel LinkingTo: Rcpp Suggests: BiocGenerics, Biostrings, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: cbd9ea1f8565dce67b0815fb53c43af4 NeedsCompilation: yes Title: Counting Reads in High-Throughput Sequencing Screens Description: Provides functions for counting reads from high-throughput sequencing screen data (e.g., CRISPR, shRNA) to quantify barcode abundance. Currently supports single barcodes in single- or paired-end data, and combinatorial barcodes in paired-end data. biocViews: CRISPR, Alignment, FunctionalGenomics, FunctionalPrediction Author: Aaron Lun [aut, cre] (ORCID: ) Maintainer: Aaron Lun URL: https://github.com/crisprVerse/screenCounter SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/screenCounter/issues git_url: https://git.bioconductor.org/packages/screenCounter git_branch: devel git_last_commit: d647762 git_last_commit_date: 2024-12-11 Date/Publication: 2024-12-12 source.ver: src/contrib/screenCounter_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/screenCounter_1.7.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/screenCounter_1.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/screenCounter_1.7.1.tgz vignettes: vignettes/screenCounter/inst/doc/counting.html vignetteTitles: Counting barcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/screenCounter/inst/doc/counting.R dependencyCount: 48 Package: ScreenR Version: 1.9.0 Depends: R (>= 4.2) Imports: methods (>= 4.0), rlang (>= 0.4), stringr (>= 1.4), limma (>= 3.46), patchwork (>= 1.1), tibble (>= 3.1.6), scales (>= 1.1.1), ggvenn (>= 0.1.9), purrr (>= 0.3.4), ggplot2 (>= 3.3), stats, tidyr (>= 1.2), magrittr (>= 1.0), dplyr (>= 1.0), edgeR (>= 3.32), tidyselect (>= 1.1.2) Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0), BiocStyle (>= 2.22.0), covr (>= 3.5) License: MIT + file LICENSE Archs: x64 MD5sum: ca3886ae9ad5f3978ba359129d8341ee NeedsCompilation: no Title: Package to Perform High Throughput Biological Screening Description: ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis. biocViews: Software, AssayDomain, GeneExpression Author: Emanuel Michele Soda [aut, cre] (ORICD: 0000-0002-2301-6465), Elena Ceccacci [aut] (ORICD: 0000-0002-2285-8994), Saverio Minucci [fnd, ths] (ORICD: 0000-0001-5678-536X) Maintainer: Emanuel Michele Soda URL: https://emanuelsoda.github.io/ScreenR/ VignetteBuilder: knitr BugReports: https://github.com/EmanuelSoda/ScreenR/issues git_url: https://git.bioconductor.org/packages/ScreenR git_branch: devel git_last_commit: 9ed4aad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ScreenR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ScreenR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ScreenR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ScreenR_1.9.0.tgz vignettes: vignettes/ScreenR/inst/doc/Analysis_Example.html vignetteTitles: ScreenR Example Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ScreenR/inst/doc/Analysis_Example.R dependencyCount: 50 Package: scRepertoire Version: 2.3.1 Depends: ggplot2, R (>= 4.0) Imports: assertthat, cubature, dplyr, evmix, ggalluvial, ggdendro, ggraph, grDevices, igraph, iNEXT, methods, plyr, quantreg, Rcpp, reshape2, rjson, rlang, S4Vectors, SeuratObject, SingleCellExperiment, stats, stringr, stringdist, SummarizedExperiment, tidygraph, truncdist, utils, VGAM, hash, purrr, lifecycle LinkingTo: Rcpp Suggests: BiocManager, BiocStyle, circlize, knitr, rmarkdown, scales, scater, Seurat, spelling, testthat (>= 3.0.0), vdiffr License: MIT + file LICENSE MD5sum: cea06b34e3edc7b3fce386b8ebf03ae9 NeedsCompilation: yes Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R workflows. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre], Qile Yang [aut], Ksenia Safina [aut] Maintainer: Nick Borcherding URL: https://www.borch.dev/uploads/scRepertoire/ VignetteBuilder: knitr BugReports: https://github.com/ncborcherding/scRepertoire/issues git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: devel git_last_commit: df773d9 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-19 source.ver: src/contrib/scRepertoire_2.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/scRepertoire_2.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scRepertoire_2.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scRepertoire_2.3.1.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R dependencyCount: 121 Package: scRNAseqApp Version: 1.7.6 Depends: R (>= 4.3.0) Imports: bibtex, bslib, circlize, ComplexHeatmap, colourpicker, data.table, DBI, DT, fs, GenomicRanges, GenomeInfoDb, ggdendro, ggforce, ggplot2, ggrepel, ggridges, grDevices, grid, gridExtra, htmltools, IRanges, jsonlite, Matrix, magrittr, methods, patchwork, plotly, RColorBrewer, RefManageR, rhdf5, Rsamtools, RSQLite, rtracklayer, S4Vectors, scales, scrypt, Seurat, SeuratObject, shiny, shinyhelper, shinymanager, slingshot, SingleCellExperiment, sortable, stats, tools, xfun, xml2, utils Suggests: rmarkdown, knitr, testthat, BiocStyle Enhances: celldex, future, SingleR, SummarizedExperiment, tricycle License: GPL-3 MD5sum: 1d99aea5fdcd328f595c98e1f082f444 NeedsCompilation: no Title: A single-cell RNAseq Shiny app-package Description: The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users. biocViews: Visualization, SingleCell, RNASeq Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/scRNAseqApp VignetteBuilder: knitr BugReports: https://github.com/jianhong/scRNAseqApp/issues git_url: https://git.bioconductor.org/packages/scRNAseqApp git_branch: devel git_last_commit: ca33026 git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-21 source.ver: src/contrib/scRNAseqApp_1.7.6.tar.gz vignettes: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.html vignetteTitles: scRNAseqApp Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.R dependencyCount: 236 Package: scruff Version: 1.25.0 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, txdbmaker, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread, parallelly Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 5afa0d01b10845165684f0755423a600 NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: devel git_last_commit: 21462a6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scruff_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scruff_1.25.0.zip vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 176 Package: scry Version: 1.19.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, HDF5Array, knitr, markdown, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: 82cc98ef235ccf53dabe0f6189b93e0c NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: devel git_last_commit: ae09b7d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scry_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scry_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scry_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scry_1.19.0.tgz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R dependencyCount: 57 Package: scShapes Version: 1.13.0 Depends: R (>= 4.1) Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS, emdbook, magrittr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 5b2f3c03832d37f3879b3cbf60f502cf NeedsCompilation: yes Title: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data Description: We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic. biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression Author: Malindrie Dharmaratne [cre, aut] (ORCID: ) Maintainer: Malindrie Dharmaratne URL: https://github.com/Malindrie/scShapes VignetteBuilder: knitr BugReports: https://github.com/Malindrie/scShapes/issues git_url: https://git.bioconductor.org/packages/scShapes git_branch: devel git_last_commit: f5a9606 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scShapes_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scShapes_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scShapes_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scShapes_1.13.0.tgz vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html vignetteTitles: The vignette for running scShapes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R dependencyCount: 34 Package: scTensor Version: 2.17.0 Depends: R (>= 4.1.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens, AnnotationHub License: Artistic-2.0 MD5sum: 79d13a34d33f8d6455b536946d971186 NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: devel git_last_commit: dceaf02 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scTensor_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTensor_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTensor_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTensor_2.17.0.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 237 Package: scTGIF Version: 1.21.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: a794200679da0fe705499d3c4633d611 NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: devel git_last_commit: 90a5f4e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scTGIF_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTGIF_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTGIF_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTGIF_1.21.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 149 Package: scTHI Version: 1.19.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 3957fc1b46aa3af828fbf80836c9fba8 NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: devel git_last_commit: f1906f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scTHI_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTHI_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTHI_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scTHI_1.19.0.tgz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 17 Package: scTreeViz Version: 1.13.0 Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph, clustree, scran, sys, epivizrData, epivizrServer, ggraph, scater, Seurat, SingleCellExperiment, ggplot2, stats, utils Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s, metagenomeSeq, epivizrStandalone, GenomeInfoDb License: Artistic-2.0 MD5sum: 4e6ac436529856199dbeffdfd5d2d189 NeedsCompilation: no Title: R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations Description: scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters. biocViews: Visualization, Infrastructure, GUI, SingleCell Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut] Maintainer: Jayaram Kancherla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: devel git_last_commit: 9eebcab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scTreeViz_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scTreeViz_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scTreeViz_1.13.0.tgz vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html vignetteTitles: Explore Data using scTreeViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R dependencyCount: 256 Package: scuttle Version: 1.17.0 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, S4Arrays, MatrixGenerics, SparseArray, DelayedArray, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, sparseMatrixStats, DelayedMatrixStats, scran License: GPL-3 MD5sum: f5b04790204d31085cd289d03cb2f93d NeedsCompilation: yes Title: Single-Cell RNA-Seq Analysis Utilities Description: Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: devel git_last_commit: 5930665 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scuttle_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/scuttle_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/scuttle_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scuttle_1.17.0.tgz vignettes: vignettes/scuttle/inst/doc/misc.html, vignettes/scuttle/inst/doc/norm.html, vignettes/scuttle/inst/doc/qc.html vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/misc.R, vignettes/scuttle/inst/doc/norm.R, vignettes/scuttle/inst/doc/qc.R dependsOnMe: scater, scran, OSCA.intro, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, batchelor, DropletUtils, epiregulon, FLAMES, imcRtools, mia, mumosa, muscat, scDblFinder, simPIC, singleCellTK, splatter, SplineDV, spoon, velociraptor, spatialLIBD, mixhvg, SpatialDDLS suggestsMe: Banksy, bluster, DESpace, dreamlet, epiregulon.extra, escheR, ggsc, iSEEde, iSEEfier, iSEEpathways, mastR, miloR, raer, ReactomeGSA, SCArray, scDiagnostics, scDotPlot, schex, SingleCellAlleleExperiment, SingleR, sketchR, smoothclust, spatialHeatmap, SpotSweeper, tpSVG, TSCAN, HCAData, MouseThymusAgeing, scCustomize linksToMe: DropletUtils, scran dependencyCount: 51 Package: scviR Version: 1.7.0 Depends: R (>= 4.3), basilisk, shiny, SingleCellExperiment Imports: reticulate, BiocFileCache, utils, pheatmap, SummarizedExperiment, S4Vectors, limma, scater, stats, MatrixGenerics Suggests: knitr, testthat, reshape2, ggplot2, rhdf5, BiocStyle License: Artistic-2.0 MD5sum: 28a66b85bb8f3fa4521e6f4e5779c95a NeedsCompilation: no Title: experimental inferface from R to scvi-tools Description: This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders. biocViews: Infrastructure, SingleCell, DataImport Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/scviR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/scviR/issues git_url: https://git.bioconductor.org/packages/scviR git_branch: devel git_last_commit: 97c87b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/scviR_1.7.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/scviR_1.7.0.tgz vignettes: vignettes/scviR/inst/doc/citeseq_tut.html, vignettes/scviR/inst/doc/compch12.html, vignettes/scviR/inst/doc/scviR.html vignetteTitles: scvi-tools CITE-seq tutorial in R,, using serialized tutorial components, Comparing totalVI and OSCA book CITE-seq analyses, scviR: an R package interfacing Bioconductor and scvi-tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scviR/inst/doc/citeseq_tut.R, vignettes/scviR/inst/doc/compch12.R, vignettes/scviR/inst/doc/scviR.R dependencyCount: 150 Package: SDAMS Version: 1.27.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 562c478a75b98fe3f327b642e0c5be20 NeedsCompilation: no Title: Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: devel git_last_commit: faef9bc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SDAMS_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SDAMS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SDAMS_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SDAMS_1.27.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 70 Package: seahtrue Version: 1.1.0 Depends: R (>= 4.2.0) Imports: dplyr (>= 1.1.2), readxl (>= 1.4.1), logger (>= 0.2.2), tidyxl (>= 1.0.8), purrr (>= 0.3.5), tidyr (>= 1.3.0), lubridate (>= 1.8.0), stringr (>= 1.4.1), tibble (>= 3.1.8), validate (>= 1.1.1), rlang (>= 1.0.0), glue (>= 1.6.2), cli (>= 3.4.1), janitor (>= 2.2.0), ggplot2 (>= 3.5.0), RColorBrewer (>= 1.1.3), colorspace (>= 2.1.0), forcats (>= 1.0.0), ggridges (>= 0.5.6), readr (>= 2.1.5), scales (>= 1.3.0) Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 MD5sum: 8d7556c07580ebc40e1523c38ec3d421 NeedsCompilation: no Title: Seahtrue revives XF data for structured data analysis Description: Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks. biocViews: CellBasedAssays, FunctionalPrediction, DataRepresentation, DataImport, CellBiology, Cheminformatics, Metabolomics, MicrotitrePlateAssay, Visualization, QualityControl, BatchEffect, ExperimentalDesign, Preprocessing, GO Author: Vincent de Boer [cre, aut] (ORCID: ), Gerwin Smits [aut], Xiang Zhang [aut] Maintainer: Vincent de Boer URL: https://vcjdeboer.github.io/seahtrue/ VignetteBuilder: knitr BugReports: https://vcjdeboer.github.io/seahtrue/issues git_url: https://git.bioconductor.org/packages/seahtrue git_branch: devel git_last_commit: c5c897f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seahtrue_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seahtrue_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seahtrue_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seahtrue_1.1.0.tgz vignettes: vignettes/seahtrue/inst/doc/seahtrue.html vignetteTitles: Introduction to Seahtrue hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seahtrue/inst/doc/seahtrue.R dependencyCount: 70 Package: sechm Version: 1.15.0 Depends: R (>= 4.0), SummarizedExperiment, ComplexHeatmap Imports: S4Vectors, seriation, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: fe3f6fd9143daf225c0183d6de9b7aa4 NeedsCompilation: no Title: sechm: Complex Heatmaps from a SummarizedExperiment Description: sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: devel git_last_commit: adfb618 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sechm_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sechm_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sechm_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sechm_1.15.0.tgz vignettes: vignettes/sechm/inst/doc/sechm.html vignetteTitles: sechm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sechm/inst/doc/sechm.R dependsOnMe: SEtools importsMe: broadSeq dependencyCount: 83 Package: segmenter Version: 1.13.0 Depends: R (>= 4.1) Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges, S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils, methods, chromhmmData Suggests: testthat, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz License: GPL-3 MD5sum: 8360274193ca52b65361b6f02c558240 NeedsCompilation: no Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM Description: Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation. biocViews: Software, HistoneModification Author: Mahmoud Ahmed [aut, cre] (ORCID: ) Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues git_url: https://git.bioconductor.org/packages/segmenter git_branch: devel git_last_commit: 2c80157 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/segmenter_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/segmenter_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/segmenter_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/segmenter_1.13.0.tgz vignettes: vignettes/segmenter/inst/doc/segmenter.html vignetteTitles: Chromatin Segmentation Analysis Using segmenter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmenter/inst/doc/segmenter.R dependencyCount: 162 Package: segmentSeq Version: 2.41.1 Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: c10b73b9722f5fd243ad1cf8a95ed55a NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/segmentSeq VignetteBuilder: knitr BugReports: https://github.com/samgg/segmentSeq/issues git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: devel git_last_commit: ce8078c git_last_commit_date: 2024-11-04 Date/Publication: 2024-11-05 source.ver: src/contrib/segmentSeq_2.41.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/segmentSeq_2.41.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/segmentSeq_2.41.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/segmentSeq_2.41.1.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.html, vignettes/segmentSeq/inst/doc/segmentSeq.html vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 69 Package: selectKSigs Version: 1.19.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 Archs: x64 MD5sum: 550c61356561b577f67aeebeb6dc1093 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: devel git_last_commit: 9e6184b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/selectKSigs_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/selectKSigs_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/selectKSigs_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/selectKSigs_1.19.0.tgz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 117 Package: SELEX Version: 1.39.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) Archs: x64 MD5sum: 5f46a821d9c2b15aee5fb216a3fdabc2 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: devel git_last_commit: 6a0fd9f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SELEX_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SELEX_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SELEX_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SELEX_1.39.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 27 Package: SemDist Version: 1.41.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: f6b436acfa2ac70606e7d8a933d4ca1f NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: devel git_last_commit: 062d1fa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SemDist_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SemDist_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SemDist_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SemDist_1.41.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 50 Package: semisup Version: 1.31.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: c19663363615957ac9454466f6455c49 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: devel git_last_commit: 5bd4eff git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/semisup_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/semisup_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/semisup_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/semisup_1.31.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: seq.hotSPOT Version: 1.7.0 Depends: R (>= 3.5.0) Imports: R.utils, hash, stats, base, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 1247b1d404059a9076ee006810ec34cb NeedsCompilation: no Title: Targeted sequencing panel design based on mutation hotspots Description: seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome Author: Sydney Grant [aut, cre], Lei Wei [aut], Gyorgy Paragh [aut] Maintainer: Sydney Grant URL: https://github.com/sydney-grant/seq.hotSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/seq.hotSPOT/issues git_url: https://git.bioconductor.org/packages/seq.hotSPOT git_branch: devel git_last_commit: 8b8cba4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seq.hotSPOT_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seq.hotSPOT_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seq.hotSPOT_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seq.hotSPOT_1.7.0.tgz vignettes: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.html vignetteTitles: hotSPOT-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.R dependencyCount: 9 Package: seq2pathway Version: 1.39.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: a938073f69a98f112e9ae0725208e9c6 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Arjun Kinstlick git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: devel git_last_commit: f56684f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seq2pathway_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seq2pathway_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seq2pathway_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seq2pathway_1.39.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R dependencyCount: 131 Package: SeqArray Version: 1.47.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 Archs: x64 MD5sum: bd1da5b4c84bb52286dfcadea011ce90 NeedsCompilation: yes Title: Data Management of Large-Scale Whole-Genome Sequence Variant Calls Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (ORCID: ), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: devel git_last_commit: 9bf676f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SeqArray_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqArray_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqArray_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqArray_1.47.0.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: GBScleanR, SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, ggmanh, VariantExperiment suggestsMe: DelayedDataFrame, HIBAG, VCFArray, GMMAT, MAGEE dependencyCount: 29 Package: seqCAT Version: 1.29.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE MD5sum: da150be4ad9db80deba7f975dbb1f4c4 NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: devel git_last_commit: ecf25e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seqCAT_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqCAT_1.29.0.zip vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 106 Package: seqcombo Version: 1.29.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 29bb57fcd4d81e833cff1140b87d6711 NeedsCompilation: no Title: Visualization Tool for Genetic Reassortment Description: Provides useful functions for visualizing virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: devel git_last_commit: 10d9bfc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seqcombo_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqcombo_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqcombo_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqcombo_1.29.0.tgz vignettes: vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 41 Package: SeqGate Version: 1.17.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 04377df3f7f609246fba239f9062e3f4 NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: devel git_last_commit: 9990f13 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/SeqGate_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqGate_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqGate_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqGate_1.17.0.tgz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 38 Package: SeqGSEA Version: 1.47.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 7b814061c3a0a89ed58ae58f949240d4 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: devel git_last_commit: eb9808f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SeqGSEA_1.47.0.tar.gz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 110 Package: seqLogo Version: 1.73.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: 8e7af1d9bc6c80e36f3e47203be24ee2 NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] (ORCID: ) Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: devel git_last_commit: 118d640 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seqLogo_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqLogo_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqLogo_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqLogo_1.73.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: rGADEM, generegulation importsMe: PWMEnrich, RCAS, rGADEM, riboSeqR, scanMiR, TFBSTools, kmeRtone suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb, universalmotif dependencyCount: 4 Package: seqPattern Version: 1.39.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: 065b4d39407d437f5f5f4640bc897a80 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: devel git_last_commit: f37ec96 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seqPattern_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqPattern_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqPattern_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqPattern_1.39.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 29 Package: seqsetvis Version: 1.27.2 Depends: R (>= 4.3), ggplot2 Imports: cowplot, data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, scales, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 491b2f20d99f5cfdc7f180c86a525fd5 NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2. biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] (ORCID: ) Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: devel git_last_commit: ae10577 git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-10 source.ver: src/contrib/seqsetvis_1.27.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqsetvis_1.27.2.zip vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 106 Package: SeqSQC Version: 1.29.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, plotly, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 1452849542e040121d65c6518543b47f NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: devel git_last_commit: 42fdfb6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SeqSQC_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqSQC_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqSQC_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqSQC_1.29.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 121 Package: seqTools Version: 1.41.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: c24c4d4c06682e30adb4e7e8e68e50a4 NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers git_url: https://git.bioconductor.org/packages/seqTools git_branch: devel git_last_commit: a203e78 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/seqTools_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/seqTools_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqTools_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqTools_1.41.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R importsMe: qckitfastq dependencyCount: 3 Package: SeqVarTools Version: 1.45.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 MD5sum: b9c7c7877ce8dd569f3955d70eb4327f NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: devel git_last_commit: 6162bf6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SeqVarTools_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SeqVarTools_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SeqVarTools_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SeqVarTools_1.45.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS suggestsMe: GMMAT, MAGEE dependencyCount: 96 Package: sesame Version: 1.25.1 Depends: R (>= 4.5.0), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, MASS, wheatmap (>= 0.2.0), GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache, GenomeInfoDb, stats, SummarizedExperiment, dplyr, reshape2 Suggests: scales, BiocManager, knitr, DNAcopy, e1071, randomForest, RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel, grDevices, KernSmooth, pals License: MIT + file LICENSE MD5sum: 25c6c5b9befd99f69002826d9a94f813 NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre] (ORCID: ), Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: devel git_last_commit: 8ed22f6 git_last_commit_date: 2024-11-30 Date/Publication: 2024-12-01 source.ver: src/contrib/sesame_1.25.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/sesame_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sesame_1.25.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sesame_1.25.1.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/KYCG.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", "5. knowYourCG", 3. Modeling, 2. Non-human Array, 1. Quality Control, "0. Basic Usage" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/inferences.R, vignettes/sesame/inst/doc/KYCG.R, vignettes/sesame/inst/doc/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: MethReg, CytoMethIC suggestsMe: knowYourCG, RnBeads, TCGAbiolinks, sesameData dependencyCount: 114 Package: SEtools Version: 1.21.0 Depends: R (>= 4.0), SummarizedExperiment, sechm Imports: BiocParallel, Matrix, DESeq2, S4Vectors, data.table, edgeR, openxlsx, pheatmap, stats, circlize, methods, sva Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: ab1af374a7e8f5a154a1006c244cb769 NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details). biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: devel git_last_commit: 17a9530 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SEtools_1.21.0.tar.gz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 129 Package: sevenbridges Version: 1.37.0 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: 429379eb4b52517590bd4f634231172a NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Phil Webster [aut, cre], Soner Koc [aut] (ORCID: ), Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Velsera [cph, fnd] Maintainer: Phil Webster URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: devel git_last_commit: 265369e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sevenbridges_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sevenbridges_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sevenbridges_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sevenbridges_1.37.0.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 31 Package: sevenC Version: 1.27.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: f12d0ee46257e854bcee6b2f72a0f1a5 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: devel git_last_commit: d2926f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sevenC_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sevenC_1.27.0.zip vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 86 Package: SGCP Version: 1.7.0 Depends: R (>= 4.2.0) Imports: ggplot2, expm, caret, plyr, dplyr, GO.db, annotate, SummarizedExperiment, genefilter, GOstats, RColorBrewer, xtable, Rgraphviz, reshape2, openxlsx, ggridges, DescTools, org.Hs.eg.db, methods, grDevices, stats, RSpectra, graph Suggests: knitr, rmarkdown, BiocManager, devtools, BiocStyle License: GPL-3 MD5sum: 3d5827493aa07ea1ebb580e3f210b77d NeedsCompilation: no Title: SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks Description: SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules. biocViews: GeneExpression, GeneSetEnrichment, NetworkEnrichment, SystemsBiology, Classification, Clustering, DimensionReduction, GraphAndNetwork, NeuralNetwork, Network, mRNAMicroarray, RNASeq, Visualization Author: Niloofar AghaieAbiane [aut, cre] (ORCID: ), Ioannis Koutis [aut] Maintainer: Niloofar AghaieAbiane URL: https://github.com/na396/SGCP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGCP git_branch: devel git_last_commit: ba0ddcc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SGCP_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SGCP_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SGCP_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SGCP_1.7.0.tgz vignettes: vignettes/SGCP/inst/doc/SGCP.html vignetteTitles: SGCP package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGCP/inst/doc/SGCP.R dependencyCount: 165 Package: SGSeq Version: 1.41.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 2ca6dac8c6a3f2daf36e7e520161aac7 NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: devel git_last_commit: 193e8a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SGSeq_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SGSeq_1.41.0.zip vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 suggestsMe: FRASER dependencyCount: 81 Package: SharedObject Version: 1.21.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 4ba71128da2350a82321ba87216e5564 NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: devel git_last_commit: b1ad203 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SharedObject_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SharedObject_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SharedObject_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SharedObject_1.21.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html, vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStartChinese, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R, vignettes/SharedObject/inst/doc/quick_start_guide.R importsMe: NewWave dependencyCount: 8 Package: shiny.gosling Version: 1.3.0 Imports: htmltools, jsonlite, rlang, shiny, shiny.react, fs, digest, rjson Suggests: config, covr, knitr, lintr, mockery (>= 0.4.3), rcmdcheck, rmarkdown, sessioninfo, spelling, testthat (>= 3.0.0), GenomicRanges, VariantAnnotation, StructuralVariantAnnotation, biovizBase, ggbio License: LGPL-3 MD5sum: dc5dce6416549e37921a4d52f9385377 NeedsCompilation: no Title: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization for R and Shiny Description: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization. http://gosling-lang.org/. This R package is based on gosling.js. It uses R functions to create gosling plots that could be embedded onto R Shiny apps. biocViews: ShinyApps, Genetics, Visualization Author: Appsilon [aut, cre], Anirban Shaw [aut] (ORCID: ), Federico Rivadeneira [aut] (ORCID: ), Vedha Viyash [aut] Maintainer: Appsilon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shiny.gosling git_branch: devel git_last_commit: 606472a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/shiny.gosling_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shiny.gosling_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shiny.gosling_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shiny.gosling_1.3.0.tgz vignettes: vignettes/shiny.gosling/inst/doc/GRanges.html, vignettes/shiny.gosling/inst/doc/intro.html, vignettes/shiny.gosling/inst/doc/lineChart.html, vignettes/shiny.gosling/inst/doc/textAnnotation.html, vignettes/shiny.gosling/inst/doc/VCF.html vignetteTitles: 2. Using a GRanges object in shiny.gosling, 1. Introduction to shiny.gosling, 4. Creating an Interactive Line Chart with shiny.gosling, 5. Creating a Multi-Scale Sequence Track, 3. Creating a Circos Plot with VCF Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shiny.gosling/inst/doc/GRanges.R, vignettes/shiny.gosling/inst/doc/intro.R, vignettes/shiny.gosling/inst/doc/lineChart.R, vignettes/shiny.gosling/inst/doc/textAnnotation.R, vignettes/shiny.gosling/inst/doc/VCF.R importsMe: gINTomics suggestsMe: AlphaMissenseR dependencyCount: 42 Package: shinyepico Version: 1.15.0 Depends: R (>= 4.3.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>= 1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>= 1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>= 4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>= 2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets (>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0), shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData, BiocStyle License: AGPL-3 + file LICENSE MD5sum: c1674a9ccf730dcfc90f57b82aadb430 NeedsCompilation: no Title: ShinyÉPICo Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis. biocViews: DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl Author: Octavio Morante-Palacios [cre, aut] Maintainer: Octavio Morante-Palacios URL: https://github.com/omorante/shiny_epico VignetteBuilder: knitr BugReports: https://github.com/omorante/shiny_epico/issues git_url: https://git.bioconductor.org/packages/shinyepico git_branch: devel git_last_commit: 072d986 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/shinyepico_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shinyepico_1.15.0.zip vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html vignetteTitles: shinyepico hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R dependencyCount: 210 Package: shinyMethyl Version: 1.43.0 Imports: Biobase, BiocGenerics, graphics, grDevices, htmltools, MatrixGenerics, methods, minfi, RColorBrewer, shiny, stats, utils Suggests: shinyMethylData, minfiData, BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 0d184057a18ba5a353abf33a73a564c0 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/shinyMethyl VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/shinyMethyl git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: devel git_last_commit: 9722e53 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/shinyMethyl_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/shinyMethyl_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/shinyMethyl_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/shinyMethyl_1.43.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.html vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 158 Package: ShortRead Version: 1.65.0 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), pwalign, hwriter, methods, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi, knitr License: Artistic-2.0 MD5sum: f9f54bc4872f13981aeb6aac8fceb014 NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Michael Lawrence [ctb], Simon Anders [ctb], Rohit Satyam [ctb] (Converted Overview.Rnw vignette from Sweave to RMarkdown / HTML.), J Wokaty [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/ShortRead, https://github.com/Bioconductor/ShortRead, https://support.bioconductor.org/tag/ShortRead VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ShortRead/issues git_url: https://git.bioconductor.org/packages/ShortRead git_branch: devel git_last_commit: ad4e1df git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ShortRead_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ShortRead_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ShortRead_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ShortRead_1.65.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.html vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing, STRMPS importsMe: amplican, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, seqpac, DBTC, genBaRcode suggestsMe: BiocParallel, CSAR, FLAMES, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq dependencyCount: 63 Package: SICtools Version: 1.37.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: e1fa16720baaa4a664c2da761243b20d NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: devel git_last_commit: e95cf5c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SICtools_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SICtools_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SICtools_1.37.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 54 Package: sigFeature Version: 1.25.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 76621a4696c3605ab898e5d3e8c3cc69 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: devel git_last_commit: ac23f1c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sigFeature_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sigFeature_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sigFeature_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sigFeature_1.25.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.html vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 78 Package: SigFuge Version: 1.45.0 Depends: R (>= 3.5.0), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: 15239dd76df577d36c5fb139b40c8d80 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: devel git_last_commit: 3cd33ce git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SigFuge_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SigFuge_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SigFuge_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SigFuge_1.45.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R dependencyCount: 59 Package: siggenes Version: 1.81.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: 79ef511a2742356a579626b0fa3ef781 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: devel git_last_commit: 60ae981 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/siggenes_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/siggenes_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/siggenes_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/siggenes_1.81.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: minfi, trio, XDE, DeSousa2013, INCATome, NPFD suggestsMe: logicFS dependencyCount: 17 Package: sights Version: 1.33.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 88ffe576205054ad34274ab0ce6e87ac NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: devel git_last_commit: 7b43510 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sights_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sights_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sights_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sights_1.33.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 42 Package: signatureSearch Version: 1.21.0 Depends: R(>= 4.2.0), Rcpp, SummarizedExperiment, org.Hs.eg.db Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray, BiocGenerics, tibble LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, signatureSearchData, DT License: Artistic-2.0 MD5sum: aa73bd93f9f8ced2122944da404fc2fc NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut] Maintainer: Brendan Gongol URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: devel git_last_commit: eef2cb2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/signatureSearch_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/signatureSearch_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/signatureSearch_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/signatureSearch_1.21.0.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R importsMe: DFD dependencyCount: 173 Package: signeR Version: 2.9.0 Depends: R (>= 3.0.2), VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus, parallel, pvclust, ppclust, clue, survival, maxstat, survivalAnalysis, future, VGAM, MASS, kknn, glmnet, e1071, randomForest, ada, future.apply, ggplot2, pROC, pheatmap, RColorBrewer, listenv, reshape2, scales, survminer, dplyr, ggpubr, cowplot, tibble, readr, shiny, shinydashboard, shinycssloaders, shinyWidgets, bsplus, DT, magrittr, tidyr, BiocFileCache, proxy, rtracklayer, BSgenome LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, rmarkdown License: GPL-3 MD5sum: f13426636d125c443aef56b1c11b2c2b NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Alexandre Defelicibus, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/TojalLab/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: devel git_last_commit: 1531a16 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/signeR_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/signeR_2.9.0.zip vignettes: vignettes/signeR/inst/doc/signeR-vignette.html, vignettes/signeR/inst/doc/signeRFlow.html vignetteTitles: signeR, signeRFlow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R, vignettes/signeR/inst/doc/signeRFlow.R dependencyCount: 242 Package: SigsPack Version: 1.21.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d6c995d1c3a199501e7407fd1c8a2f2e NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: devel git_last_commit: 7943dfb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SigsPack_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SigsPack_1.21.0.zip vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 81 Package: sigsquared Version: 1.39.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 969d6031d4182d7acdf7db957b0c7b28 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: devel git_last_commit: 1fece7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sigsquared_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sigsquared_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sigsquared_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sigsquared_1.39.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 13 Package: SIM Version: 1.77.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: x64 MD5sum: d3388641da50862f2f7679f211038346 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: devel git_last_commit: 6065456 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SIM_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIM_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIM_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIM_1.77.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 60 Package: SIMAT Version: 1.39.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 9ff628b3a07b5b04a9c697fa5238ab32 NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: devel git_last_commit: bb9e288 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SIMAT_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMAT_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMAT_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMAT_1.39.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 48 Package: SIMD Version: 1.25.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: 86057a073f9048360ed7c703fdc36657 NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: devel git_last_commit: 6259f02 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SIMD_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMD_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMD_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMD_1.25.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 12 Package: SimFFPE Version: 1.19.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 973c41b699285abed9b8be8afd7c9846 NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: devel git_last_commit: 77724cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SimFFPE_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SimFFPE_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SimFFPE_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SimFFPE_1.19.0.tgz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 58 Package: similaRpeak Version: 1.39.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 055293d24db39fa59c1ce8ddb237ed42 NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: devel git_last_commit: e8beccd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/similaRpeak_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/similaRpeak_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/similaRpeak_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/similaRpeak_1.39.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R dependencyCount: 2 Package: SIMLR Version: 1.33.0 Depends: R (>= 4.1.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: e13f7b726b4e66e3c3d38e76bd5e0364 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut] (ORCID: ), Bo Wang [aut], Luca De Sano [cre, aut] (ORCID: ), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: devel git_last_commit: 5ff2f17 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SIMLR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SIMLR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIMLR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SIMLR_1.33.0.tgz vignettes: vignettes/SIMLR/inst/doc/v1_introduction.html, vignettes/SIMLR/inst/doc/v2_running_SIMLR.html vignetteTitles: Introduction, Running SIMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/v1_introduction.R, vignettes/SIMLR/inst/doc/v2_running_SIMLR.R dependencyCount: 14 Package: simona Version: 1.5.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp, matrixStats, GetoptLong, grid, GlobalOptions, igraph, Polychrome, S4Vectors, xml2 (>= 1.3.3), circlize, ComplexHeatmap, grDevices, stats, utils, shiny LinkingTo: Rcpp Suggests: knitr, testthat, BiocManager, GO.db, org.Hs.eg.db, proxyC, AnnotationDbi, Matrix, DiagrammeR, ragg, png, InteractiveComplexHeatmap, UniProtKeywords, simplifyEnrichment, AnnotationHub, jsonlite License: MIT + file LICENSE MD5sum: b4028707a6654c3481f40aa4e510674d NeedsCompilation: yes Title: Semantic Similarity on Bio-Ontologies Description: This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. biocViews: Software, Annotation, GO, BiomedicalInformatics Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simona SystemRequirements: Perl, Java VignetteBuilder: knitr BugReports: https://github.com/jokergoo/simona/issues git_url: https://git.bioconductor.org/packages/simona git_branch: devel git_last_commit: 40a2e14 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/simona_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simona_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simona_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simona_1.5.0.tgz vignettes: vignettes/simona/inst/doc/simona.html vignetteTitles: The simona package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: simplifyEnrichment dependencyCount: 68 Package: simPIC Version: 1.3.0 Depends: R (>= 4.4.0), SingleCellExperiment Imports: BiocGenerics, checkmate (>= 2.0.0), fitdistrplus, matrixStats, Matrix, stats, SummarizedExperiment, actuar, rlang, S4Vectors, methods, scales, scuttle Suggests: ggplot2 (>= 3.4.0), knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: f517575958847c5d84aed00102610578 NeedsCompilation: no Title: simPIC: flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data Description: simPIC is a package for simulating single-cell ATAC-seq count data. It provides a user-friendly, well documented interface for data simulation. Functions are provided for parameter estimation, realistic scATAC-seq data simulation, and comparing real and simulated datasets. biocViews: SingleCell, ATACSeq, Software, Sequencing, ImmunoOncology, DataImport Author: Sagrika Chugh [aut, cre] (ORCID: ), Davis McCarthy [aut], Heejung Shim [aut] Maintainer: Sagrika Chugh URL: https://github.com/sagrikachugh/simPIC VignetteBuilder: knitr BugReports: https://github.com/sagrikachugh/simPIC/issues git_url: https://git.bioconductor.org/packages/simPIC git_branch: devel git_last_commit: 17e7089 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/simPIC_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simPIC_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simPIC_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simPIC_1.3.0.tgz vignettes: vignettes/simPIC/inst/doc/vignette.html vignetteTitles: simPIC: simulating single-cell ATAC-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simPIC/inst/doc/vignette.R dependencyCount: 71 Package: simpleSeg Version: 1.9.0 Depends: R (>= 3.5.0) Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors, grDevices, SummarizedExperiment, methods, cytomapper Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2 License: GPL-3 MD5sum: 9cec74dec8117ed31fcd419c9b16df5a NeedsCompilation: no Title: A package to perform simple cell segmentation Description: Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images. biocViews: Classification, Survival, SingleCell, Normalization, Spatial Author: Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick URL: https://sydneybiox.github.io/simpleSeg/ https://github.com/SydneyBioX/simpleSeg VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/simpleSeg/issues git_url: https://git.bioconductor.org/packages/simpleSeg git_branch: devel git_last_commit: 4649541 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/simpleSeg_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simpleSeg_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simpleSeg_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simpleSeg_1.9.0.tgz vignettes: vignettes/simpleSeg/inst/doc/simpleSeg.html vignetteTitles: "Introduction to simpleSeg" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSeg/inst/doc/simpleSeg.R importsMe: lisaClust, spicyR suggestsMe: spicyWorkflow dependencyCount: 152 Package: simplifyEnrichment Version: 2.1.0 Depends: R (>= 4.0.0) Imports: simona, ComplexHeatmap (>= 2.7.4), grid, circlize, GetoptLong, digest, tm, GO.db, AnnotationDbi, slam, methods, clue, grDevices, stats, utils, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: 1ae5932059a0afe6322991afe755ed6a NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: devel git_last_commit: 5590aab git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/simplifyEnrichment_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/simplifyEnrichment_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/simplifyEnrichment_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/simplifyEnrichment_2.1.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html vignetteTitles: The simplifyEnrichment package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: cola, InteractiveComplexHeatmap, simona, scITD dependencyCount: 94 Package: sincell Version: 1.39.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) MD5sum: 99fc53dbaa9cb5aced6c634bb0354f91 NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: devel git_last_commit: e455929 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sincell_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sincell_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sincell_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sincell_1.39.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R dependencyCount: 59 Package: SingleCellAlleleExperiment Version: 1.3.0 Depends: R (>= 4.4.0), SingleCellExperiment Imports: SummarizedExperiment, BiocParallel, DelayedArray, methods, utils, Matrix, S4Vectors, stats Suggests: scaeData, knitr, rmarkdown, BiocStyle, scran, scater, scuttle, ggplot2, patchwork, org.Hs.eg.db, AnnotationDbi, DropletUtils, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 8f2ffe18b1339d3792860dbdb7299314 NeedsCompilation: no Title: S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes Description: Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis. biocViews: DataRepresentation, Infrastructure, SingleCell, Transcriptomics, GeneExpression, Genetics, ImmunoOncology, DataImport Author: Jonas Schuck [aut, cre] (ORCID: ), Ahmad Al Ajami [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Katharina Imkeller [aut] (ORCID: ) Maintainer: Jonas Schuck URL: https://github.com/AGImkeller/SingleCellAlleleExperiment VignetteBuilder: knitr BugReports: https://github.com/AGImkeller/SingleCellAlleleExperiment/issues git_url: https://git.bioconductor.org/packages/SingleCellAlleleExperiment git_branch: devel git_last_commit: cdd2082 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SingleCellAlleleExperiment_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellAlleleExperiment_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleCellAlleleExperiment_1.3.0.tgz vignettes: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.html vignetteTitles: An introduction to the SingleCellAlleleExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.R suggestsMe: scaeData dependencyCount: 48 Package: SingleCellExperiment Version: 1.29.1 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 Archs: x64 MD5sum: 85ff5758fcae65fb74f9581562ea293a NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb], Luke Zappia [ctb] (ORCID: , github: lazappi) Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: devel git_last_commit: 6ed3996 git_last_commit_date: 2024-11-08 Date/Publication: 2024-11-10 source.ver: src/contrib/SingleCellExperiment_1.29.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleCellExperiment_1.29.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellExperiment_1.29.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleCellExperiment_1.29.1.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html, vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Applying over a SingleCellExperiment object, 3. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R, vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: alabaster.sce, BASiCS, batchelor, BayesSpace, CATALYST, celda, CellBench, CelliD, CellTrails, CHETAH, clusterExperiment, cydar, cytomapper, demuxSNP, dreamlet, DropletUtils, epiregulon, epiregulon.extra, ExperimentSubset, iSEE, iSEEhub, iSEEindex, LoomExperiment, lute, MAST, mia, mumosa, POWSC, scAnnotatR, scater, scDblFinder, scGPS, schex, scPipe, scran, scuttle, scviR, simPIC, SingleCellAlleleExperiment, singleCellTK, SiPSiC, SpatialExperiment, splatter, switchde, TENxIO, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scMultiome, scRNAseq, STexampleData, TENxBrainData, TENxPBMCData, TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO, imcExperiment, karyotapR importsMe: ADImpute, aggregateBioVar, airpart, APL, ASURAT, Banksy, BASiCStan, bayNorm, BUSseq, CatsCradle, ccfindR, ccImpute, CDI, CellMixS, Cepo, ChromSCape, CiteFuse, ClusterFoldSimilarity, clustifyr, CoGAPS, concordexR, condiments, corral, COTAN, CTexploreR, CuratedAtlasQueryR, cytofQC, cytoviewer, decontX, destiny, DifferentialRegulation, Dino, distinct, dittoSeq, escape, escheR, EWCE, FEAST, fishpond, FLAMES, ggsc, ggspavis, glmGamPoi, GloScope, GSVA, HIPPO, ILoReg, imcRtools, infercnv, iSEEfier, iSEEtree, iSEEu, lemur, lisaClust, mastR, mbkmeans, MEB, MetaNeighbor, miaViz, miloR, miQC, MPAC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, partCNV, peco, pipeComp, raer, RCSL, RegionalST, SC3, SCArray, scBFA, scCB2, sccomp, scDD, scDDboost, scDesign3, scDiagnostics, scDotPlot, scds, scHOT, scmap, scMerge, scMET, SCnorm, scone, scp, scReClassify, scRepertoire, scRNAseqApp, scruff, scry, scTensor, scTGIF, scTreeViz, slalom, slingshot, Spaniel, SpaNorm, SpatialFeatureExperiment, spatialHeatmap, speckle, spicyR, SplineDV, SPOTlight, SpotSweeper, standR, Statial, tidySpatialExperiment, tpSVG, tradeSeq, treekoR, UCell, VAExprs, VDJdive, velociraptor, VisiumIO, visiumStitched, Voyager, waddR, xenLite, zellkonverter, HCATonsilData, MerfishData, raerdata, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, mixhvg, nebula, SC.MEB, SCIntRuler, SCRIP, scROSHI, SpatialDDLS suggestsMe: ANCOMBC, cellxgenedp, CTdata, DEsingle, dominoSignal, FuseSOM, genomicInstability, hca, HDF5Array, InteractiveComplexHeatmap, M3Drop, microSTASIS, MOFA2, MOSim, ontoProc, phenopath, progeny, QFeatures, ReactomeGSA, scBubbletree, scFeatureFilter, scPCA, scRecover, SingleR, sketchR, SummarizedExperiment, tidytof, TREG, updateObject, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, CytoSimplex, dyngen, harmony, Platypus, RaceID, rliger, SCdeconR, SCORPIUS, Seurat, singleCellHaystack, SuperCell, tidydr dependencyCount: 37 Package: SingleCellSignalR Version: 1.19.0 Depends: R (>= 4.0) Imports: BiocManager, circlize, limma, igraph, gplots, grDevices, edgeR, data.table, pheatmap, stats, Rtsne, graphics, stringr, foreach, multtest, scran, utils, Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 0300a9dd57604bec47533b81ae2ddcda NeedsCompilation: no Title: Cell Signalling Using Single Cell RNAseq Data Analysis Description: Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions. biocViews: SingleCell, Network, Clustering, RNASeq, Classification Author: Simon Cabello-Aguilar Developer [aut], Jacques Colinge Developer [aut, cre] Maintainer: Jacques Colinge Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: devel git_last_commit: f0f49f1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SingleCellSignalR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleCellSignalR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellSignalR_1.19.0.tgz vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R importsMe: scFeatures suggestsMe: tidySingleCellExperiment, scDiffCom dependencyCount: 103 Package: singleCellTK Version: 2.15.2 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, anndata, AnnotationHub, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, ensembldb, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, grid, GSVA (>= 1.50.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix (>= 1.6-1), matrixStats, methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, stringr, SoupX, sva, reshape2, shinyalert, circlize, enrichR (>= 3.2), celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, tidyr, eds, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN, TrajectoryUtils, scuttle, utils, stats, zellkonverter, tidyr Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling, org.Mm.eg.db, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0), harmony, SeuratObject, optparse License: MIT + file LICENSE MD5sum: 04200ec40b491ccd0895cd26ee8deb0c NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology, BatchEffect, Normalization, QualityControl, DataImport, GUI Author: Yichen Wang [aut] (ORCID: ), Irzam Sarfraz [aut] (ORCID: ), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida Pervaiz [aut], David Jenkins [aut] (ORCID: ), Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut] (ORCID: ), Ming Liu [aut], Joshua David Campbell [aut, cre] (ORCID: ) Maintainer: Joshua David Campbell URL: https://www.camplab.net/sctk/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: devel git_last_commit: d91daa2 git_last_commit_date: 2024-10-22 Date/Publication: 2024-10-25 source.ver: src/contrib/singleCellTK_2.15.2.tar.gz vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html vignetteTitles: 1. Introduction to singleCellTK hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R suggestsMe: celda dependencyCount: 385 Package: SingleMoleculeFootprinting Version: 2.1.0 Depends: R (>= 4.4.1) Imports: BiocGenerics, Biostrings, BSgenome, dplyr, GenomeInfoDb, GenomicRanges, ggpointdensity, ggplot2, ggrepel, grDevices, IRanges, Matrix, parallel, patchwork, plyr, plyranges, QuasR, RColorBrewer, rlang, S4Vectors, stats, stringr, tibble, tidyr, tidyverse, viridis Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, qs, rmarkdown, readr, SingleMoleculeFootprintingData, testthat (>= 3.0.0) License: GPL-3 MD5sum: 74f94c106d47d05e78d1b197804566c3 NeedsCompilation: no Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location. biocViews: DNAMethylation, Coverage, NucleosomePositioning, DataRepresentation, Epigenetics, MethylSeq, QualityControl, Sequencing Author: Guido Barzaghi [aut, cre] (ORCID: ), Arnaud Krebs [aut] (ORCID: ), Mike Smith [ctb] (ORCID: ) Maintainer: Guido Barzaghi VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues git_url: https://git.bioconductor.org/packages/SingleMoleculeFootprinting git_branch: devel git_last_commit: 4352241 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/SingleMoleculeFootprinting_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleMoleculeFootprinting_2.1.0.zip vignettes: vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.html, vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.html, vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.html vignetteTitles: FootprintCharter.html, methylation_calling_and_QCs.html, single_molecule_sorting_by_TF.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.R, vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.R, vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.R dependencyCount: 184 Package: SingleR Version: 2.9.3 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocNeighbors, stats, utils, Rcpp, beachmat (>= 2.23.5) LinkingTo: Rcpp, beachmat, assorthead, BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scrapper, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 MD5sum: 2fdbfabe23fbbee0cd42e88975394b8e NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/SingleR-inc/SingleR SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SingleR-inc/SingleR/issues git_url: https://git.bioconductor.org/packages/SingleR git_branch: devel git_last_commit: da88210 git_last_commit_date: 2024-12-15 Date/Publication: 2024-12-16 source.ver: src/contrib/SingleR_2.9.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/SingleR_2.9.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SingleR_2.9.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SingleR_2.9.3.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.workflows, SingleRBook importsMe: singleCellTK suggestsMe: scDiagnostics, sketchR, tidySingleCellExperiment, tidyseurat dependencyCount: 53 Package: singscore Version: 1.27.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: pkgdown, BiocStyle, hexbin, knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: e9c29b198a42e63996f7164215957607 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dharmesh D. Bhuva [aut] (ORCID: ), Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (ORCID: ), Malvika Kharbanda [aut, cre] (ORCID: ) Maintainer: Malvika Kharbanda URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: devel git_last_commit: b18b6f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/singscore_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/singscore_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/singscore_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/singscore_1.27.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: TBSignatureProfiler, SingscoreAMLMutations, clustermole, GSEMA suggestsMe: mastR, vissE, msigdb dependencyCount: 129 Package: SiPSiC Version: 1.7.0 Depends: Matrix, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle License: file LICENSE MD5sum: 9adda7dd2b14f02bf11f1d6afe01755f NeedsCompilation: no Title: Calculate Pathway Scores for Each Cell in scRNA-Seq Data Description: Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells. biocViews: Software, DifferentialExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, Transcriptomics, RNASeq, SingleCell, Transcription, Sequencing, ImmunoOncology, DataImport Author: Daniel Davis [aut, cre] (ORCID: ), Yotam Drier [aut] Maintainer: Daniel Davis URL: https://www.genome.org/cgi/doi/10.1101/gr.278431.123 VignetteBuilder: knitr BugReports: https://github.com/DanielDavis12/SiPSiC/issues git_url: https://git.bioconductor.org/packages/SiPSiC git_branch: devel git_last_commit: 688dfb8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SiPSiC_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SiPSiC_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SiPSiC_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SiPSiC_1.7.0.tgz vignettes: vignettes/SiPSiC/inst/doc/SiPSiC.html vignetteTitles: Infer Biological Pathway Activity from Single-Cell RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SiPSiC/inst/doc/SiPSiC.R dependencyCount: 38 Package: sitadela Version: 1.15.0 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, txdbmaker, utils Suggests: BiocStyle, BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 Archs: x64 MD5sum: f4c0bc1261f66e06b1f6880fd914ca1a NeedsCompilation: no Title: An R package for the easy provision of simple but complete tab-delimited genomic annotation from a variety of sources and organisms Description: Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported. biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing, Transcriptomics, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/sitadela VignetteBuilder: knitr BugReports: https://github.com/pmoulos/sitadela/issues git_url: https://git.bioconductor.org/packages/sitadela git_branch: devel git_last_commit: 76eef2f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sitadela_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sitadela_1.15.0.zip vignettes: vignettes/sitadela/inst/doc/sitadela.html vignetteTitles: Building a simple annotation database hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sitadela/inst/doc/sitadela.R dependencyCount: 102 Package: sitePath Version: 1.23.0 Depends: R (>= 4.1) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE Archs: x64 MD5sum: ac7e259bcb769c7e4356ea79149acfa4 NeedsCompilation: yes Title: Phylogeny-based sequence clustering with site polymorphism Description: Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (ORCID: ), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: devel git_last_commit: abc9ced git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sitePath_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sitePath_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sitePath_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sitePath_1.23.0.tgz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 68 Package: sizepower Version: 1.77.0 Depends: stats License: LGPL MD5sum: c879d4604ceb21dc257087acb1be0708 NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: devel git_last_commit: 4adad42 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sizepower_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sizepower_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sizepower_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sizepower_1.77.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: sketchR Version: 1.3.0 Imports: basilisk, Biobase, DelayedArray, dplyr, ggplot2, methods, reticulate, rlang, scales, stats Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TENxPBMCData, scuttle, scran, scater, SingleR, celldex, cowplot, SummarizedExperiment, beachmat.hdf5, BiocStyle, BiocManager, SingleCellExperiment License: MIT + file LICENSE MD5sum: b608ebbd307fd2b9bbb42f9d80457fa0 NeedsCompilation: no Title: An R interface for python subsampling/sketching algorithms Description: Provides an R interface for various subsampling algorithms implemented in python packages. Currently, interfaces to the geosketch and scSampler python packages are implemented. In addition it also provides diagnostic plots to evaluate the subsampling. biocViews: SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/sketchR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/sketchR/issues git_url: https://git.bioconductor.org/packages/sketchR git_branch: devel git_last_commit: 87da4c7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/sketchR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sketchR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sketchR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sketchR_1.3.0.tgz vignettes: vignettes/sketchR/inst/doc/sketchR.html vignetteTitles: sketchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sketchR/inst/doc/sketchR.R dependencyCount: 66 Package: skewr Version: 1.39.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Archs: x64 MD5sum: 87b97a07eaadb02ccb66b9641203b06c NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: devel git_last_commit: d7cf1ed git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/skewr_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/skewr_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/skewr_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/skewr_1.39.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 176 Package: slalom Version: 1.29.0 Depends: R (>= 4.0) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat License: GPL-2 MD5sum: 557dfff3a3b82fe42987791b96217610 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome, KEGG Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: devel git_last_commit: 9d5e3d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/slalom_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/slalom_1.29.0.zip vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 87 Package: slingshot Version: 2.15.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: 38908959724cbd9de3a2f88ce401522d NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (ORCID: , github: rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: devel git_last_commit: cf41eec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/slingshot_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/slingshot_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/slingshot_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/slingshot_2.15.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology: Comparing Conditions along a Trajectory, Slingshot: Trajectory Inference for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R importsMe: condiments, scRNAseqApp, tradeSeq suggestsMe: Platypus, RaceID dependencyCount: 50 Package: SLqPCR Version: 1.73.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) Archs: x64 MD5sum: c3be6921c898cb49024a14a1fb115b73 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: devel git_last_commit: 988a269 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SLqPCR_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SLqPCR_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SLqPCR_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SLqPCR_1.73.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R dependencyCount: 1 Package: SMAD Version: 1.23.0 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 336445097ef61aab2f372cb230df60f3 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to protein interactions captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: devel git_last_commit: 78f48cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SMAD_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SMAD_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SMAD_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SMAD_1.23.0.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 30 Package: smartid Version: 1.3.2 Depends: R (>= 4.4) Imports: dplyr, ggplot2, graphics, Matrix, mclust, methods, mixtools, sparseMatrixStats, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, dbscan, ggpubr, knitr, rmarkdown, scater, splatter, testthat (>= 3.0.0), tidytext, UpSetR License: MIT + file LICENSE MD5sum: fe28a5e585f9e568d17bf326e5d3b5e0 NeedsCompilation: no Title: Scoring and Marker Selection Method Based on Modified TF-IDF Description: This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package. biocViews: Software, GeneExpression, Transcriptomics Author: Jinjin Chen [aut, cre] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/smartid VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/smartid/issues git_url: https://git.bioconductor.org/packages/smartid git_branch: devel git_last_commit: a939065 git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-13 source.ver: src/contrib/smartid_1.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/smartid_1.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/smartid_1.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/smartid_1.3.2.tgz vignettes: vignettes/smartid/inst/doc/smartid_Demo.html vignetteTitles: smartid: Scoring and MARker selection method based on modified Tf-IDf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smartid/inst/doc/smartid_Demo.R dependencyCount: 103 Package: SMITE Version: 1.35.0 Depends: R (>= 3.5), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 38100371870105e480ea68dde5b61bdb NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: devel git_last_commit: 92d4d6f git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/SMITE_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SMITE_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SMITE_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SMITE_1.35.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 155 Package: smoothclust Version: 1.3.0 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, sparseMatrixStats, spdep, methods, utils Suggests: BiocStyle, knitr, STexampleData, scuttle, scran, scater, ggspavis, testthat License: MIT + file LICENSE Archs: x64 MD5sum: edb78a4eb65fe7382f074c054b9aed07 NeedsCompilation: no Title: smoothclust Description: Method for segmentation of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Clustering Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/smoothclust VignetteBuilder: knitr BugReports: https://github.com/lmweber/smoothclust/issues git_url: https://git.bioconductor.org/packages/smoothclust git_branch: devel git_last_commit: 28cfdec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/smoothclust_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/smoothclust_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/smoothclust_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/smoothclust_1.3.0.tgz vignettes: vignettes/smoothclust/inst/doc/smoothclust.html vignetteTitles: Smoothclust Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smoothclust/inst/doc/smoothclust.R dependencyCount: 89 Package: SNAGEE Version: 1.47.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 083aa3b4ac8d16912ea6481368ae4d99 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: devel git_last_commit: 0372fc0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SNAGEE_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNAGEE_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNAGEE_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNAGEE_1.47.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapcount Version: 1.19.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 3762fb8b748da42deed55778f803d2aa NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles [aut, cre] Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: devel git_last_commit: 9751037 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/snapcount_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snapcount_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snapcount_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snapcount_1.19.0.tgz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 48 Package: snifter Version: 1.17.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 MD5sum: 19b8495fececeb77b9146fd6fd99f08a NeedsCompilation: no Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/snifter VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: devel git_last_commit: 4fd5628 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/snifter_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snifter_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snifter_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snifter_1.17.0.tgz vignettes: vignettes/snifter/inst/doc/snifter.html vignetteTitles: Introduction to snifter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snifter/inst/doc/snifter.R suggestsMe: scater dependencyCount: 26 Package: snm Version: 1.55.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL Archs: x64 MD5sum: bcdb31a1913dcb3914c5037bbb546208 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: devel git_last_commit: 1e313ea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/snm_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snm_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snm_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snm_1.55.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: ExpressionNormalizationWorkflow dependencyCount: 19 Package: SNPediaR Version: 1.33.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: c3d005be6b134b56ffd3b5d31c88ae01 NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: devel git_last_commit: 33c74c4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SNPediaR_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNPediaR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNPediaR_1.33.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.37.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb (>= 1.34.8), BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: a923772d4b9a033039294750edf85c3b NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: mailto: git_url: https://git.bioconductor.org/packages/SNPhood git_branch: devel git_last_commit: 5ebb343 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SNPhood_1.37.0.tar.gz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 113 Package: SNPRelate Version: 1.41.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: 803cd26d54adab0f6280b48aaa46ad0a NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: devel git_last_commit: 5e409c6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SNPRelate_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SNPRelate_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SNPRelate_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SNPRelate_1.41.0.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: RAIDS, SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.57.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 MD5sum: 3481fd35700bf9c478a5758808e8ed22 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: devel git_last_commit: a3f434d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/snpStats_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/snpStats_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/snpStats_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/snpStats_1.57.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: MAGAR importsMe: cardelino, DExMA, GeneGeneInteR, gwascat, martini, RVS, scoreInvHap, GenomicTools.fileHandler, gpcp, GWASbyCluster, PhenotypeSimulator, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome, omicsPrint, VariantAnnotation, adjclust, dartR, dartR.base, dartR.popgen, genio, pegas, statgenGWAS dependencyCount: 13 Package: soGGi Version: 1.39.0 Depends: R (>= 3.5.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: acc79d4ade6046652b0a9afc0d9d3474 NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: devel git_last_commit: 192a969 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/soGGi_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/soGGi_1.39.0.zip vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: profileplyr dependencyCount: 101 Package: SomaticSignatures Version: 2.43.0 Depends: R (>= 3.5.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE Archs: x64 MD5sum: 5fb7b437eb808e4de069b2695f7e2c5a NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: devel git_last_commit: f592f53 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SomaticSignatures_2.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SomaticSignatures_2.43.0.zip vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: YAPSA dependencyCount: 172 Package: SpaceMarkers Version: 1.3.1 Depends: R (>= 4.4.0) Imports: matrixStats, matrixTests, rstatix, spatstat.explore, spatstat.geom, ape, hdf5r, nanoparquet, jsonlite, Matrix, qvalue, stats, utils, methods Suggests: data.table, devtools, dplyr, ggplot2, hrbrthemes, knitr, RColorBrewer, cowplot, readbitmap, rjson, rmarkdown, BiocStyle, testthat (>= 3.0.0), viridis, CoGAPS Enhances: BiocParallel License: MIT + file LICENSE MD5sum: 56411ef9bde270c74106bd08a46b2b28 NeedsCompilation: no Title: Spatial Interaction Markers Description: Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics. biocViews: SingleCell, GeneExpression, Software, Spatial, Transcriptomics Author: Atul Deshpande [aut, cre] (ORCID: ), Ludmila Danilova [ctb], Dmitrijs Lvovs [ctb] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/DeshpandeLab/SpaceMarkers VignetteBuilder: knitr BugReports: https://github.com/DeshpandeLab/SpaceMarkers/issues git_url: https://git.bioconductor.org/packages/SpaceMarkers git_branch: devel git_last_commit: 5bd997f git_last_commit_date: 2024-10-31 Date/Publication: 2024-10-31 source.ver: src/contrib/SpaceMarkers_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpaceMarkers_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpaceMarkers_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpaceMarkers_1.3.1.tgz vignettes: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.html vignetteTitles: Inferring Immune Interactions in Breast Cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.R dependencyCount: 94 Package: Spaniel Version: 1.21.0 Depends: R (>= 4.0) Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr, methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg, magrittr, utils, S4Vectors, DropletUtils, jsonlite, png Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: caa1ea076f80c49871752307047730fb NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen [aut, cre] Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: devel git_last_commit: b316159 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Spaniel_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Spaniel_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Spaniel_1.21.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html vignetteTitles: Spaniel 10X Visium hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R dependencyCount: 215 Package: SpaNorm Version: 1.1.0 Depends: R (>= 4.4) Imports: edgeR, ggplot2, Matrix, matrixStats, methods, rlang, scran, SeuratObject, SingleCellExperiment, SpatialExperiment, stats, SummarizedExperiment, S4Vectors, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, prettydoc, pkgdown, covr, BiocStyle, Seurat, patchwork, ggforce, ggnewscale License: GPL (>= 3) MD5sum: 2e7f2b25be5f4d78bca359f666ff04c7 NeedsCompilation: no Title: Spatially-aware normalisation for spatial transcriptomics data Description: This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data. biocViews: Software, GeneExpression, Transcriptomics, Spatial, CellBiology Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Agus Salim [aut] (ORCID: ), Ahmed Mohamed [aut] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://bhuvad.github.io/SpaNorm VignetteBuilder: knitr BugReports: https://github.com/bhuvad/SpaNorm/issues git_url: https://git.bioconductor.org/packages/SpaNorm git_branch: devel git_last_commit: 6bf7b58 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpaNorm_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpaNorm_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpaNorm_1.1.0.tgz vignettes: vignettes/SpaNorm/inst/doc/SpaNorm.html vignetteTitles: SpaNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpaNorm/inst/doc/SpaNorm.R dependencyCount: 127 Package: sparrow Version: 1.13.4 Depends: R (>= 4.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, magrittr, matrixStats, msigdbr (>= 7.4.1), KernSmooth, knitr, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE Archs: x64 MD5sum: 11d635582f8b019e95a9d1b5bb3cd6ae NeedsCompilation: no Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (ORCID: ), Arkadiusz Gladki [ctb], Aratus Informatics, LLC [fnd] (2023+), Denali Therapeutics [fnd] (2018-2022), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues git_url: https://git.bioconductor.org/packages/sparrow git_branch: devel git_last_commit: 960335c git_last_commit_date: 2024-12-03 Date/Publication: 2024-12-03 source.ver: src/contrib/sparrow_1.13.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparrow_1.13.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparrow_1.13.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparrow_1.13.4.tgz vignettes: vignettes/sparrow/inst/doc/sparrow.html vignetteTitles: Performing gene set enrichment analyses with sparrow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparrow/inst/doc/sparrow.R suggestsMe: gCrisprTools dependencyCount: 146 Package: SparseArray Version: 1.7.2 Depends: R (>= 4.3.0), methods, Matrix, BiocGenerics (>= 0.43.1), MatrixGenerics (>= 1.11.1), S4Vectors (>= 0.43.2), S4Arrays (>= 1.5.11) Imports: utils, stats, matrixStats, IRanges, XVector LinkingTo: S4Vectors, IRanges, XVector Suggests: HDF5Array, ExperimentHub, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 4a7567a6c94090693a9140f98f49f3a3 NeedsCompilation: yes Title: High-performance sparse data representation and manipulation in R Description: The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] (ORCID: ), Vince Carey [fnd] (ORCID: ), Rafael A. Irizarry [fnd] (ORCID: ), Jacques Serizay [ctb] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SparseArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SparseArray/issues git_url: https://git.bioconductor.org/packages/SparseArray git_branch: devel git_last_commit: 49e375d git_last_commit_date: 2024-11-14 Date/Publication: 2024-11-14 source.ver: src/contrib/SparseArray_1.7.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/SparseArray_1.7.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SparseArray_1.7.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SparseArray_1.7.2.tgz vignettes: vignettes/SparseArray/inst/doc/SparseArray_objects.html vignetteTitles: SparseArray objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SparseArray/inst/doc/SparseArray_objects.R dependsOnMe: DelayedArray, DelayedRandomArray, HDF5Array, TileDBArray importsMe: alabaster.matrix, batchelor, beachmat, DelayedMatrixStats, DelayedTensor, dreamlet, DropletUtils, glmGamPoi, SCArray, scater, scuttle, TSCAN, scRNAseq, IDLFM suggestsMe: BiocGenerics, MatrixGenerics, S4Arrays, SummarizedExperiment dependencyCount: 21 Package: sparseMatrixStats Version: 1.19.0 Depends: MatrixGenerics (>= 1.5.3) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: cf632a40ed06cbe93386a44d4cd2d6ca NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/sparseMatrixStats SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: devel git_last_commit: 4a46ab4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sparseMatrixStats_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparseMatrixStats_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparseMatrixStats_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparseMatrixStats_1.19.0.tgz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: atena, ccImpute, concordexR, DelayedMatrixStats, dreamlet, GSVA, scone, smartid, smoothclust, SplineDV, SPOTlight, adjclust, CRMetrics, GrabSVG, mombf, scBSP suggestsMe: APL, MatrixGenerics, miloR, scPCA, scrapper, scuttle, SpatialFeatureExperiment, StabMap, zinbwave, singleCellHaystack dependencyCount: 11 Package: sparsenetgls Version: 1.25.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: 255ee95a1d0ccfd3b6ca9968f9465803 NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: devel git_last_commit: 76afb55 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sparsenetgls_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sparsenetgls_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sparsenetgls_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sparsenetgls_1.25.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 28 Package: SparseSignatures Version: 2.17.0 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2, RhpcBLASctl Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE Archs: x64 MD5sum: efab10084a7684ebf4bc98feb52371d7 NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [cre, aut] (ORCID: ), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: devel git_last_commit: f2c94d3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SparseSignatures_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SparseSignatures_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SparseSignatures_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SparseSignatures_2.17.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/v1_introduction.html, vignettes/SparseSignatures/inst/doc/v2_using_the_package.html vignetteTitles: v1_introduction.html, v2_using_the_package.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/v2_using_the_package.R dependencyCount: 106 Package: spaSim Version: 1.9.0 Depends: R (>= 4.2.0) Imports: ggplot2, methods, stats, dplyr, spatstat.geom, spatstat.random, SpatialExperiment, SummarizedExperiment, RANN Suggests: RefManageR, BiocStyle, knitr, testthat (>= 3.0.0), sessioninfo, rmarkdown, markdown License: Artistic-2.0 MD5sum: d5a55fb013a35697d9205e21baf716cf NeedsCompilation: no Title: Spatial point data simulator for tissue images Description: A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels. biocViews: StatisticalMethod, Spatial, BiomedicalInformatics Author: Yuzhou Feng [aut, cre] (ORCID: ), Anna Trigos [aut] (ORCID: ) Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/spaSim/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/spaSim git_url: https://git.bioconductor.org/packages/spaSim git_branch: devel git_last_commit: 27c44e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spaSim_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spaSim_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spaSim_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spaSim_1.9.0.tgz vignettes: vignettes/spaSim/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spaSim/inst/doc/vignette.R dependencyCount: 95 Package: SpatialCPie Version: 1.23.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 36320e0455530a9b02cd5fa40e77dce4 NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: devel git_last_commit: 8f8ad85 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpatialCPie_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialCPie_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialCPie_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialCPie_1.23.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 122 Package: spatialDE Version: 1.13.0 Depends: R (>= 4.3) Imports: reticulate, basilisk (>= 1.9.10), checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 22aeb212f74362552a65b4967bc100b5 NeedsCompilation: no Title: R wrapper for SpatialDE Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk. biocViews: Software, Transcriptomics Author: Davide Corso [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Gabriele Sales [cre] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/spatialDE, https://bioconductor.org/packages/spatialDE/ VignetteBuilder: knitr BugReports: https://github.com/sales-lab/spatialDE/issues git_url: https://git.bioconductor.org/packages/spatialDE git_branch: devel git_last_commit: 03b4e0e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spatialDE_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatialDE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialDE_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialDE_1.13.0.tgz vignettes: vignettes/spatialDE/inst/doc/spatialDE.html vignetteTitles: Introduction to spatialDE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialDE/inst/doc/spatialDE.R dependencyCount: 101 Package: SpatialDecon Version: 1.17.0 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix, logNormReg (>= 0.4) Suggests: testthat, knitr, rmarkdown, qpdf, Seurat License: MIT + file LICENSE MD5sum: ed17ef6dc24428671de58bb56b7facad NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics, Spatial Author: Maddy Griswold [cre, aut], Patrick Danaher [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: devel git_last_commit: 7d7d839 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpatialDecon_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialDecon_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialDecon_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialDecon_1.17.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a small GeoMx dataet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R suggestsMe: GeomxTools dependencyCount: 135 Package: SpatialExperiment Version: 1.17.0 Depends: methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix, DropletUtils License: GPL-3 MD5sum: 0edfabf3d5dd25433d329b50efc8a82f NeedsCompilation: no Title: S4 Class for Spatially Resolved -omics Data Description: Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. biocViews: DataRepresentation, DataImport, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L. Crowell [aut], Lukas M. Weber [aut], Nicholas J. Eagles [ctb] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpatialExperiment VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: devel git_last_commit: 29a659f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpatialExperiment_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialExperiment_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialExperiment_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialExperiment_1.17.0.tgz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Introduction to the SpatialExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: alabaster.spatial, ExperimentSubset, imcRtools, SPIAT, tidySpatialExperiment, visiumStitched, imcdatasets, MerfishData, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: Banksy, CatsCradle, concordexR, CTSV, cytomapper, DESpace, escheR, FLAMES, ggspavis, GSVA, hoodscanR, lisaClust, MoleculeExperiment, nnSVG, scider, smoothclust, SpaNorm, spaSim, spatialDE, SpatialFeatureExperiment, spatialSimGP, spicyR, spoon, SpotClean, SpotSweeper, standR, Statial, stJoincount, tpSVG, VisiumIO, Voyager, xenLite, HCATonsilData, SingleCellMultiModal, SubcellularSpatialData, TENxXeniumData, SpatialDDLS suggestsMe: GeomxTools, ggsc, SPOTlight dependencyCount: 72 Package: SpatialFeatureExperiment Version: 1.9.6 Depends: R (>= 4.2.0) Imports: Biobase, BiocGenerics, BiocNeighbors, BiocParallel, data.table, DropletUtils, EBImage, grDevices, lifecycle, Matrix, methods, rjson, rlang, S4Vectors, sf, sfheaders, SingleCellExperiment, SpatialExperiment, spatialreg, spdep (>= 1.1-7), SummarizedExperiment, stats, terra, utils, zeallot Suggests: arrow, BiocStyle, dplyr, knitr, RBioFormats, rhdf5, rmarkdown, scater, sfarrow, SFEData (>= 1.5.3), Seurat, SeuratObject, sparseMatrixStats, testthat (>= 3.0.0), tidyr, Voyager (>= 1.7.2), withr, xml2 License: Artistic-2.0 MD5sum: dbd13df1cecebc621ca1ecebde53e98b NeedsCompilation: no Title: Integrating SpatialExperiment with Simple Features in sf Description: A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used. biocViews: DataRepresentation, Transcriptomics, Spatial Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/SpatialFeatureExperiment VignetteBuilder: knitr BugReports: https://github.com/pachterlab/SpatialFeatureExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialFeatureExperiment git_branch: devel git_last_commit: 0803d83 git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/SpatialFeatureExperiment_1.9.6.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialFeatureExperiment_1.9.6.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialFeatureExperiment_1.9.6.tgz vignettes: vignettes/SpatialFeatureExperiment/inst/doc/SFE.html vignetteTitles: Introduction to the SpatialFeatureExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialFeatureExperiment/inst/doc/SFE.R dependsOnMe: Voyager importsMe: TENxXeniumData suggestsMe: concordexR, xenLite, SFEData dependencyCount: 157 Package: spatialHeatmap Version: 2.13.1 Depends: R (>= 3.5.0) Imports: data.table, dplyr, edgeR, genefilter, ggplot2, grImport, grid, gridExtra, igraph, methods, Matrix, rsvg, shiny, grDevices, graphics, ggplotify, parallel, reshape2, stats, SummarizedExperiment, SingleCellExperiment, shinydashboard, S4Vectors, spsComps (>= 0.3.3.0), tibble, utils, xml2 Suggests: AnnotationDbi, av, BiocParallel, BiocFileCache, BiocGenerics, BiocStyle, BiocSingular, Biobase, cachem, DESeq2, dendextend, DT, dynamicTreeCut, flashClust, gplots, ggdendro, HDF5Array, htmltools, htmlwidgets, kableExtra, knitr, limma, magick, memoise, ExpressionAtlas, GEOquery, org.Hs.eg.db, org.Mm.eg.db, org.At.tair.db, org.Dr.eg.db, org.Dm.eg.db, pROC, plotly, rmarkdown, rols, rappdirs, RUnit, Rtsne, rhdf5, scater, scuttle, scran, shinyWidgets, shinyjs, shinyBS, sortable, Seurat, sparkline, spsUtil, uwot, UpSetR, visNetwork, WGCNA, xlsx, yaml License: Artistic-2.0 MD5sum: 92a54977edcdeffe903a60e7c2e13421 NeedsCompilation: no Title: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions Description: The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org. biocViews: Spatial, Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes [aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://spatialheatmap.org, https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues git_url: https://git.bioconductor.org/packages/spatialHeatmap git_branch: devel git_last_commit: 0485a13 git_last_commit_date: 2024-11-24 Date/Publication: 2024-11-24 source.ver: src/contrib/spatialHeatmap_2.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatialHeatmap_2.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialHeatmap_2.13.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialHeatmap_2.13.1.tgz vignettes: vignettes/spatialHeatmap/inst/doc/covisualize.html, vignettes/spatialHeatmap/inst/doc/custom_SVGs.html, vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html vignetteTitles: (B) Co-visualizing Bulk and Single-cell Assay Data, (C) Creating Custom Annotated SVGs, (A) Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spatialHeatmap/inst/doc/covisualize.R, vignettes/spatialHeatmap/inst/doc/custom_SVGs.R, vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R dependencyCount: 124 Package: SpatialOmicsOverlay Version: 1.7.0 Depends: R (>= 4.1.0) Imports: S4Vectors, Biobase, base64enc, EBImage, ggplot2, XML, scattermore, dplyr, pbapply, data.table, readxl, magick, grDevices, stringr, plotrix, GeomxTools, BiocFileCache, stats, utils, methods, ggtext, tools, RBioFormats Suggests: knitr, rmarkdown, testthat (>= 3.0.0), stringi, qpdf, pheatmap, viridis, cowplot, vdiffr, sf License: MIT MD5sum: 8136667fe9a92778c0f5c87430610c80 NeedsCompilation: no Title: Spatial Overlay for Omic Data from Nanostring GeoMx Data Description: Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, ProprietaryPlatforms, RNASeq, Spatial, DataRepresentation, Visualization Author: Maddy Griswold [cre, aut], Megan Vandenberg [ctb], Stephanie Zimmerman [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialOmicsOverlay git_branch: devel git_last_commit: 7476c4f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpatialOmicsOverlay_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpatialOmicsOverlay_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpatialOmicsOverlay_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpatialOmicsOverlay_1.7.0.tgz vignettes: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.html vignetteTitles: Introduction to SpatialOmicsOverlay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.R dependencyCount: 159 Package: spatialSimGP Version: 1.1.0 Depends: R (>= 4.4) Imports: SpatialExperiment, MASS, SummarizedExperiment Suggests: testthat (>= 3.0.0), STexampleData, ggplot2, knitr License: MIT + file LICENSE MD5sum: 86b2b027d9461f792ee9bc85613e08de NeedsCompilation: no Title: Simulate Spatial Transcriptomics Data with the Mean-variance Relationship Description: This packages simulates spatial transcriptomics data with the mean- variance relationship using a Gaussian Process model per gene. biocViews: Spatial, Transcriptomics, GeneExpression Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spatialSimGP VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spatialSimGP/issues git_url: https://git.bioconductor.org/packages/spatialSimGP git_branch: devel git_last_commit: ea76aca git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spatialSimGP_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatialSimGP_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spatialSimGP_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spatialSimGP_1.1.0.tgz vignettes: vignettes/spatialSimGP/inst/doc/spatialSimGP.html vignetteTitles: spatialSimGP Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialSimGP/inst/doc/spatialSimGP.R dependencyCount: 74 Package: spatzie Version: 1.13.0 Depends: R (>= 4.3) Imports: BiocGenerics, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicInteractions, GenomicRanges, ggplot2, IRanges, MatrixGenerics, matrixStats, motifmatchr, S4Vectors, stats, SummarizedExperiment, TFBSTools, utils Suggests: BiocManager, Biostrings, knitr, pheatmap, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 MD5sum: 1ece21fd1a4f1359369a885fb7f0a76d NeedsCompilation: no Title: Identification of enriched motif pairs from chromatin interaction data Description: Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC, Transcription Author: Jennifer Hammelman [aut, cre, cph] (ORCID: ), Konstantin Krismer [aut] (ORCID: ), David Gifford [ths, cph] (ORCID: ) Maintainer: Jennifer Hammelman URL: https://spatzie.mit.edu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spatzie git_branch: devel git_last_commit: c97feec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spatzie_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spatzie_1.13.0.zip vignettes: vignettes/spatzie/inst/doc/individual-steps.html, vignettes/spatzie/inst/doc/single-call.html vignetteTitles: YY1 ChIA-PET motif analysis (step-by-step), YY1 ChIA-PET motif analysis (single call) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 184 Package: speckle Version: 1.7.0 Depends: R (>= 4.2.0) Imports: limma, edgeR, SingleCellExperiment, Seurat, ggplot2, methods, stats, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, statmod, CellBench, scater, patchwork, jsonlite, vdiffr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 0655cbab431cb42f5f17dce95b818038 NeedsCompilation: no Title: Statistical methods for analysing single cell RNA-seq data Description: The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions. biocViews: SingleCell, RNASeq, Regression, GeneExpression Author: Belinda Phipson [aut, cre] Maintainer: Belinda Phipson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/speckle git_branch: devel git_last_commit: 204269e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/speckle_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/speckle_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/speckle_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/speckle_1.7.0.tgz vignettes: vignettes/speckle/inst/doc/speckle.html vignetteTitles: speckle: statistical methods for analysing single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/speckle/inst/doc/speckle.R dependencyCount: 176 Package: specL Version: 1.41.0 Depends: R (>= 4.1), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.7), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 Archs: x64 MD5sum: db5737c6df505bebd0523c9c21cae373 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (ORCID: ), Jonas Grossmann [aut] (ORCID: ), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: devel git_last_commit: bb4f519 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/specL_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/specL_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/specL_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/specL_1.41.0.tgz vignettes: vignettes/specL/inst/doc/report.html, vignettes/specL/inst/doc/specL.html vignetteTitles: Automatic specL Workflow, Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 33 Package: SpeCond Version: 1.61.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: c43f302920b9108aff478d626707dcdf NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: devel git_last_commit: 84f04f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpeCond_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpeCond_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpeCond_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpeCond_1.61.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 18 Package: Spectra Version: 1.17.4 Depends: R (>= 4.0.0), S4Vectors, BiocParallel Imports: ProtGenerics (>= 1.39.1), methods, IRanges, MsCoreUtils (>= 1.7.5), graphics, grDevices, stats, tools, utils, fs, BiocGenerics, MetaboCoreUtils Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr (>= 1.0.0), msentropy, patrick License: Artistic-2.0 MD5sum: e7e71af15c46414fa4f68314ac747819 NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Jan Stanstrup [ctb] (ORCID: ), Nir Shahaf [ctb], Mar Garcia-Aloy [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: devel git_last_commit: 6bcbc94 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/Spectra_1.17.4.tar.gz win.binary.ver: bin/windows/contrib/4.5/Spectra_1.17.4.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Spectra_1.17.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Spectra_1.17.4.tgz vignettes: vignettes/Spectra/inst/doc/MsBackend.html, vignettes/Spectra/inst/doc/Spectra-large-scale.html, vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Creating new `MsBackend` class, Large-scale data handling and processing with Spectra, Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/MsBackend.R, vignettes/Spectra/inst/doc/Spectra-large-scale.R, vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: hdxmsqc, MetCirc, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql importsMe: CompoundDb, MetaboAnnotation, MsExperiment, MsQuality, SpectraQL, xcms suggestsMe: koinar, MetNet, MsDataHub, MSnbase, PSMatch, RaMS dependencyCount: 29 Package: SpectralTAD Version: 1.23.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges, utils Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: 40445c71c62bea40a71269bf8958f96e NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: devel git_last_commit: 1f05a5d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpectralTAD_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpectralTAD_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpectralTAD_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpectralTAD_1.23.0.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 88 Package: SpectraQL Version: 1.1.0 Depends: R (>= 4.4.0), ProtGenerics (>= 1.25.1) Imports: Spectra (>= 1.5.6), MsCoreUtils, methods Suggests: testthat, msdata (>= 0.19.3), roxygen2, rmarkdown, knitr, S4Vectors, BiocStyle, mzR License: Artistic-2.0 MD5sum: 9995c2fa51263a6be0569ce90cc4ef1d NeedsCompilation: no Title: MassQL support for Spectra Description: The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut], Sebastian Gibb [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/SpectraQL VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/SpectraQL/issues git_url: https://git.bioconductor.org/packages/SpectraQL git_branch: devel git_last_commit: 85b34ee git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpectraQL_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpectraQL_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpectraQL_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpectraQL_1.1.0.tgz vignettes: vignettes/SpectraQL/inst/doc/SpectraQL.html vignetteTitles: Mass Spec Query Language Support to the Spectra Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpectraQL/inst/doc/SpectraQL.R dependencyCount: 30 Package: SPEM Version: 1.47.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 74484d4559ef0e5875bb55f569c2ee69 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: devel git_last_commit: d1af4f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SPEM_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPEM_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPEM_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPEM_1.47.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 10 Package: SPIA Version: 2.59.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 36721cc46862a634b9651dc5f5b2fda5 NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: devel git_last_commit: 7432fd5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SPIA_2.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPIA_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPIA_2.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPIA_2.59.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 15 Package: SPIAT Version: 1.9.0 Depends: R (>= 4.2.0), SpatialExperiment (>= 1.8.0) Imports: apcluster (>= 1.4.7), ggplot2 (>= 3.2.1), gridExtra (>= 2.3), gtools (>= 3.8.1), reshape2 (>= 1.4.3), dplyr (>= 0.8.3), RANN (>= 2.6.1), pracma (>= 2.2.5), dbscan (>= 1.1-5), mmand (>= 1.5.4), tibble (>= 2.1.3), grDevices, stats, utils, vroom, dittoSeq, spatstat.geom, methods, spatstat.explore, raster, sp, SummarizedExperiment, rlang Suggests: BiocStyle, plotly (>= 4.9.0), knitr, rmarkdown, pkgdown, testthat, graphics, alphahull, Rtsne, umap, ComplexHeatmap, elsa License: Artistic-2.0 + file LICENSE MD5sum: e4b9ef873d95087098d97b962f780466 NeedsCompilation: no Title: Spatial Image Analysis of Tissues Description: SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis. biocViews: BiomedicalInformatics, CellBiology, Spatial, Clustering, DataImport, ImmunoOncology, QualityControl, SingleCell, Software, Visualization Author: Anna Trigos [aut] (ORCID: ), Yuzhou Feng [aut, cre] (ORCID: ), Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut] Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/SPIAT/ VignetteBuilder: knitr BugReports: https://github.com/trigosteam/SPIAT/issues git_url: https://git.bioconductor.org/packages/SPIAT git_branch: devel git_last_commit: da43adc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SPIAT_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPIAT_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPIAT_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPIAT_1.9.0.tgz vignettes: vignettes/SPIAT/inst/doc/basic_analysis.html, vignettes/SPIAT/inst/doc/cell-colocalisation.html, vignettes/SPIAT/inst/doc/data_reading-formatting.html, vignettes/SPIAT/inst/doc/neighborhood.html, vignettes/SPIAT/inst/doc/quality-control_visualisation.html, vignettes/SPIAT/inst/doc/spatial-heterogeneity.html, vignettes/SPIAT/inst/doc/SPIAT.html, vignettes/SPIAT/inst/doc/tissue-structure.html vignetteTitles: Basic analyses with SPIAT, Quantifying cell colocalisation with SPIAT, Reading in data and data formatting in SPIAT, Identifying cellular neighborhood with SPIAT, Quality control and visualisation with SPIAT, Spatial heterogeneity with SPIAT, Overview of the SPIAT package, Characterising tissue structure with SPIAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIAT/inst/doc/basic_analysis.R, vignettes/SPIAT/inst/doc/cell-colocalisation.R, vignettes/SPIAT/inst/doc/data_reading-formatting.R, vignettes/SPIAT/inst/doc/neighborhood.R, vignettes/SPIAT/inst/doc/quality-control_visualisation.R, vignettes/SPIAT/inst/doc/spatial-heterogeneity.R, vignettes/SPIAT/inst/doc/SPIAT.R, vignettes/SPIAT/inst/doc/tissue-structure.R dependencyCount: 120 Package: spicyR Version: 1.19.3 Depends: R (>= 4.1) Imports: ggplot2, concaveman, BiocParallel, spatstat.explore, spatstat.geom, lmerTest, S4Vectors, methods, pheatmap, rlang, grDevices, stats, data.table, dplyr, tidyr, scam, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, ggforce, ClassifyR, tibble, magrittr, cli, survival, ggthemes, ggh4x, coxme, ggnewscale, lifecycle, simpleSeg Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, pkgdown, imcRtools, testthat (>= 3.0.0) License: GPL (>=2) Archs: x64 MD5sum: 920abff009c524959b899a18458c87df NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable. biocViews: SingleCell, CellBasedAssays, Spatial Author: Nicolas Canete [aut], Ellis Patrick [aut, cre], Nicholas Robertson [ctb], Alex Qin [ctb], Farhan Ameen [ctb], Shreya Rao [ctb] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/spicyR/ https://github.com/SydneyBioX/spicyR, https://sydneybiox.github.io/spicyR/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: devel git_last_commit: 1b87dce git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/spicyR_1.19.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/spicyR_1.19.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spicyR_1.19.3.tgz vignettes: vignettes/spicyR/inst/doc/spicyR.html vignetteTitles: "Spatial Linear and Mixed-Effects Modelling with spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/spicyR.R importsMe: lisaClust suggestsMe: Statial, spicyWorkflow dependencyCount: 222 Package: spikeLI Version: 2.67.0 Imports: graphics, grDevices, stats, utils License: GPL-2 Archs: x64 MD5sum: 4bc6d193f1f4eef50dea6dc6864187c1 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: devel git_last_commit: 443a12f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spikeLI_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spikeLI_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spikeLI_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spikeLI_2.67.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.13.0 Depends: Rsamtools, GenomicRanges, R (>= 3.6.0) Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings, GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome, S4Vectors, graphics, ggplot2, utils Suggests: covr, testthat, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: 26eae640a73b29c3f401ecc03c1e995e NeedsCompilation: no Title: Spike-in calibration for cell-free MeDIP Description: spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms. biocViews: DifferentialMethylation, DNAMethylation, Normalization, Preprocessing, QualityControl, Sequencing Author: Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut, cre] Maintainer: Tim Triche URL: https://github.com/trichelab/spiky VignetteBuilder: knitr BugReports: https://github.com/trichelab/spiky/issues git_url: https://git.bioconductor.org/packages/spiky git_branch: devel git_last_commit: 1c71f59 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spiky_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spiky_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spiky_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spiky_1.13.0.tgz vignettes: vignettes/spiky/inst/doc/spiky_vignette.html vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in controls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R dependencyCount: 95 Package: spillR Version: 1.3.0 Depends: R (>= 4.3.0), SummarizedExperiment, CATALYST Imports: dplyr, tibble, tidyselect, stats, ggplot2, tidyr, spatstat.univar, S4Vectors, parallel Suggests: knitr, rmarkdown, cowplot, testthat (>= 3.0.0), BiocStyle, hexbin License: LGPL-3 MD5sum: 4a72ce740460d8087aa7370037944c6b NeedsCompilation: no Title: Spillover Compensation in Mass Cytometry Data Description: Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model. biocViews: FlowCytometry, ImmunoOncology, MassSpectrometry, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization, Regression Author: Marco Guazzini [aut, cre] (ORCID: ), Alexander G. Reisach [aut] (ORCID: ), Sebastian Weichwald [aut] (ORCID: ), Christof Seiler [aut] (ORCID: ) Maintainer: Marco Guazzini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spillR git_branch: devel git_last_commit: 7a34e97 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spillR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spillR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spillR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spillR_1.3.0.tgz vignettes: vignettes/spillR/inst/doc/spillR-vignette.html vignetteTitles: spillR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spillR/inst/doc/spillR-vignette.R dependencyCount: 182 Package: spkTools Version: 1.63.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 6432311dcd63686234b762091ede4ebd NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: devel git_last_commit: c8630a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spkTools_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spkTools_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spkTools_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spkTools_1.63.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 10 Package: splatter Version: 1.31.0 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), crayon, edgeR, fitdistrplus, grDevices, locfit, matrixStats, methods, rlang, S4Vectors, scuttle, stats, SummarizedExperiment, utils, withr Suggests: BASiCS (>= 1.7.10), BiocManager, BiocSingular, BiocStyle, Biostrings, covr, cowplot, GenomeInfoDb, GenomicRanges, ggplot2 (>= 3.4.0), igraph, IRanges, knitr, limSolve, lme4, magick, mfa, phenopath, preprocessCore, progress, pscl, rmarkdown, scales, scater (>= 1.15.16), scDD, scran, SparseDC, spelling, testthat, VariantAnnotation, zinbwave License: GPL-3 + file LICENSE MD5sum: ff50d593ad6c5a09ee541218221add79 NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (ORCID: , GitHub: lazappi), Belinda Phipson [aut] (ORCID: , GitHub: bphipson), Christina Azodi [ctb] (ORCID: , GitHub: azodichr), Alicia Oshlack [aut] (ORCID: ) Maintainer: Luke Zappia URL: https://bioconductor.org/packages/splatter/, https://github.com/Oshlack/splatter, http://oshlacklab.com/splatter/ VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: devel git_last_commit: 77f3611 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/splatter_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splatter_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splatter_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splatter_1.31.0.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatPop.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, splatPop simulation, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatPop.R, vignettes/splatter/inst/doc/splatter.R importsMe: SCRIP suggestsMe: ccImpute, mastR, NewWave, scone, scPCA, smartid, scellpam dependencyCount: 64 Package: SpliceWiz Version: 1.9.0 Depends: R (>= 3.5.0), NxtIRFdata Imports: ompBAM, methods, stats, utils, tools, parallel, scales, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, RSQLite, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, htmltools, IRanges, patchwork, pheatmap, progress, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors, shiny, shinyFiles, shinyWidgets, shinydashboard, stringi, rhandsontable, DT, grDevices, heatmaply, matrixStats, RColorBrewer, rvest, httr LinkingTo: ompBAM, Rcpp, RcppProgress Suggests: knitr, rmarkdown, crayon, splines, testthat (>= 3.0.0), DESeq2, limma, DoubleExpSeq, edgeR, DBI, GO.db, AnnotationDbi, fgsea, Rsubread License: MIT + file LICENSE MD5sum: 733227bf14f72740e4d82963388f7d67 NeedsCompilation: yes Title: interactive analysis and visualization of alternative splicing in R Description: The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing, DifferentialExpression, GUI, Sequencing Author: Alex Chit Hei Wong [aut, cre, cph], Ulf Schmitz [ctb], William Ritchie [cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/SpliceWiz SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SpliceWiz git_branch: devel git_last_commit: 8784449 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpliceWiz_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpliceWiz_1.9.0.zip vignettes: vignettes/SpliceWiz/inst/doc/SW_Cookbook.html, vignettes/SpliceWiz/inst/doc/SW_QuickStart.html vignetteTitles: SpliceWiz: the cookbook, SpliceWiz: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpliceWiz/inst/doc/SW_Cookbook.R, vignettes/SpliceWiz/inst/doc/SW_QuickStart.R dependencyCount: 193 Package: SplicingFactory Version: 1.15.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: f09aa2b929faebe5de2e7897567e48c3 NeedsCompilation: no Title: Splicing Diversity Analysis for Transcriptome Data Description: The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions. biocViews: Transcriptomics, RNASeq, DifferentialSplicing, AlternativeSplicing, TranscriptomeVariant Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut, cre] (ORCID: ) Maintainer: Endre Sebestyen URL: https://github.com/esebesty/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/esebesty/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: devel git_last_commit: ac49427 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SplicingFactory_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplicingFactory_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplicingFactory_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplicingFactory_1.15.0.tgz vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html vignetteTitles: SplicingFactory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R dependencyCount: 37 Package: SplicingGraphs Version: 1.47.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb, GenomicRanges (>= 1.23.21), Rsamtools, graph Suggests: igraph, Gviz, txdbmaker, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 7993f0431a742a208da65d9f8e135810 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: devel git_last_commit: 2e358aa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SplicingGraphs_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplicingGraphs_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplicingGraphs_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplicingGraphs_1.47.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 82 Package: SplineDV Version: 0.99.11 Depends: R (>= 3.5.0) Imports: plotly, dplyr, scuttle, methods, Biobase, BiocGenerics, S4Vectors, sparseMatrixStats, SingleCellExperiment, SummarizedExperiment, Matrix (>= 1.6.4), utils Suggests: knitr, DelayedMatrixStats, rmarkdown, BiocStyle, ggplot2, ggpubr, MASS, scales, scRNAseq, testthat (>= 3.0.0) License: GPL-2 MD5sum: 15fec493df162e89d6d9a12bd57f74ff NeedsCompilation: no Title: Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions) Description: A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes. biocViews: Software, SingleCell, Sequencing, DifferentialExpression, RNASeq, GeneExpression, Transcriptomics, FeatureExtraction Author: Shreyan Gupta [aut, cre] (ORCID: ), James Cai [aut] (ORCID: ) Maintainer: Shreyan Gupta URL: https://github.com/Xenon8778/SplineDV VignetteBuilder: knitr BugReports: https://github.com/Xenon8778/SplineDV/issues git_url: https://git.bioconductor.org/packages/SplineDV git_branch: devel git_last_commit: ef9afbe git_last_commit_date: 2024-11-19 Date/Publication: 2024-11-20 source.ver: src/contrib/SplineDV_0.99.11.tar.gz win.binary.ver: bin/windows/contrib/4.5/SplineDV_0.99.11.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SplineDV_0.99.11.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SplineDV_0.99.11.tgz vignettes: vignettes/SplineDV/inst/doc/SplineDV.html vignetteTitles: Introduction to Spline-DV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplineDV/inst/doc/SplineDV.R dependencyCount: 111 Package: splineTimeR Version: 1.35.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: 0797269530ae8d64453b3716a5c67792 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: devel git_last_commit: 3853c2f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/splineTimeR_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splineTimeR_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splineTimeR_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splineTimeR_1.35.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 65 Package: splots Version: 1.73.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI, dplyr, ggplot2 License: LGPL MD5sum: a0f3e1c40661fc9ad85398615a316024 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is here to support legacy usages of it, but it should not be used for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or ggplot2 (geom_raster, facet_wrap) as exemplified in the vignette of this package. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: devel git_last_commit: da028a9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/splots_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/splots_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/splots_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/splots_1.73.0.tgz vignettes: vignettes/splots/inst/doc/splots.html vignetteTitles: splots: visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splots.R dependsOnMe: HD2013SGI dependencyCount: 2 Package: SPONGE Version: 1.29.0 Depends: R (>= 3.6) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse, caret, dplyr, biomaRt, randomForest, ggridges, cvms, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory, GSVA License: GPL (>=3) MD5sum: 322ce39486340b36cfd4ad0de1f3ef05 NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression, RandomForest, MachineLearning Author: Markus List [aut, cre] (ORCID: ), Markus Hoffmann [aut] (ORCID: ), Lena Strasser [aut] (ORCID: ) Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: devel git_last_commit: 9357494 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SPONGE_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPONGE_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPONGE_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPONGE_1.29.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html, vignettes/SPONGE/inst/doc/spongEffects.html vignetteTitles: SPONGE vignette, spongEffects vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R, vignettes/SPONGE/inst/doc/spongEffects.R importsMe: miRspongeR suggestsMe: mirTarRnaSeq dependencyCount: 226 Package: spoon Version: 1.3.1 Depends: R (>= 4.4) Imports: SpatialExperiment, BRISC, nnSVG, BiocParallel, Matrix, methods, SummarizedExperiment, stats, utils, scuttle Suggests: testthat, STexampleData, knitr License: MIT + file LICENSE MD5sum: 884997a2c0fef09186a120eb7d2b04dd NeedsCompilation: no Title: Address the Mean-variance Relationship in Spatial Transcriptomics Data Description: This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spoon VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spoon/issues git_url: https://git.bioconductor.org/packages/spoon git_branch: devel git_last_commit: 0987de7 git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-01 source.ver: src/contrib/spoon_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/spoon_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spoon_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spoon_1.3.1.tgz vignettes: vignettes/spoon/inst/doc/spoon.html vignetteTitles: spoon Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spoon/inst/doc/spoon.R dependencyCount: 91 Package: SpotClean Version: 1.9.0 Depends: R (>= 4.2.0), Imports: stats, methods, utils, dplyr, S4Vectors, SummarizedExperiment, SpatialExperiment, Matrix, rhdf5, ggplot2, grid, readbitmap, rjson, tibble, viridis, grDevices, RColorBrewer, Seurat, rlang Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, R.utils, spelling License: GPL-3 MD5sum: 1f9245363f0f932debb070e44b80bd1d NeedsCompilation: yes Title: SpotClean adjusts for spot swapping in spatial transcriptomics data Description: SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses. biocViews: DataImport, RNASeq, Sequencing, GeneExpression, Spatial, SingleCell, Transcriptomics, Preprocessing Author: Zijian Ni [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/SpotClean VignetteBuilder: knitr BugReports: https://github.com/zijianni/SpotClean/issues git_url: https://git.bioconductor.org/packages/SpotClean git_branch: devel git_last_commit: 0e3a77e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SpotClean_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpotClean_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpotClean_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpotClean_1.9.0.tgz vignettes: vignettes/SpotClean/inst/doc/SpotClean.html vignetteTitles: SpotClean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpotClean/inst/doc/SpotClean.R dependencyCount: 192 Package: SPOTlight Version: 1.11.0 Depends: R (>= 4.1) Imports: ggplot2, NMF, Matrix, matrixStats, nnls, SingleCellExperiment, sparseMatrixStats, stats Suggests: BiocStyle, colorBlindness, DelayedArray, DropletUtils, ExperimentHub, ggcorrplot, grDevices, grid, igraph, jpeg, knitr, methods, png, rmarkdown, scater, scatterpie, scran, SpatialExperiment, SummarizedExperiment, S4Vectors, TabulaMurisSenisData, TENxVisiumData, testthat License: GPL-3 MD5sum: 1fea9bbebe67f856ed0b9a2b18ad3096 NeedsCompilation: no Title: `SPOTlight`: Spatial Transcriptomics Deconvolution Description: `SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). biocViews: SingleCell, Spatial, StatisticalMethod Author: Marc Elosua-Bayes [aut, cre], Helena L. Crowell [aut] Maintainer: Marc Elosua-Bayes URL: https://github.com/MarcElosua/SPOTlight VignetteBuilder: knitr BugReports: https://github.com/MarcElosua/SPOTlight/issues git_url: https://git.bioconductor.org/packages/SPOTlight git_branch: devel git_last_commit: eae1ff5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SPOTlight_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SPOTlight_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPOTlight_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SPOTlight_1.11.0.tgz vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html vignetteTitles: "SPOTlight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R dependencyCount: 83 Package: SpotSweeper Version: 1.3.2 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, SingleCellExperiment, stats, escheR, MASS, ggplot2, spatialEco, grDevices, BiocParallel Suggests: knitr, BiocStyle, rmarkdown, scuttle, STexampleData, ggpubr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ca3648affab6cb651bfeb9df281d98f7 NeedsCompilation: no Title: Spatially-aware quality control for spatial transcriptomics Description: Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. biocViews: Software, Spatial, Transcriptomics, QualityControl, GeneExpression, Author: Michael Totty [aut, cre] (ORCID: ), Stephanie Hicks [aut] (ORCID: ), Boyi Guo [aut] (ORCID: ) Maintainer: Michael Totty URL: https://github.com/MicTott/SpotSweeper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/SpotSweeper git_url: https://git.bioconductor.org/packages/SpotSweeper git_branch: devel git_last_commit: 9f16259 git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-02 source.ver: src/contrib/SpotSweeper_1.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/SpotSweeper_1.3.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SpotSweeper_1.3.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpotSweeper_1.3.2.tgz vignettes: vignettes/SpotSweeper/inst/doc/getting_started.html vignetteTitles: Getting Started with `SpotSweeper` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpotSweeper/inst/doc/getting_started.R dependencyCount: 110 Package: spqn Version: 1.19.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 MD5sum: 005fc423b98190db42eb306a909ecb3a NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: devel git_last_commit: ebac315 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/spqn_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/spqn_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/spqn_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/spqn_1.19.0.tgz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 64 Package: SQLDataFrame Version: 1.21.0 Depends: DelayedArray, S4Vectors Imports: stats, utils, methods, BiocGenerics, RSQLite, duckdb, DBI Suggests: knitr, rmarkdown, BiocStyle, testthat License: LGPL (>= 3); File LICENSE MD5sum: ba914ce1dbf33b80bede3c4af05ca510 NeedsCompilation: no Title: Representation of SQL tables in DataFrame metaphor Description: Implements bindings for SQL tables that are compatible with Bioconductor S4 data structures, namely the DataFrame and DelayedArray. This allows SQL-derived data to be easily used inside other Bioconductor objects (e.g., SummarizedExperiments) while keeping everything on disk. biocViews: DataRepresentation, Infrastructure, Software Author: Qian Liu [aut, cre] (ORCID: ), Aaron Lun [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: devel git_last_commit: 909e7f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SQLDataFrame_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SQLDataFrame_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SQLDataFrame_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SQLDataFrame_1.21.0.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame_userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame_userguide.R dependencyCount: 40 Package: squallms Version: 1.1.0 Depends: R (>= 3.5.0) Imports: xcms, MSnbase, MsExperiment, RaMS, dplyr, tidyr, tibble, ggplot2, shiny, plotly, data.table, caret, stats, graphics, utils, keys Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: cd04d48df6385166f9b5ea1312bc8586 NeedsCompilation: no Title: Speedy quality assurance via lasso labeling for LC-MS data Description: squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis. biocViews: MassSpectrometry, Metabolomics, Proteomics, Lipidomics, ShinyApps, Classification, Clustering, FeatureExtraction, PrincipalComponent, Regression, Preprocessing, QualityControl, Visualization Author: William Kumler [aut, cre, cph] (ORCID: ) Maintainer: William Kumler URL: https://github.com/wkumler/squallms VignetteBuilder: knitr BugReports: https://github.com/wkumler/squallms/issues git_url: https://git.bioconductor.org/packages/squallms git_branch: devel git_last_commit: c717511 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/squallms_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/squallms_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/squallms_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/squallms_1.1.0.tgz vignettes: vignettes/squallms/inst/doc/intro_to_squallms.html vignetteTitles: Introduction to squallms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/squallms/inst/doc/intro_to_squallms.R dependencyCount: 186 Package: sRACIPE Version: 1.23.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture License: MIT + file LICENSE MD5sum: 9129e965914d183b60f2281414167a77 NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Vivek Kohar [aut, cre] (ORCID: ), Mingyang Lu [aut] Maintainer: Vivek Kohar URL: https://vivekkohar.github.io/sRACIPE/, https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: devel git_last_commit: 0e15aec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sRACIPE_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sRACIPE_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sRACIPE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sRACIPE_1.23.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 103 Package: SRAdb Version: 1.69.2 Depends: RSQLite, graph, RCurl Imports: R.utils Suggests: Rgraphviz License: Artistic-2.0 MD5sum: e867e6fd7ae81c6b519d04950e235072 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu BugReports: https://github.com/zhujack/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: devel git_last_commit: b59574d git_last_commit_date: 2024-12-10 Date/Publication: 2024-12-10 source.ver: src/contrib/SRAdb_1.69.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/SRAdb_1.69.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SRAdb_1.69.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SRAdb_1.69.2.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R suggestsMe: parathyroidSE dependencyCount: 30 Package: sscu Version: 2.37.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 4fe0d55cd906d8f493c7f388f6703789 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: devel git_last_commit: febf8e0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sscu_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sscu_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sscu_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sscu_2.37.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 37 Package: sSeq Version: 1.45.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 218fcf7e70cd8329ac7d5696875d5c12 NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: devel git_last_commit: d459b87 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sSeq_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sSeq_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sSeq_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sSeq_1.45.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq suggestsMe: NBLDA dependencyCount: 3 Package: ssize Version: 1.81.0 Depends: gdata, xtable License: LGPL MD5sum: a48a13552dee83c5724e4ff51396c091 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: devel git_last_commit: 40be585 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ssize_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssize_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssize_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssize_1.81.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: maGUI dependencyCount: 6 Package: sSNAPPY Version: 1.11.0 Depends: R (>= 4.3.0), ggplot2 Imports: dplyr (>= 1.1), magrittr, rlang, stats, graphite, tibble, ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment, edgeR, methods, ggforce, pheatmap, utils, stringr, gtools, tidyr Suggests: BiocManager, BiocStyle, colorspace, cowplot, DT, htmltools, knitr, pander, patchwork, rmarkdown, spelling, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 0bfe3c1b9b01530ee6727bd2af663e41 NeedsCompilation: no Title: Single Sample directioNAl Pathway Perturbation analYsis Description: A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels. biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling Author: Wenjun Liu [aut, cre] (ORCID: ), Stephen Pederson [aut] (ORCID: ) Maintainer: Wenjun Liu URL: https://wenjun-liu.github.io/sSNAPPY/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues git_url: https://git.bioconductor.org/packages/sSNAPPY git_branch: devel git_last_commit: 8ca48a7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sSNAPPY_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sSNAPPY_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sSNAPPY_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sSNAPPY_1.11.0.tgz vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html vignetteTitles: Single Sample Directional Pathway Perturbation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R dependencyCount: 109 Package: ssPATHS Version: 1.21.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 5332d79c3cfffd45f2d4e2aa078d9c74 NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: devel git_last_commit: bd3f81e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ssPATHS_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssPATHS_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssPATHS_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssPATHS_1.21.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 119 Package: ssrch Version: 1.23.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 6313e44aa89edb9779716d9bf319741c NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: devel git_last_commit: 2b3fcf5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ssrch_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssrch_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssrch_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssrch_1.23.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R dependencyCount: 47 Package: ssviz Version: 1.41.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 MD5sum: f4ed45f748b1d29ef34d8d7c6198f183 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: devel git_last_commit: 6c33152 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ssviz_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ssviz_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ssviz_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ssviz_1.41.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 72 Package: StabMap Version: 1.1.0 Depends: R (>= 4.4.0), Imports: igraph, slam, BiocNeighbors, Matrix, MASS, abind, SummarizedExperiment, methods, MatrixGenerics, BiocGenerics, BiocSingular, BiocParallel Suggests: scran, scater, knitr, UpSetR, gridExtra, SingleCellMultiModal, BiocStyle, magrittr, testthat (>= 3.0.0), purrr, sparseMatrixStats License: GPL-2 Archs: x64 MD5sum: 4552eadf29943312462045a15d149416 NeedsCompilation: no Title: Stabilised mosaic single cell data integration using unshared features Description: StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists. biocViews: SingleCell, DimensionReduction, Software Author: Shila Ghazanfar [aut, cre, ctb], Aiden Jin [ctb], Nicholas Robertson [ctb] Maintainer: Shila Ghazanfar URL: https://sydneybiox.github.io/StabMap, https://sydneybiox.github.io/StabMap/ VignetteBuilder: knitr BugReports: https://github.com/sydneybiox/StabMap/issues git_url: https://git.bioconductor.org/packages/StabMap git_branch: devel git_last_commit: 2005245 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/StabMap_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/StabMap_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/StabMap_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/StabMap_1.1.0.tgz vignettes: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.html vignetteTitles: Mosaic single cell data integration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.R dependencyCount: 65 Package: stageR Version: 1.29.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: 5591ee266b9a5c904b6c1eb792df025e NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: devel git_last_commit: 2a767cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/stageR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stageR_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stageR_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stageR_1.29.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg, muscat, satuRn dependencyCount: 37 Package: standR Version: 1.11.1 Depends: R (>= 4.1) Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment, SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2, tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics, grDevices, stats, methods, ggalluvial, mclustcomp, RUVSeq Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggpubr, ggrepel, cluster, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 403f0baac6620427df8de647ab6feb38 NeedsCompilation: no Title: Spatial transcriptome analyses of Nanostring's DSP data in R Description: standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations. biocViews: Spatial, Transcriptomics, GeneExpression, DifferentialExpression, QualityControl, Normalization, ExperimentHubSoftware Author: Ning Liu [aut, cre] (ORCID: ), Dharmesh D Bhuva [aut] (ORCID: ), Ahmed Mohamed [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/standR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/standR/issues git_url: https://git.bioconductor.org/packages/standR git_branch: devel git_last_commit: 0dbb86e git_last_commit_date: 2024-12-01 Date/Publication: 2024-12-02 source.ver: src/contrib/standR_1.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/standR_1.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/standR_1.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/standR_1.11.1.tgz vignettes: vignettes/standR/inst/doc/Quick_start.html vignetteTitles: standR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/standR/inst/doc/Quick_start.R dependencyCount: 151 Package: STATegRa Version: 1.43.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: 1fb375f51e40f44d5f3e28c6bc2a3796 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: devel git_last_commit: 7a5e8cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/STATegRa_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/STATegRa_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/STATegRa_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/STATegRa_1.43.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 57 Package: Statial Version: 1.9.0 Depends: R (>= 4.1.0) Imports: BiocParallel, spatstat.geom, concaveman, data.table, spatstat.explore, dplyr, tidyr, SingleCellExperiment, tibble, stringr, tidyselect, ggplot2, methods, stats, SummarizedExperiment, S4Vectors, plotly, purrr, ranger, magrittr, limma, SpatialExperiment, cluster, treekoR Suggests: BiocStyle, knitr, testthat (>= 3.0.0), ClassifyR, spicyR, ggsurvfit, lisaClust, survival License: GPL-3 MD5sum: 024fde4afe64267c6487f02d1b2acb9e NeedsCompilation: no Title: A package to identify changes in cell state relative to spatial associations Description: Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with. biocViews: SingleCell, Spatial, Classification Author: Farhan Ameen [aut, cre], Sourish Iyengar [aut], Shila Ghazanfar [aut], Ellis Patrick [aut] Maintainer: Farhan Ameen URL: https://sydneybiox.github.io/Statial https://github.com/SydneyBioX/Statial/issues VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/Statial/issues git_url: https://git.bioconductor.org/packages/Statial git_branch: devel git_last_commit: b4c031f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Statial_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Statial_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Statial_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Statial_1.9.0.tgz vignettes: vignettes/Statial/inst/doc/Statial.html vignetteTitles: "Introduction to Statial" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Statial/inst/doc/Statial.R suggestsMe: spicyWorkflow dependencyCount: 224 Package: statTarget Version: 1.37.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown License: LGPL (>= 3) MD5sum: 492cfd2bb1aab20bce4fa603e3da667d NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: devel git_last_commit: 581d4ef git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/statTarget_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/statTarget_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/statTarget_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/statTarget_1.37.0.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 26 Package: stepNorm Version: 1.79.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 8f6014ef04cd41199bb209b46b9cf7da NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: devel git_last_commit: c5798e7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/stepNorm_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stepNorm_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stepNorm_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stepNorm_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 9 Package: stJoincount Version: 1.9.0 Depends: R (>= 4.2.0) Imports: graphics, stats, dplyr, magrittr, sp, raster, spdep, ggplot2, pheatmap, grDevices, Seurat, SpatialExperiment, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 066261eadf90643a0d80ee09f8569cec NeedsCompilation: no Title: stJoincount - Join count statistic for quantifying spatial correlation between clusters Description: stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance. biocViews: Transcriptomics, Clustering, Spatial, BiocViews, Software Author: Jiarong Song [cre, aut] (ORCID: ), Rania Bassiouni [aut], David Craig [aut] Maintainer: Jiarong Song URL: https://github.com/Nina-Song/stJoincount VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stJoincount git_branch: devel git_last_commit: 0a115cb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/stJoincount_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/stJoincount_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/stJoincount_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/stJoincount_1.9.0.tgz vignettes: vignettes/stJoincount/inst/doc/stJoincount-vignette.html vignetteTitles: Introduction to stJoincount hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/stJoincount/inst/doc/stJoincount-vignette.R dependencyCount: 198 Package: strandCheckR Version: 1.25.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr, rmarkdown Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 2d8bd6ddf845cf0d91123d26ea4efcbe NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Steve Pederson [aut] Maintainer: Thu-Hien To URL: https://github.com/UofABioinformaticsHub/strandCheckR VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/strandCheckR/issues git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: devel git_last_commit: d22ff46 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/strandCheckR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/strandCheckR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/strandCheckR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/strandCheckR_1.25.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 122 Package: Streamer Version: 1.53.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: f69f5838ce73bf5a807aca786cd8e00d NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/Streamer git_branch: devel git_last_commit: 6658a05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Streamer_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Streamer_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Streamer_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Streamer_1.53.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R dependencyCount: 11 Package: STRINGdb Version: 2.19.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, httr, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 11c2b3878eb64348d550a23722f2e285 NeedsCompilation: no Title: STRINGdb - Protein-Protein Interaction Networks and Functional Enrichment Analysis Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: devel git_last_commit: 95ff4e9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/STRINGdb_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/STRINGdb_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/STRINGdb_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/STRINGdb_2.19.0.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: GeDi, IMMAN, RITAN, TDbasedUFEadv, XINA, crosstalkr suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 50 Package: struct Version: 1.19.0 Depends: R (>= 4.0) Imports: methods,ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors, rols Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: 68c8cbf1009ecdcb9d22e6ae3739e170 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: devel git_last_commit: fd866cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/struct_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/struct_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/struct_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/struct_1.19.0.tgz vignettes: vignettes/struct/inst/doc/struct_templates_and_helper_functions.html vignetteTitles: Introduction to STRUCT - STatistics in R using Class-based Templates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/struct/inst/doc/struct_templates_and_helper_functions.R dependsOnMe: MetMashR, structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 54 Package: Structstrings Version: 1.23.1 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: 2f1158be14cda8eb0cbb8bf8d5af0bc7 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: devel git_last_commit: b17276e git_last_commit_date: 2024-11-01 Date/Publication: 2024-11-03 source.ver: src/contrib/Structstrings_1.23.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Structstrings_1.23.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Structstrings_1.23.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Structstrings_1.23.1.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 34 Package: structToolbox Version: 1.19.0 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 MD5sum: 7dc714778d8d17422e19badbe2414e0d NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://github.com/computational-metabolomics/structToolbox, https://computational-metabolomics.github.io/structToolbox/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: devel git_last_commit: 4622d2f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/structToolbox_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/structToolbox_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/structToolbox_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/structToolbox_1.19.0.tgz vignettes: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html vignetteTitles: Data analysis of metabolomics and other omics datasets using the structToolbox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R suggestsMe: metabolomicsWorkbenchR, MetMashR dependencyCount: 80 Package: StructuralVariantAnnotation Version: 1.23.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, pwalign, stringr, dplyr, methods, rlang, GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment, GenomeInfoDb, Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown, tidyverse, knitr, ggbio, biovizBase, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, License: GPL-3 + file LICENSE MD5sum: 2ec5f64ba307776cbcfe5acface03bb2 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (ORCID: ), Ruining Dong [aut] (ORCID: ) Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: devel git_last_commit: 0faca97 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/StructuralVariantAnnotation_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/StructuralVariantAnnotation_1.23.0.zip vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependsOnMe: svaNUMT, svaRetro suggestsMe: shiny.gosling dependencyCount: 92 Package: SubCellBarCode Version: 1.23.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 49ced08324241ad505888525cf2129bc NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: devel git_last_commit: 260c964 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SubCellBarCode_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SubCellBarCode_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SubCellBarCode_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SubCellBarCode_1.23.0.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 138 Package: subSeq Version: 1.37.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE Archs: x64 MD5sum: 3fb3cdd26cefae528a575f42f2ad0ffe NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: devel git_last_commit: 694bd4d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/subSeq_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/subSeq_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/subSeq_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/subSeq_1.37.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 52 Package: SUITOR Version: 1.9.0 Depends: R (>= 4.2.0) Imports: stats, utils, graphics, ggplot2, BiocParallel Suggests: devtools, MutationalPatterns, RUnit, BiocManager, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 3d80aae3578383bec661c91dbd1647d6 NeedsCompilation: yes Title: Selecting the number of mutational signatures through cross-validation Description: An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data. biocViews: Genetics, Software, SomaticMutation Author: DongHyuk Lee [aut], Bin Zhu [aut], Bill Wheeler [cre] Maintainer: Bill Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/SUITOR/issues git_url: https://git.bioconductor.org/packages/SUITOR git_branch: devel git_last_commit: 293c905 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SUITOR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SUITOR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SUITOR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SUITOR_1.9.0.tgz vignettes: vignettes/SUITOR/inst/doc/vignette.pdf vignetteTitles: SUITOR: selecting the number of mutational signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SUITOR/inst/doc/vignette.R dependencyCount: 45 Package: SummarizedExperiment Version: 1.37.0 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.55.2), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.3), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), S4Arrays (>= 1.1.1), DelayedArray (>= 0.31.12) Suggests: jsonlite, rhdf5, HDF5Array (>= 1.7.5), annotate, AnnotationDbi, GenomicFeatures, SparseArray, SingleCellExperiment, TxDb.Hsapiens.UCSC.hg19.knownGene, hgu95av2.db, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 70e1a3afd21fa145657e4414256762b7 NeedsCompilation: no Title: A container (S4 class) for matrix-like assays Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan [aut], Valerie Obenchain [aut], Jim Hester [aut], Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: devel git_last_commit: cbdaaaa git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SummarizedExperiment_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SummarizedExperiment_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SummarizedExperiment_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SummarizedExperiment_1.37.0.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, alabaster.se, AllelicImbalance, atena, bambu, betaHMM, BiocSklearn, BiSeq, bnbc, broadSeq, bsseq, CAGEfightR, celaref, clusterExperiment, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deepSNV, DeMixT, DESeq2, DiffBind, diffcoexp, diffHic, dinoR, divergence, DMCFB, DMCHMM, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, ExperimentSubset, ExpressionAtlas, extraChIPs, FEAST, FRASER, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hermes, HERON, HiCDOC, hipathia, InteractionSet, iSEE, iSEEhex, iSEEhub, iSEEindex, ISLET, isomiRs, ivygapSE, lefser, LimROTS, lipidr, LoomExperiment, lute, Macarron, made4, MatrixQCvis, MBASED, methrix, methylPipe, MetNet, MGnifyR, mia, miaViz, MICSQTL, minfi, moanin, mpra, MultiAssayExperiment, multistateQTL, NADfinder, NBAMSeq, NewWave, orthos, OUTRIDER, padma, PDATK, phenomis, PhIPData, profileplyr, PRONE, qmtools, qsvaR, QTLExperiment, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scAnnotatR, scGPS, scone, screenCounter, scTreeViz, SDAMS, sechm, SeqGate, SEtools, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, soGGi, spillR, spqn, ssPATHS, stageR, survtype, TENxIO, tidyCoverage, tidySummarizedExperiment, TissueEnrich, TREG, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, BioPlex, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, fission, GSVAdata, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, MicrobiomeBenchmarkData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, parathyroidSE, sampleClassifierData, scMultiome, spatialDmelxsim, spqnData, timecoursedata, tuberculosis, DRomics, OncoSubtype, ordinalbayes importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, animalcules, anota2seq, APAlyzer, apeglm, APL, appreci8R, ASICS, ASURAT, ATACseqTFEA, AUCell, autonomics, awst, Banksy, barcodetrackR, BASiCS, BASiCStan, batchelor, BatchQC, BayesSpace, bayNorm, BBCAnalyzer, beer, BERT, bettr, BioGA, BioNERO, biosigner, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BUMHMM, BUScorrect, BUSseq, CaDrA, CAGEr, CATALYST, CatsCradle, cBioPortalData, ccfindR, ccImpute, CDI, celda, CelliD, CellMixS, CellScore, CellTrails, censcyt, Cepo, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, CleanUpRNAseq, clustifyr, cmapR, CNVfilteR, CNVRanger, CoGAPS, comapr, concordexR, condiments, consensusDE, consICA, CopyNumberPlots, corral, COTAN, countsimQC, CTexploreR, CTSV, CuratedAtlasQueryR, cydar, cypress, cytofQC, cytoKernel, cytomapper, cytoviewer, DAMEfinder, debCAM, debrowser, decompTumor2Sig, decontX, DEFormats, DEGreport, DELocal, deltaCaptureC, demuxSNP, DEP, DEScan2, DESpace, destiny, DEWSeq, diffcyt, DifferentialRegulation, Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DominoEffect, doppelgangR, doseR, dreamlet, DropletUtils, Dune, easyRNASeq, eisaR, ELMER, epigraHMM, EpiMix, epimutacions, epiregulon, epiregulon.extra, epistack, epivizrData, erma, escape, escheR, EWCE, fcScan, FeatSeekR, findIPs, FindIT2, fishpond, FLAMES, FuseSOM, GARS, gCrisprTools, gDNAx, gDRcore, gDRimport, gDRutils, gemma.R, GeneTonic, genomicInstability, GEOquery, GeoTcgaData, getDEE2, ggbio, ggsc, ggspavis, gINTomics, Glimma, glmGamPoi, glmSparseNet, GRaNIE, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HarmonizR, HiContacts, hoodscanR, hummingbird, HybridExpress, iasva, icetea, ideal, IFAA, IgGeneUsage, ILoReg, imcRtools, iNETgrate, infercnv, INSPEcT, iSEEde, iSEEfier, iSEEpathways, iSEEtree, iSEEu, IsoBayes, kmcut, LACE, lemur, limpca, lineagespot, lionessR, lisaClust, MADSEQ, MAI, mariner, marr, MAST, mastR, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, MetaboAnnotation, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, MethylAid, methyLImp2, methylscaper, methylumi, miaSim, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, MLInterfaces, MLSeq, mobileRNA, monaLisa, MoonlightR, mosdef, motifbreakR, motifmatchr, MotifPeeker, MPAC, MPRAnalyze, MsExperiment, MsFeatures, msgbsR, mspms, MSPrep, msqrob2, MuData, MultiDataSet, MultiRNAflow, multiWGCNA, mumosa, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, NetActivity, netSmooth, nipalsMCIA, nnSVG, NormalyzerDE, oligoClasses, omicRexposome, omicsPrint, omicsViewer, omXplore, oncomix, ontoProc, ORFik, OVESEG, PAIRADISE, pairkat, pcaExplorer, peco, PepSetTest, pgxRpi, PharmacoGx, phenopath, PhosR, pipeComp, Pirat, PIUMA, planttfhunter, plyxp, pmp, PolySTest, POMA, POWSC, proActiv, proDA, psichomics, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment, RareVariantVis, RcisTarget, ReactomeGSA, receptLoss, RegionalST, regsplice, rgsepd, rifi, rifiComparative, Rmmquant, RNAAgeCalc, RNAsense, roar, RolDE, ropls, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview, SC3, SCArray, SCArray.sat, scater, scBFA, scCB2, scDblFinder, scDD, scDDboost, scDesign3, scDiagnostics, scds, scHOT, scider, scmap, scMerge, scMET, scmeth, scMultiSim, SCnorm, scoreInvHap, scp, scPipe, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, scviR, segmenter, seqCAT, sesame, SGCP, sigFeature, SigsPack, simPIC, simpleSeg, SingleCellAlleleExperiment, singscore, slalom, slingshot, smartid, smoothclust, snapcount, SNPhood, Spaniel, SpaNorm, spaSim, SpatialCPie, spatialDE, SpatialExperiment, SpatialFeatureExperiment, spatialHeatmap, spatialSimGP, spatzie, SPIAT, spicyR, splatter, SpliceWiz, SplicingFactory, SplineDV, spoon, SpotClean, SpotSweeper, sSNAPPY, StabMap, standR, Statial, stJoincount, struct, StructuralVariantAnnotation, supersigs, SVMDO, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TCseq, tenXplore, TFutils, tidybulk, tidySingleCellExperiment, tidySpatialExperiment, TOAST, tomoda, ToxicoGx, tpSVG, tradeSeq, TrajectoryUtils, transformGamPoi, transmogR, treeclimbR, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, TTMap, TVTB, tximeta, UCell, VAExprs, VariantFiltering, VDJdive, vidger, VisiumIO, visiumStitched, Voyager, wpm, xcms, xenLite, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, easierData, emtdata, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GSE13015, HCATonsilData, HiBED, homosapienDEE2CellScore, IHWpaper, LegATo, MerfishData, MetaGxBreast, orthosData, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, TENxXeniumData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, DWLS, HeritSeq, imcExperiment, karyotapR, MetAlyzer, microbial, MOCHA, multimedia, PlasmaMutationDetector, PlasmaMutationDetector2, RCPA, RNAseqQC, SC.MEB, SCIntRuler, SCRIP, scROSHI, SpatialDDLS, treediff, VSOLassoBag suggestsMe: alabaster.mae, AlpsNMR, ANCOMBC, AnnotationHub, BindingSiteFinder, biobroom, BiocPkgTools, cageminer, CTdata, dcanr, dce, dearseq, decoupleR, DelayedArray, easier, edgeR, EnMCB, epialleleR, epivizr, epivizrChart, esetVis, fobitools, funOmics, gDR, GENIE3, GenomicRanges, globalSeq, gsean, hca, HDF5Array, HPiP, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, knowYourCG, MatrixGenerics, microSTASIS, MOFA2, MSnbase, pathwayPCA, philr, podkat, PSMatch, RiboProfiling, Rvisdiff, S4Vectors, scFeatureFilter, semisup, sketchR, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, tidytof, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, gDRtestData, GSE103322, multiWGCNAdata, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, clustree, conos, CytoSimplex, dyngen, file2meco, lfc, MiscMetabar, parafac4microbiome, polyRAD, RaceID, rliger, seqgendiff, Seurat, Signac, singleCellHaystack, speakeasyR, SuperCell, teal.slice, tidydr, volcano3D dependencyCount: 36 Package: Summix Version: 2.13.0 Depends: R (>= 4.3) Imports: dplyr, nloptr, magrittr, methods, tibble, tidyselect, BEDASSLE, scales, visNetwork, randomcoloR Suggests: rmarkdown, markdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 364f91b3a0de91670b810ef5fe070a37 NeedsCompilation: no Title: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data Description: This package contains the Summix2 method for estimating and adjusting for substructure in genetic summary allele frequency data. The function summix() estimates reference group proportions using a mixture model. The adjAF() function produces adjusted allele frequencies for an observed group with reference group proportions matching a target individual or sample. The summix_local() function estimates local ancestry mixture proportions and performs selection scans in genetic summary data. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], Price Adelle [aut], Stoneman Haley [aut] Maintainer: Audrey Hendricks VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: devel git_last_commit: a864fc6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Summix_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Summix_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Summix_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Summix_2.13.0.tgz vignettes: vignettes/Summix/inst/doc/Summix.html vignetteTitles: Summix.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Summix/inst/doc/Summix.R dependencyCount: 75 Package: supersigs Version: 1.15.0 Depends: R (>= 4.1) Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang, utils, Biostrings, stats, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, testthat, VariantAnnotation License: GPL-3 MD5sum: 8336d966189af1ea71990076bd9f80f3 NeedsCompilation: no Title: Supervised mutational signatures Description: Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife). biocViews: FeatureExtraction, Classification, Regression, Sequencing, WholeGenome, SomaticMutation Author: Albert Kuo [aut, cre] (ORCID: ), Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut] Maintainer: Albert Kuo URL: https://tomasettilab.github.io/supersigs/ VignetteBuilder: knitr BugReports: https://github.com/TomasettiLab/supersigs/issues git_url: https://git.bioconductor.org/packages/supersigs git_branch: devel git_last_commit: 4d9190b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/supersigs_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/supersigs_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/supersigs_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/supersigs_1.15.0.tgz vignettes: vignettes/supersigs/inst/doc/supersigs.html vignetteTitles: Using supersigs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supersigs/inst/doc/supersigs.R dependencyCount: 116 Package: surfaltr Version: 1.13.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: c1dfcad3085002ae70289d8326144ade NeedsCompilation: no Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr Description: Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette. biocViews: Software, Visualization, DataRepresentation, SplicedAlignment, Alignment, MultipleSequenceAlignment, MultipleComparison Author: Pooja Gangras [aut, cre] (ORCID: ), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: devel git_last_commit: 98fe9a8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/surfaltr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/surfaltr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/surfaltr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/surfaltr_1.13.0.tgz vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html vignetteTitles: surfaltr_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R dependencyCount: 113 Package: survClust Version: 1.1.0 Depends: R (>= 3.5.0) Imports: Rcpp, MultiAssayExperiment, pdist, survival LinkingTo: Rcpp Suggests: knitr, testthat (>= 3.0.0), gplots, htmltools, BiocParallel License: MIT + file LICENSE MD5sum: 27e183704a22631e2bcb8a577abc7da4 NeedsCompilation: yes Title: Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning Description: survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types). biocViews: Software, Clustering, Survival, Classification Author: Arshi Arora [aut, cre] (ORCID: ) Maintainer: Arshi Arora URL: https://github.com/arorarshi/survClust VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/survClust git_url: https://git.bioconductor.org/packages/survClust git_branch: devel git_last_commit: ddcd08b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/survClust_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survClust_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survClust_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survClust_1.1.0.tgz vignettes: vignettes/survClust/inst/doc/survClust_vignette.html vignetteTitles: An introduction to survClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/survClust/inst/doc/survClust_vignette.R dependencyCount: 62 Package: survcomp Version: 1.57.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: bf6a43615270ed945956518a6e8dd98e NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut], Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut], Zhaleh Safikhani [aut] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: devel git_last_commit: d938b84 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/survcomp_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survcomp_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survcomp_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survcomp_1.57.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: metaseqR2, PDATK, Coxmos, FLORAL, pencal, plsRcox, SIGN suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 39 Package: survtype Version: 1.23.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 2f374128127c956a7bf018507f2a7578 NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: devel git_last_commit: 150b74a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/survtype_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/survtype_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/survtype_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/survtype_1.23.0.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 137 Package: sva Version: 3.55.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: 52dfcf2063cecc3e914553cfe9faf5e2 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: devel git_last_commit: 5dc7e05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/sva_3.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/sva_3.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/sva_3.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/sva_3.55.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: DeMixT, SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BERT, BioNERO, bnbc, bnem, DaMiRseq, debrowser, DExMA, doppelgangR, edge, GEOexplorer, HarmonizR, KnowSeq, MatrixQCvis, MSPrep, omicRexposome, PAA, POMA, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, causalBatch, cinaR, dSVA, oncoPredict, scITD, seqgendiff, TransProR suggestsMe: compcodeR, Harman, iasva, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics, SuperLearner dependencyCount: 72 Package: svaNUMT Version: 1.13.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>= 4.0) Imports: assertthat, stringr, dplyr, methods, rlang, GenomeInfoDb, S4Vectors, GenomicFeatures, pwalign Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: 09ef8d622b09389e76c8dc98cabef187 NeedsCompilation: no Title: NUMT detection from structural variant calls Description: svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: devel git_last_commit: 1a144f0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/svaNUMT_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/svaNUMT_1.13.0.zip vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html vignetteTitles: svaNUMT Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R dependencyCount: 93 Package: svaRetro Version: 1.13.0 Depends: GenomicRanges, rtracklayer, BiocGenerics, StructuralVariantAnnotation, R (>= 4.0) Imports: VariantAnnotation, assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomicFeatures, GenomeInfoDb, S4Vectors, utils Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges, circlize, tictoc, IRanges, stats, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: 69b47570ac048ed05ebcd6831606b280 NeedsCompilation: no Title: Retrotransposed transcript detection from structural variants Description: svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation, Coverage, VariantDetection Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: devel git_last_commit: 4aeb18c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/svaRetro_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/svaRetro_1.13.0.zip vignettes: vignettes/svaRetro/inst/doc/svaRetro.html vignetteTitles: svaRetro Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R dependencyCount: 93 Package: SVMDO Version: 1.7.0 Depends: R(>= 4.4), shiny (>= 1.7.4) Imports: shinyFiles (>= 0.9.3), shinytitle (>= 0.1.0), golem (>= 0.3.5), nortest (>= 1.0-4), e1071 (>= 1.7-12), BSDA (>= 1.2.1), data.table (>= 1.14.6), sjmisc (>= 2.8.9), klaR (>= 1.7-1), caTools (>= 1.18.2), caret (>= 6.0-93), survival (>= 3.4-0), DT (>= 0.33.0), DOSE (>= 3.24.2), AnnotationDbi (>= 1.60.0), org.Hs.eg.db (>= 3.16.0), dplyr (>= 1.0.10), SummarizedExperiment (>= 1.28.0), grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.1.6) License: GPL-3 MD5sum: d16eba166422453f574113d644b3ac0e NeedsCompilation: no Title: Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology Description: It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets. biocViews: GeneSetEnrichment, DifferentialExpression, GUI, Classification, RNASeq, Transcriptomics, Survival Author: Mustafa Erhan Ozer [aut, cre] (ORCID: ), Pemra Ozbek Sarica [aut], Kazim Yalcin Arga [aut] Maintainer: Mustafa Erhan Ozer VignetteBuilder: knitr BugReports: https://github.com/robogeno/SVMDO/issues git_url: https://git.bioconductor.org/packages/SVMDO git_branch: devel git_last_commit: 5dea70d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SVMDO_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SVMDO_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SVMDO_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SVMDO_1.7.0.tgz vignettes: vignettes/SVMDO/inst/doc/SVMDO_guide.html vignetteTitles: SVMDO-Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVMDO/inst/doc/SVMDO_guide.R dependencyCount: 200 Package: SWATH2stats Version: 1.37.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: 6f9af9fb8174d97756d25b4a2bcf2fee NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: devel git_last_commit: 8dd5551 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SWATH2stats_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SWATH2stats_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SWATH2stats_1.37.0.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 90 Package: SwathXtend Version: 2.29.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 35f27c683fcb2e77d9de3991c55632d8 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: devel git_last_commit: 4c2c9ad git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SwathXtend_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SwathXtend_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SwathXtend_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SwathXtend_2.29.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.33.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: c5266935511d0c75ce95a89e57212bd9 NeedsCompilation: no Title: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: devel git_last_commit: fa8b787 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/swfdr_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/swfdr_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/swfdr_1.33.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: switchBox Version: 1.43.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: 1d71b2ec1e5685f710f303ee2f317114 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: devel git_last_commit: 606e3de git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/switchBox_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/switchBox_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/switchBox_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/switchBox_1.43.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R importsMe: PDATK suggestsMe: multiclassPairs dependencyCount: 11 Package: switchde Version: 1.33.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: beca95c533f01b5ec0ffa015b76d046d NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: devel git_last_commit: bf3d5a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/switchde_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/switchde_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/switchde_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/switchde_1.33.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 66 Package: synapsis Version: 1.13.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 7c84d0cdc93d34ca0daf1daea0018ca4 NeedsCompilation: no Title: An R package to automate the analysis of double-strand break repair during meiosis Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand. biocViews: Software, SingleCell Author: Lucy McNeill [aut, cre, cph] (ORCID: ), Wayne Crismani [rev, ctb] (ORCID: ) Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: devel git_last_commit: 5262f7d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/synapsis_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synapsis_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synapsis_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synapsis_1.13.0.tgz vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html vignetteTitles: Using-synapsis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R dependencyCount: 46 Package: synapter Version: 2.31.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle, knitr License: GPL-2 MD5sum: d8f6450a2a1639c7a5cb8888f57853db NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: devel git_last_commit: 4502012 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/synapter_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synapter_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synapter_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synapter_2.31.0.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R dependsOnMe: synapterdata dependencyCount: 151 Package: synergyfinder Version: 3.15.0 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: ebaeba298e4b0b3bd114073d5ae5ee45 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (http://www.synergyfinder.org) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: http://www.synergyfinder.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: devel git_last_commit: b44cfae git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/synergyfinder_3.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synergyfinder_3.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synergyfinder_3.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synergyfinder_3.15.0.tgz vignettes: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html vignetteTitles: User tutorial of the SynergyFinder Plus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R dependencyCount: 205 Package: SynExtend Version: 1.19.0 Depends: R (>= 4.4.0), DECIPHER (>= 2.28.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats, parallel, graphics, grDevices, RSQLite, DBI Suggests: BiocStyle, knitr, igraph, markdown, rmarkdown License: GPL-3 Archs: x64 MD5sum: 3f0f7c651bca2facaa218ea801c5d752 NeedsCompilation: yes Title: Tools for Working With Synteny Objects Description: Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (ORCID: ), Aidan Lakshman [aut, ctb] (ORCID: ), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley URL: https://github.com/npcooley/SynExtend VignetteBuilder: knitr BugReports: https://github.com/npcooley/SynExtend/issues/new/ git_url: https://git.bioconductor.org/packages/SynExtend git_branch: devel git_last_commit: 88b8d0f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SynExtend_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SynExtend_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SynExtend_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SynExtend_1.19.0.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.html vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 44 Package: synlet Version: 2.7.0 Depends: R (>= 3.5.0) Imports: data.table, ggplot2, grDevices, magrittr, methods, patchwork, RankProd, RColorBrewer, stats, utils Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-3 MD5sum: 7908cf6433f8448ef903f19ca385de49 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: devel git_last_commit: 007191f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/synlet_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/synlet_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/synlet_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/synlet_2.7.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 40 Package: SynMut Version: 1.23.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 6028356dd37f25ad42f3869b42a928ba NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: devel git_last_commit: 460f815 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/SynMut_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/SynMut_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SynMut_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SynMut_1.23.0.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 45 Package: syntenet Version: 1.9.1 Depends: R (>= 4.2) Imports: Rcpp (>= 1.0.8), BiocParallel, GenomicRanges, rlang, Biostrings, rtracklayer, utils, methods, igraph, stats, grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork, intergraph LinkingTo: Rcpp, testthat Suggests: BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat (>= 3.0.0), xml2, networkD3 License: GPL-3 MD5sum: efb95d590d0b2f1f3fe50800a0e91830 NeedsCompilation: yes Title: Inference And Analysis Of Synteny Networks Description: syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood. biocViews: Software, NetworkInference, FunctionalGenomics, ComparativeGenomics, Phylogenetics, SystemsBiology, GraphAndNetwork, WholeGenome, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Tao Zhao [aut] (ORCID: ), Kristian K Ullrich [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/syntenet VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/syntenet git_url: https://git.bioconductor.org/packages/syntenet git_branch: devel git_last_commit: 1d7e1a6 git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-18 source.ver: src/contrib/syntenet_1.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/syntenet_1.9.1.zip vignettes: vignettes/syntenet/inst/doc/syntenet.html vignetteTitles: Inference and Analysis of Synteny Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/syntenet/inst/doc/syntenet.R importsMe: doubletrouble dependencyCount: 110 Package: systemPipeR Version: 2.13.0 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr, magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata, GenomicAlignments, grid, dplyr, testthat, rjson, annotate, AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT, rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools, GenomicFeatures, txdbmaker, VariantAnnotation (>= 1.25.11) License: Artistic-2.0 MD5sum: ce37af5f8ac40f8b34dc5c0b0d6df1b7 NeedsCompilation: no Title: systemPipeR: Workflow Environment for Data Analysis and Report Generation Description: systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, WorkflowStep, WorkflowManagement Author: Thomas Girke Maintainer: Thomas Girke URL: https://systempipe.org/, https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: devel git_last_commit: 94357f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/systemPipeR_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/systemPipeR_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeR_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/systemPipeR_2.13.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflow Templates, Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 110 Package: systemPipeShiny Version: 1.17.0 Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps (>= 0.3.3), drawer (>= 0.2) Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite, openssl Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>= 2.2.0), systemPipeRdata (>= 2.0.0), rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.1.0), cicerone License: GPL (>= 3) Archs: x64 MD5sum: 1e23ad0dbe1f65de0e4a037ab3b17b06 NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: ShinyApps, Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://systempipe.org/sps, https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: devel git_last_commit: 204a1c6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/systemPipeShiny_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/systemPipeShiny_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeShiny_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/systemPipeShiny_1.17.0.tgz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 120 Package: systemPipeTools Version: 1.15.0 Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr, ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble, magrittr, DT, stats Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), BiocGenerics, Biostrings, methods License: Artistic-2.0 MD5sum: d9065495c04090382d6783f2e66d1fc6 NeedsCompilation: no Title: Tools for data visualization Description: systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering, DifferentialExpression, MultidimensionalScaling, PrincipalComponent Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut] Maintainer: Daniela Cassol VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: devel git_last_commit: 05bda0e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/systemPipeTools_1.15.0.tar.gz vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html vignetteTitles: systemPipeTools: Data Visualizations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R dependencyCount: 132 Package: tadar Version: 1.5.0 Depends: GenomicRanges, ggplot2, R (>= 4.4.0) Imports: BiocGenerics, GenomeInfoDb, Gviz, IRanges, MatrixGenerics, methods, rlang, Rsamtools, S4Vectors, stats, VariantAnnotation Suggests: BiocStyle, covr, knitr, limma, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 4ec1094982eb3ca36ccac18a841614ec NeedsCompilation: no Title: Transcriptome Analysis of Differential Allelic Representation Description: This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data. biocViews: Sequencing, RNASeq, SNP, GenomicVariation, VariantAnnotation, DifferentialExpression Author: Lachlan Baer [aut, cre] (ORCID: ), Stevie Pederson [aut] (ORCID: ) Maintainer: Lachlan Baer URL: https://github.com/baerlachlan/tadar VignetteBuilder: knitr BugReports: https://github.com/baerlachlan/tadar/issues git_url: https://git.bioconductor.org/packages/tadar git_branch: devel git_last_commit: 9f7bb4f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tadar_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tadar_1.5.0.zip vignettes: vignettes/tadar/inst/doc/dar.html vignetteTitles: DAR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tadar/inst/doc/dar.R dependencyCount: 157 Package: TADCompare Version: 1.17.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, SpectralTAD, magick, qpdf License: MIT + file LICENSE MD5sum: 32881617a9c8052ad6afc0f5aaa22646 NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/TADCompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: devel git_last_commit: e550de3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TADCompare_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TADCompare_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TADCompare_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TADCompare_1.17.0.tgz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 123 Package: tanggle Version: 1.13.0 Depends: R (>= 4.1), ggplot2 (>= 2.2.0), ggtree Imports: ape (>= 5.0), phangorn (>= 2.5), utils, methods Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown License: Artistic-2.0 MD5sum: 62a08f23ec68815dc692d6b5de693c27 NeedsCompilation: no Title: Visualization of Phylogenetic Networks Description: Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (ORCID: ), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (ORCID: ), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb] Maintainer: Klaus Schliep URL: https://klausvigo.github.io/tanggle, https://github.com/KlausVigo/tanggle VignetteBuilder: knitr BugReports: https://github.com/KlausVigo/tanggle/issues git_url: https://git.bioconductor.org/packages/tanggle git_branch: devel git_last_commit: 2fc19e5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tanggle_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tanggle_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tanggle_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tanggle_1.13.0.tgz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html, vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con *ggplot2*, ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R, vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 64 Package: TAPseq Version: 1.19.0 Depends: R (>= 4.0.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer, BiocStyle License: MIT + file LICENSE MD5sum: 17721615c70cf3cad40c4ee3d5f04c57 NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas R. Gschwind [aut, cre] (ORCID: ), Lars Velten [aut] (ORCID: ), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: devel git_last_commit: e6e4d9c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TAPseq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TAPseq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TAPseq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TAPseq_1.19.0.tgz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 91 Package: target Version: 1.21.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 Archs: x64 MD5sum: a260fb3073751a5663e61588e55351df NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: devel git_last_commit: 9383e81 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/target_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/target_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/target_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/target_1.21.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 53 Package: TargetDecoy Version: 1.13.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 Archs: x64 MD5sum: c0bcb1232fd8687a40b267bb604a39f8 NeedsCompilation: no Title: Diagnostic Plots to Evaluate the Target Decoy Approach Description: A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method. biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization Author: Elke Debrie [aut, cre], Lieven Clement [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ) Maintainer: Elke Debrie URL: https://www.bioconductor.org/packages/TargetDecoy, https://statomics.github.io/TargetDecoy/, https://github.com/statOmics/TargetDecoy/ VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: devel git_last_commit: d7af3b2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TargetDecoy_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetDecoy_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetDecoy_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetDecoy_1.13.0.tgz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 113 Package: TargetScore Version: 1.45.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 4cfb953bb22304b943a0456d58eece7b NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: devel git_last_commit: d419059 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TargetScore_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetScore_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetScore_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetScore_1.45.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 2.9.1 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) Archs: x64 MD5sum: 2777b71dd65d1e71480fd9cf04cc1b28 NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a flexible, fast and accurate method for targeted pre-processing of GC-MS data. The user provides a (often very large) set of GC chromatograms and a metabolite library of targets. The package will automatically search those targets in the chromatograms resulting in a data matrix that can be used for further data analysis. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza [aut, cre], Jan Lisec [aut], Henning Redestig [aut], Matt Hannah [aut] Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: devel git_last_commit: 5a422d0 git_last_commit_date: 2024-11-18 Date/Publication: 2024-11-18 source.ver: src/contrib/TargetSearch_2.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/TargetSearch_2.9.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TargetSearch_2.9.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TargetSearch_2.9.1.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TBSignatureProfiler Version: 1.19.0 Depends: R (>= 4.4.0) Imports: ASSIGN (>= 1.23.1), BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, glmnet, GSVA (>= 1.51.3), HGNChelper, magrittr, methods, pROC, RColorBrewer, reshape2, ROCit, S4Vectors, singscore, stats, SummarizedExperiment Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, impute, knitr, lintr, MASS, plyr, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE MD5sum: aa0286e25487845533860a2c6d28d499 NeedsCompilation: no Title: Profile RNA-Seq Data Using TB Pathway Signatures Description: Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility. biocViews: GeneExpression, DifferentialExpression Author: Aubrey R. Odom [aut, cre, dtm] (ORCID: ), David Jenkins [aut, org] (ORCID: ), Xutao Wang [aut], Yue Zhao [ctb] (ORCID: ), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Aubrey R. Odom URL: https://github.com/wejlab/TBSignatureProfiler https://wejlab.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/wejlab/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: devel git_last_commit: d7f3c8a git_last_commit_date: 2024-12-18 Date/Publication: 2024-12-19 source.ver: src/contrib/TBSignatureProfiler_1.19.0.tar.gz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.R suggestsMe: LegATo dependencyCount: 184 Package: TCC Version: 1.47.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, ROC Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: e8552fe4193d34ba7cad72f92188e466 NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: devel git_last_commit: 8a4c02e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TCC_1.47.0.tar.gz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR dependencyCount: 86 Package: TCGAbiolinks Version: 2.35.0 Depends: R (>= 4.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>= 1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, Biobase, affy, testthat, sesame, AnnotationHub, ExperimentHub, pathview, clusterProfiler, Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid, DT License: GPL (>= 3) MD5sum: 1192d63da5a193ff69dc064c144b5b12 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: devel git_last_commit: cd39121 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TCGAbiolinks_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCGAbiolinks_2.35.0.zip vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/stemness_score.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 11. Stemness score, 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/stemness_score.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: ELMER, MoonlightR, SingscoreAMLMutations, TCGAWorkflow, CureAuxSP, oncoPredict suggestsMe: GeoTcgaData, iNETgrate, musicatk dependencyCount: 114 Package: TCGAutils Version: 1.27.6 Depends: R (>= 4.2.0) Imports: AnnotationDbi, BiocGenerics, BiocBaseUtils, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: AnnotationHub, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, httr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox (>= 2.17.4), rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 271b9907a3c60aa51fb238e21030c991 NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. biocViews: Software, WorkflowStep, Preprocessing, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: devel git_last_commit: 8e4301d git_last_commit_date: 2024-12-16 Date/Publication: 2024-12-19 source.ver: src/contrib/TCGAutils_1.27.6.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCGAutils_1.27.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCGAutils_1.27.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCGAutils_1.27.6.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: cBioPortalData, glmSparseNet, RTCGAToolbox, terraTCGAdata suggestsMe: CNVRanger, dce, curatedTCGAData dependencyCount: 105 Package: TCseq Version: 1.31.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: bc1f4a462a2bba10b05a9ed120145239 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: devel git_last_commit: 2ff417d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TCseq_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TCseq_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TCseq_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TCseq_1.31.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R dependencyCount: 91 Package: TDbasedUFE Version: 1.7.0 Imports: GenomicRanges, rTensor, readr, methods, MOFAdata, tximport, tximportData, graphics, stats, utils, shiny Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 1e278fe8bc4250b37bede529534aafe0 NeedsCompilation: no Title: Tensor Decomposition Based Unsupervised Feature Extraction Description: This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFE VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFE/issues git_url: https://git.bioconductor.org/packages/TDbasedUFE git_branch: devel git_last_commit: 5b8c87d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TDbasedUFE_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TDbasedUFE_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TDbasedUFE_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TDbasedUFE_1.7.0.tgz vignettes: vignettes/TDbasedUFE/inst/doc/QuickStart.html, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.html vignetteTitles: QuickStart, TDbasedUFE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDbasedUFE/inst/doc/QuickStart.R, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.R importsMe: TDbasedUFEadv dependencyCount: 73 Package: TDbasedUFEadv Version: 1.7.0 Imports: TDbasedUFE, Biobase, GenomicRanges, utils, rTensor, methods, graphics, RTCGA, stats, enrichplot, DOSE, STRINGdb, enrichR, hash, shiny Suggests: knitr, rmarkdown, testthat (>= 3.0.0), RTCGA.rnaseq, RTCGA.clinical, BiocStyle, MOFAdata License: GPL-3 MD5sum: c60456738e4ccc821ecefee0d4cc93ca NeedsCompilation: no Title: Advanced package of tensor decomposition based unsupervised feature extraction Description: This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell, Software Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFEadv VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFEadv/issues git_url: https://git.bioconductor.org/packages/TDbasedUFEadv git_branch: devel git_last_commit: 8134ead git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TDbasedUFEadv_1.7.0.tar.gz vignettes: vignettes/TDbasedUFEadv/inst/doc/Enrichment.html, vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.html, vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.html vignetteTitles: Enrichment, Explanation of TDbasedUFEadv, How to use TDbasedUFEadv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDbasedUFEadv/inst/doc/Enrichment.R, vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.R, vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.R dependencyCount: 218 Package: TEKRABber Version: 1.11.0 Depends: R (>= 4.3) Imports: apeglm, biomaRt, dplyr, doParallel, DESeq2, foreach, GenomeInfoDb, magrittr, Rcpp (>= 1.0.7), rtracklayer, SCBN, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, bslib, ggplot2, ggpubr, plotly, rmarkdown, shiny, knitr, testthat (>= 3.0.0) License: LGPL (>=3) MD5sum: 0cfc59ea227d7c5752a1cdcc1b9fdfce NeedsCompilation: yes Title: An R package estimates the correlations of orthologs and transposable elements between two species Description: TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes. biocViews: DifferentialExpression, Normalization, Transcription, GeneExpression Author: Yao-Chung Chen [aut, cre] (ORCID: ), Katja Nowick [aut] (ORCID: ) Maintainer: Yao-Chung Chen URL: https://github.com/ferygood/TEKRABber VignetteBuilder: knitr BugReports: https://github.com/ferygood/TEKRABber/issues git_url: https://git.bioconductor.org/packages/TEKRABber git_branch: devel git_last_commit: 3e1203e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TEKRABber_1.11.0.tar.gz vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html vignetteTitles: TEKRABber hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R dependencyCount: 132 Package: TENxIO Version: 1.9.3 Depends: R (>= 4.2.0), SingleCellExperiment, SummarizedExperiment Imports: BiocBaseUtils, BiocGenerics, BiocIO, GenomeInfoDb, GenomicRanges, HDF5Array, Matrix, MatrixGenerics, methods, RCurl, readr, rhdf5, R.utils, S4Vectors, utils Suggests: BiocStyle, DropletTestFiles, ExperimentHub, knitr, RaggedExperiment (>= 1.29.5), rmarkdown, Rsamtools, tinytest License: Artistic-2.0 MD5sum: 3ae09f0ee661a459ff4a366f63a86f4a NeedsCompilation: no Title: Import methods for 10X Genomics files Description: Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment. biocViews: Software, Infrastructure, DataImport, SingleCell Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/TENxIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TENxIO/issues git_url: https://git.bioconductor.org/packages/TENxIO git_branch: devel git_last_commit: c6c5a73 git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-21 source.ver: src/contrib/TENxIO_1.9.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/TENxIO_1.9.3.zip vignettes: vignettes/TENxIO/inst/doc/TENxIO.html vignetteTitles: TENxIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TENxIO/inst/doc/TENxIO.R dependsOnMe: VisiumIO importsMe: xenLite dependencyCount: 72 Package: tenXplore Version: 1.29.0 Depends: R (>= 4.0), shiny Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils, BiocFileCache Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 4c105d6cc268c46a462d653dd79c2ec3 NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: devel git_last_commit: 4caaa52 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tenXplore_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tenXplore_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tenXplore_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tenXplore_1.29.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 122 Package: TEQC Version: 4.29.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) Archs: x64 MD5sum: b9618179f716d73ee969a5b22e3bad6b NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Sarah Bonnin git_url: https://git.bioconductor.org/packages/TEQC git_branch: devel git_last_commit: 73056f5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TEQC_4.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TEQC_4.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TEQC_4.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TEQC_4.29.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 42 Package: ternarynet Version: 1.51.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 60d2f56af87db6806e1c378e3094611c NeedsCompilation: yes Title: Ternary Network Estimation Description: Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI. biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: devel git_last_commit: 251d20a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ternarynet_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ternarynet_1.51.0.zip vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 26 Package: terraTCGAdata Version: 1.11.0 Depends: AnVILGCP, MultiAssayExperiment Imports: AnVIL, BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: AnVILBase, knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 5d4e8cb16d91dc7ac9acdb7fbd36038c NeedsCompilation: no Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment Description: Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra. biocViews: Software, Infrastructure, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/terraTCGAdata VignetteBuilder: knitr BugReports: https://github.com/waldronlab/terraTCGAdata/issues git_url: https://git.bioconductor.org/packages/terraTCGAdata git_branch: devel git_last_commit: 3e21167 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/terraTCGAdata_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/terraTCGAdata_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/terraTCGAdata_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/terraTCGAdata_1.11.0.tgz vignettes: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.html vignetteTitles: Obtain Terra TCGA data as MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.R dependencyCount: 141 Package: TFARM Version: 1.29.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 4dda7040a617252c3e9e6df3e62778fb NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: devel git_last_commit: da642eb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TFARM_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFARM_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFARM_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFARM_1.29.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 48 Package: TFBSTools Version: 1.45.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), pwalign, BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown License: GPL-2 MD5sum: 490ce2cc1e1df4f5ee7848094a4ced99 NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: devel git_last_commit: 58fd543 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TFBSTools_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFBSTools_1.45.0.zip vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: ATACseqTFEA, chromVAR, esATAC, MatrixRider, MethReg, monaLisa, motifmatchr, motifStack, primirTSS, spatzie suggestsMe: enhancerHomologSearch, GRaNIE, MAGAR, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 126 Package: TFEA.ChIP Version: 1.27.0 Depends: R (>= 3.5) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 43072548f15ea8e9cd47915f2cefb674 NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: devel git_last_commit: 34b5d7e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TFEA.ChIP_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFEA.ChIP_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFEA.ChIP_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFEA.ChIP_1.27.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R dependencyCount: 105 Package: TFHAZ Version: 1.29.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: fae831b95472009758d8f0df44768303 NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli Maintainer: Gaia Ceddia VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: devel git_last_commit: b5ab06b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TFHAZ_1.29.0.tar.gz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 140 Package: TFutils Version: 1.27.1 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils, GenomicFiles, SummarizedExperiment Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomeInfoDb, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 76c534adf00082e781104d83907e42cf NeedsCompilation: no Title: TFutils Description: This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided. biocViews: Transcriptomics Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: devel git_last_commit: 6c53651 git_last_commit_date: 2024-11-12 Date/Publication: 2024-11-13 source.ver: src/contrib/TFutils_1.27.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/TFutils_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TFutils_1.27.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TFutils_1.27.1.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R dependencyCount: 134 Package: tidybulk Version: 1.19.0 Depends: R (>= 4.4.0), ttservice (>= 0.3.6) Imports: tibble, readr, dplyr (>= 1.1.0), magrittr, tidyr, stringi, stringr, rlang, purrr, tidyselect, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, GenomicRanges, methods, S4Vectors, crayon, Matrix Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional, survminer, tidySummarizedExperiment, markdown, uwot, matrixStats, igraph, EGSEA, IRanges, here, glmmSeq, pbapply, pbmcapply, lme4, glmmTMB, MASS, pkgconfig License: GPL-3 MD5sum: 1adcfa18adde11c0d0a57e9b83b0df46 NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidybulk VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidybulk/issues git_url: https://git.bioconductor.org/packages/tidybulk git_branch: devel git_last_commit: 1a18995 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidybulk_1.19.0.tar.gz vignettes: vignettes/tidybulk/inst/doc/introduction.html vignetteTitles: Overview of the tidybulk package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/introduction.R importsMe: tidyomics dependencyCount: 109 Package: tidyCoverage Version: 1.3.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, BiocParallel, BiocIO, rtracklayer, methods, tidyr, ggplot2, dplyr, fansi, pillar, rlang, scales, cli, purrr, vctrs, stats Suggests: tidySummarizedExperiment, plyranges, TxDb.Hsapiens.UCSC.hg19.knownGene, AnnotationHub, GenomicFeatures, BiocStyle, hues, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ac1ea52fcc07e9744b25a63fcb83a8cb NeedsCompilation: no Title: Extract and aggregate genomic coverage over features of interest Description: `tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows. biocViews: Software, Sequencing, Coverage, Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/tidyCoverage VignetteBuilder: knitr BugReports: https://github.com/js2264/tidyCoverage/issues git_url: https://git.bioconductor.org/packages/tidyCoverage git_branch: devel git_last_commit: defcab5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidyCoverage_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyCoverage_1.3.0.zip vignettes: vignettes/tidyCoverage/inst/doc/tidyCoverage.html vignetteTitles: Introduction to tidyCoverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyCoverage/inst/doc/tidyCoverage.R dependencyCount: 91 Package: tidyFlowCore Version: 1.1.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, flowCore, ggplot2, methods, purrr, rlang, stats, stringr, tibble, tidyr Suggests: BiocStyle, HDCytoData, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 97d2f37a89700f78b254def2269957ee NeedsCompilation: no Title: tidyFlowCore: Bringing flowCore to the tidyverse Description: tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data. biocViews: SingleCell, FlowCytometry, Infrastructure Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://github.com/keyes-timothy/tidyFlowCore, https://keyes-timothy.github.io/tidyFlowCore/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidyFlowCore/issues git_url: https://git.bioconductor.org/packages/tidyFlowCore git_branch: devel git_last_commit: a9a0990 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidyFlowCore_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyFlowCore_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidyFlowCore_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidyFlowCore_1.1.0.tgz vignettes: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.html vignetteTitles: tidyFlowCore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.R dependencyCount: 55 Package: tidyomics Version: 1.3.0 Depends: R (>= 4.2) Imports: tidySummarizedExperiment, tidySingleCellExperiment, tidyseurat, tidybulk, plyranges, nullranges, purrr, rlang, stringr, cli, utils Suggests: tidyr, dplyr, tibble, ggplot2, mockr (>= 0.2.0), knitr (>= 1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6) License: MIT + file LICENSE MD5sum: bda7056bf0a22a1c3b8c87832c4b89ce NeedsCompilation: no Title: Easily install and load the tidyomics ecosystem Description: The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ), William Hutchison [aut] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/tidyomics/tidyomics VignetteBuilder: knitr BugReports: https://github.com/tidyomics/tidyomics/issues git_url: https://git.bioconductor.org/packages/tidyomics git_branch: devel git_last_commit: b8e3de3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidyomics_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidyomics_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidyomics_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidyomics_1.3.0.tgz vignettes: vignettes/tidyomics/inst/doc/loading-tidyomics.html vignetteTitles: Loading the tidyomics ecosystem hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyomics/inst/doc/loading-tidyomics.R dependencyCount: 207 Package: tidysbml Version: 1.1.0 Depends: R (>= 4.4.0) Imports: xml2, methods Suggests: rmarkdown, knitr, BiocStyle, biomaRt, RCy3, testthat (>= 3.0.0) License: CC BY 4.0 MD5sum: 17d4687204a874679c479fca1cb2cf98 NeedsCompilation: no Title: Extract SBML's data into dataframes Description: Starting from one SBML file, it extracts information from each listOfCompartments, listOfSpecies and listOfReactions element by saving them into data frames. Each table provides one row for each entity (i.e. either compartment, species, reaction or speciesReference) and one set of columns for the attributes, one column for the content of the 'notes' subelement and one set of columns for the content of the 'annotation' subelement. biocViews: GraphAndNetwork, Network, Pathways, Software Author: Veronica Paparozzi [aut, cre] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Veronica Paparozzi URL: https://github.com/veronicapaparozzi/tidysbml VignetteBuilder: knitr BugReports: https://github.com/veronicapaparozzi/tidysbml/issues git_url: https://git.bioconductor.org/packages/tidysbml git_branch: devel git_last_commit: 1c63b02 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidysbml_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidysbml_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidysbml_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidysbml_1.1.0.tgz vignettes: vignettes/tidysbml/inst/doc/tidysbml-introduction.html vignetteTitles: Introduction to the tidysbml package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidysbml/inst/doc/tidysbml-introduction.R dependencyCount: 5 Package: tidySingleCellExperiment Version: 1.17.0 Depends: R (>= 4.4.0), SingleCellExperiment Imports: dplyr, tidyr, ttservice (>= 0.4.0), SummarizedExperiment, tibble, ggplot2, magrittr, rlang, purrr, pkgconfig, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, Matrix, stats Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, uwot, celldex, dittoSeq, plotly License: GPL-3 MD5sum: da509aa04643501977faf2512a7fdbf8 NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: 'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks). biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, SingleCell, GeneExpression, Normalization, Clustering, QualityControl, Sequencing Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySingleCellExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: devel git_last_commit: 9469430 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidySingleCellExperiment_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySingleCellExperiment_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySingleCellExperiment_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySingleCellExperiment_1.17.0.tgz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R dependsOnMe: tidySpatialExperiment importsMe: tidyomics suggestsMe: CuratedAtlasQueryR, sccomp, spicyWorkflow dependencyCount: 100 Package: tidySpatialExperiment Version: 1.3.0 Depends: R (>= 4.3.0), SpatialExperiment, tidySingleCellExperiment Imports: ttservice, SummarizedExperiment, SingleCellExperiment, BiocGenerics, Matrix, S4Vectors, methods, utils, pkgconfig, tibble, dplyr, tidyr, ggplot2, plotly, rlang, purrr, stringr, vctrs, tidyselect, pillar, cli, fansi, lifecycle, magick, tidygate (>= 1.0.13), shiny Suggests: BiocStyle, testthat, knitr, markdown, scater, igraph, cowplot, DropletUtils, tidySummarizedExperiment License: GPL (>= 3) MD5sum: 18f9001d2c8abfb27a4e124b19af9e4b NeedsCompilation: no Title: SpatialExperiment with tidy principles Description: tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object. biocViews: Infrastructure, RNASeq, GeneExpression, Sequencing, Spatial, Transcriptomics, SingleCell Author: William Hutchison [aut, cre] (ORCID: ), Stefano Mangiola [aut] Maintainer: William Hutchison URL: https://github.com/william-hutchison/tidySpatialExperiment, https://william-hutchison.github.io/tidySpatialExperiment/ VignetteBuilder: knitr BugReports: https://github.com/william-hutchison/tidySpatialExperiment/issues git_url: https://git.bioconductor.org/packages/tidySpatialExperiment git_branch: devel git_last_commit: 317a6d5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidySpatialExperiment_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySpatialExperiment_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySpatialExperiment_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySpatialExperiment_1.3.0.tgz vignettes: vignettes/tidySpatialExperiment/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySpatialExperiment/inst/doc/overview.R dependencyCount: 121 Package: tidySummarizedExperiment Version: 1.17.0 Depends: R (>= 4.3.0), SummarizedExperiment, ttservice (>= 0.4.0) Imports: dplyr, tibble (>= 3.0.4), magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, stats, pkgconfig Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, plotly License: GPL-3 MD5sum: 377d1b39fb4be6bdacb9e0845ad189fc NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: devel git_last_commit: 84d246b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidySummarizedExperiment_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidySummarizedExperiment_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidySummarizedExperiment_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidySummarizedExperiment_1.17.0.tgz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R importsMe: tidyomics suggestsMe: nullranges, tidybulk, tidyCoverage, tidySpatialExperiment dependencyCount: 99 Package: tidytof Version: 1.1.0 Depends: R (>= 4.3) Imports: doParallel, dplyr, flowCore, foreach, ggplot2, ggraph, glmnet, methods, parallel, purrr, readr, recipes, rlang, stringr, survival, tidygraph, tidyr, tidyselect, yardstick, Rcpp, tibble, stats, utils, RcppHNSW LinkingTo: Rcpp Suggests: ConsensusClusterPlus, Biobase, broom, covr, diffcyt, emdist, FlowSOM, forcats, ggrepel, HDCytoData, knitr, markdown, philentropy, rmarkdown, Rtsne, statmod, SummarizedExperiment, testthat (>= 3.0.0), lmerTest, lme4, ggridges, spelling, scattermore, preprocessCore, SingleCellExperiment, Seurat, SeuratObject, embed, rsample, BiocGenerics License: MIT + file LICENSE MD5sum: eb48ada25d0f81466a915917589b6c5f NeedsCompilation: yes Title: Analyze High-dimensional Cytometry Data Using Tidy Data Principles Description: This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis. biocViews: SingleCell, FlowCytometry Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://keyes-timothy.github.io/tidytof, https://keyes-timothy.github.io/tidytof/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidytof/issues git_url: https://git.bioconductor.org/packages/tidytof git_branch: devel git_last_commit: 3ca5f4a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tidytof_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tidytof_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tidytof_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tidytof_1.1.0.tgz vignettes: vignettes/tidytof/inst/doc/clustering.html, vignettes/tidytof/inst/doc/contributing-to-tidytof.html, vignettes/tidytof/inst/doc/differential-discovery-analysis.html, vignettes/tidytof/inst/doc/dimensionality-reduction.html, vignettes/tidytof/inst/doc/downsampling.html, vignettes/tidytof/inst/doc/feature-extraction.html, vignettes/tidytof/inst/doc/modeling.html, vignettes/tidytof/inst/doc/preprocessing.html, vignettes/tidytof/inst/doc/quality-control.html, vignettes/tidytof/inst/doc/reading-and-writing-data.html, vignettes/tidytof/inst/doc/tidytof.html vignetteTitles: 07. Clustering and metaclustering, 11. How to contribute code, 08. Differential discovery analysis, 06. Dimensionality reduction, 05. Downsampling, 09. Feature extraction, 10. Modeling, 04. Preprocessing, 03. Quality control, 02. Reading and writing data, 01. Getting started with tidytof hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidytof/inst/doc/clustering.R, vignettes/tidytof/inst/doc/contributing-to-tidytof.R, vignettes/tidytof/inst/doc/differential-discovery-analysis.R, vignettes/tidytof/inst/doc/dimensionality-reduction.R, vignettes/tidytof/inst/doc/downsampling.R, vignettes/tidytof/inst/doc/feature-extraction.R, vignettes/tidytof/inst/doc/modeling.R, vignettes/tidytof/inst/doc/preprocessing.R, vignettes/tidytof/inst/doc/quality-control.R, vignettes/tidytof/inst/doc/reading-and-writing-data.R, vignettes/tidytof/inst/doc/tidytof.R dependencyCount: 116 Package: tigre Version: 1.61.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 MD5sum: e7a209a121b70f5b65652618b1da6799 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: devel git_last_commit: 34c6d0d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tigre_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tigre_1.61.0.zip vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 54 Package: TileDBArray Version: 1.17.0 Depends: SparseArray (>= 1.5.20), DelayedArray (>= 0.31.7) Imports: methods, tiledb, S4Vectors Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 5bda2d76099598555776f593f05525cc NeedsCompilation: no Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: devel git_last_commit: 277ceda git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TileDBArray_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TileDBArray_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TileDBArray_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TileDBArray_1.17.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R dependencyCount: 35 Package: tilingArray Version: 1.85.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: 2df281cf6701be75f4f898bb997a1266 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: devel git_last_commit: 10dd5d0 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tilingArray_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tilingArray_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tilingArray_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tilingArray_1.85.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2 dependencyCount: 87 Package: timecourse Version: 1.79.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL Archs: x64 MD5sum: 4328777e387a0105b8192ff81b65a2ff NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: devel git_last_commit: e883bf8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/timecourse_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timecourse_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timecourse_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timecourse_1.79.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 12 Package: timeOmics Version: 1.19.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, lmtest, plyr, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 1c3c9cc11e7454ac5f1b0c6512c5d46d NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: devel git_last_commit: 6546b5f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/timeOmics_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timeOmics_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timeOmics_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timeOmics_1.19.0.tgz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R dependencyCount: 95 Package: timescape Version: 1.31.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: a76b5ed82ef58f46cf63e07f1be5b07d NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: devel git_last_commit: 9a6dbe7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/timescape_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/timescape_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/timescape_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/timescape_1.31.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 47 Package: TIN Version: 1.39.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 30a83a5e9a677815534787511fc386af NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: devel git_last_commit: 6ae7ba4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TIN_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TIN_1.39.0.zip vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 135 Package: TissueEnrich Version: 1.27.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: d427cdc0fb06d1e8ab300955a3304a78 NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: devel git_last_commit: ba84df9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TissueEnrich_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TissueEnrich_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TissueEnrich_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TissueEnrich_1.27.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 88 Package: TitanCNA Version: 1.45.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 MD5sum: 1d8b0cee3df546fc4c7768202c9590b9 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: devel git_last_commit: 19f37d4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TitanCNA_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TitanCNA_1.45.0.zip vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 91 Package: tkWidgets Version: 1.85.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: f4ddf5399ad2f79e05610843c21b5c25 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: devel git_last_commit: 7a9c05a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tkWidgets_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tkWidgets_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tkWidgets_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tkWidgets_1.85.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: tLOH Version: 1.15.0 Depends: R (>= 4.2) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics, bestNormalize, depmixS4, naniar, stringr Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 504b8c4e1c4025fbd311f6090c9bb672 NeedsCompilation: no Title: Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP). biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression, Transcriptomics Author: Michelle Webb [cre, aut], David Craig [aut] Maintainer: Michelle Webb URL: https://github.com/USCDTG/tLOH VignetteBuilder: knitr BugReports: https://github.com/USCDTG/tLOH/issues git_url: https://git.bioconductor.org/packages/tLOH git_branch: devel git_last_commit: fba6f99 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tLOH_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tLOH_1.15.0.zip vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html vignetteTitles: tLOH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R dependencyCount: 165 Package: TMixClust Version: 1.29.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) Archs: x64 MD5sum: 6c0a537b36627863903784fc6fbbd092 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: devel git_last_commit: 67da9fc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TMixClust_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TMixClust_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TMixClust_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TMixClust_1.29.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 32 Package: TMSig Version: 1.1.0 Depends: R (>= 4.4.0), limma Imports: circlize, ComplexHeatmap, data.table, grDevices, grid, GSEABase, Matrix, methods, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 9246ff776e87b50a42f49fc346729b68 NeedsCompilation: no Title: Tools for Molecular Signatures Description: The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis. biocViews: Clustering, GeneSetEnrichment, GraphAndNetwork, Pathways, Visualization Author: Tyler Sagendorf [aut, cre] (ORCID: ), Di Wu [ctb], Gordon Smyth [ctb] Maintainer: Tyler Sagendorf URL: https://github.com/EMSL-Computing/TMSig VignetteBuilder: knitr BugReports: https://github.com/EMSL-Computing/TMSig/issues git_url: https://git.bioconductor.org/packages/TMSig git_branch: devel git_last_commit: fcdbeea git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TMSig_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TMSig_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TMSig_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TMSig_1.1.0.tgz vignettes: vignettes/TMSig/inst/doc/TMSig.html vignetteTitles: An Introduction to TMSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMSig/inst/doc/TMSig.R dependencyCount: 74 Package: TnT Version: 1.29.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 MD5sum: 9bc4e132220369fe862332485979a3ed NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: devel git_last_commit: 3af21c4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TnT_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TnT_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TnT_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TnT_1.29.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 51 Package: TOAST Version: 1.21.0 Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog Imports: stats, methods, SummarizedExperiment, corpcor, doParallel, parallel, ggplot2, tidyr, GGally Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix License: GPL-2 Archs: x64 MD5sum: a23c4fa1cee103bf6368d051da1df8d9 NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: devel git_last_commit: 3fa4c26 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TOAST_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TOAST_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TOAST_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TOAST_1.21.0.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R importsMe: MICSQTL, RegionalST dependencyCount: 94 Package: tomoda Version: 1.17.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 435346198d6cb76c0ab1c442dfb21b81 NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Clustering, Visualization Author: Wendao Liu [aut, cre] (ORCID: ) Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: devel git_last_commit: dcf5dcb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tomoda_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tomoda_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tomoda_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tomoda_1.17.0.tgz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 79 Package: tomoseqr Version: 1.11.0 Depends: R (>= 4.2) Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr, methods, shiny, BiocFileCache, readr, tools, plotly, ggplot2 Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: f51a85e39fa55ea1b1977be9cd395bf0 NeedsCompilation: no Title: R Package for Analyzing Tomo-seq Data Description: `tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Visualization, Software Author: Ryosuke Matsuzawa [aut, cre] (ORCID: ) Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: devel git_last_commit: d515f3f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tomoseqr_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tomoseqr_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tomoseqr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tomoseqr_1.11.0.tgz vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html vignetteTitles: tomoseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R dependencyCount: 102 Package: TOP Version: 1.7.0 Depends: R (>= 3.5.0) Imports: assertthat, caret, ClassifyR, directPA, doParallel, dplyr, ggnewscale, ggplot2, ggraph, ggrepel, ggthemes, glmnet, Hmisc, igraph, latex2exp, limma, magrittr, methods, plotly, pROC, purrr, reshape2, stats, stringr, survival, tibble, tidygraph, tidyr, statmod Suggests: knitr, rmarkdown, BiocStyle, Biobase, curatedOvarianData, ggbeeswarm, ggsci, survminer, tidyverse License: GPL-3 MD5sum: 1612a2158529fb5e90b70e73d1bc83cc NeedsCompilation: no Title: TOP Constructs Transferable Model Across Gene Expression Platforms Description: TOP constructs a transferable model across gene expression platforms for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The TOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models. biocViews: Software, Survival, GeneExpression Author: Harry Robertson [aut, cre] (ORCID: ), Nicholas Robertson [aut] Maintainer: Harry Robertson URL: https://github.com/Harry25R/TOP VignetteBuilder: knitr BugReports: https://github.com/Harry25R/TOP/issues git_url: https://git.bioconductor.org/packages/TOP git_branch: devel git_last_commit: ade71fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TOP_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TOP_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TOP_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TOP_1.7.0.tgz vignettes: vignettes/TOP/inst/doc/BuildingATOPModel.html vignetteTitles: "Introduction to TOP" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOP/inst/doc/BuildingATOPModel.R dependencyCount: 213 Package: topconfects Version: 1.23.2 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2, scales, grid, grDevices Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: 8c986a2624e8c0f82d8c42ce05238c7c NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: devel git_last_commit: ef5968f git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-11 source.ver: src/contrib/topconfects_1.23.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/topconfects_1.23.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topconfects_1.23.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topconfects_1.23.2.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: GeoTcgaData, weitrix dependencyCount: 37 Package: topdownr Version: 1.29.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.4-2), MSnbase (>= 2.3.10), PSMatch (>= 1.6.0), ggplot2 (>= 2.2.1), mzR (>= 2.27.5) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 185c46092d0570febe79b77ae16050c7 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (ORCID: ), Pavel Shliaha [aut] (ORCID: ), Ole Nørregaard Jensen [aut] (ORCID: ) Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: devel git_last_commit: 9e53e8c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/topdownr_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/topdownr_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topdownr_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topdownr_1.29.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependsOnMe: topdownrdata dependencyCount: 138 Package: topGO Version: 2.59.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: fe71239bebd66d43c7edbb9dd72c6d46 NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa git_url: https://git.bioconductor.org/packages/topGO git_branch: devel git_last_commit: 2711dac git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/topGO_2.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/topGO_2.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/topGO_2.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/topGO_2.59.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, compEpiTools, EGSEA, ideal, moanin, tRanslatome, maEndToEnd importsMe: APL, cellity, consICA, GRaNIE, mosdef, OmaDB, pcaExplorer, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: fenr, FGNet, GeDi, geva, IntramiRExploreR, miRNAtap dependencyCount: 52 Package: ToxicoGx Version: 2.11.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma, jsonlite Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable, markdown License: MIT + file LICENSE MD5sum: 7001ac978542466c845a2383c7b787e0 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: devel git_last_commit: 15ee7e7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ToxicoGx_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ToxicoGx_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ToxicoGx_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ToxicoGx_2.11.0.tgz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 145 Package: TPP Version: 3.35.0 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 58c234aafa878dd05299ddbad3ecd683 NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: devel git_last_commit: d3e2611 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TPP_3.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TPP_3.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TPP_3.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TPP_3.35.0.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R suggestsMe: Rtpca dependencyCount: 96 Package: TPP2D Version: 1.23.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: 60a62e959ebeb14d5800b8a2a7e51eaf NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: devel git_last_commit: 9179fb5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TPP2D_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TPP2D_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TPP2D_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TPP2D_1.23.0.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 63 Package: tpSVG Version: 1.3.0 Depends: mgcv, R (>= 4.4) Imports: stats, BiocParallel, MatrixGenerics, methods, SingleCellExperiment, SummarizedExperiment, SpatialExperiment Suggests: BiocStyle, knitr, nnSVG, rmarkdown, scran, scuttle, STexampleData, escheR, ggpubr, colorspace, BumpyMatrix, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 40b2c680facb6fa89ed78440215e8ffe NeedsCompilation: no Title: Thin plate models to detect spatially variable genes Description: The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis. biocViews: Spatial, Transcriptomics, GeneExpression, Software, StatisticalMethod, DimensionReduction, Regression, Preprocessing Author: Boyi Guo [aut, cre] (ORCID: ), Lukas M. Weber [ctb] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/tpSVG VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/tpSVG/issues git_url: https://git.bioconductor.org/packages/tpSVG git_branch: devel git_last_commit: 3b8b10d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tpSVG_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tpSVG_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tpSVG_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tpSVG_1.3.0.tgz vignettes: vignettes/tpSVG/inst/doc/intro_to_tpSVG.html vignetteTitles: intro_to_tpSVG hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tpSVG/inst/doc/intro_to_tpSVG.R dependencyCount: 85 Package: tracktables Version: 1.41.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) Archs: x64 MD5sum: fa65b43c4730046988c281ac5924dd4e NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: devel git_last_commit: 9970f63 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tracktables_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tracktables_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tracktables_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tracktables_1.41.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 56 Package: trackViewer Version: 1.43.5 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, InteractionSet, utils, rhdf5, strawr, txdbmaker Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown, motifStack License: GPL (>= 2) MD5sum: 435e44e29416a642344719a59af44c36 NeedsCompilation: no Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: devel git_last_commit: 8c80d8b git_last_commit_date: 2024-12-09 Date/Publication: 2024-12-09 source.ver: src/contrib/trackViewer_1.43.5.tar.gz win.binary.ver: bin/windows/contrib/4.5/trackViewer_1.43.5.zip vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html, vignettes/trackViewer/inst/doc/dandelionPlot.html, vignettes/trackViewer/inst/doc/lollipopPlot.html, vignettes/trackViewer/inst/doc/plotInteractionData.html, vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette: change the track styles, trackViewer Vignette: dandelionPlot, trackViewer Vignette: lollipopPlot, trackViewer Vignette: plot interaction data, trackViewer Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R, vignettes/trackViewer/inst/doc/dandelionPlot.R, vignettes/trackViewer/inst/doc/lollipopPlot.R, vignettes/trackViewer/inst/doc/plotInteractionData.R, vignettes/trackViewer/inst/doc/trackViewer.R importsMe: geomeTriD, NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 164 Package: tradeSeq Version: 1.21.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment, DelayedMatrixStats License: MIT + file LICENSE Archs: x64 MD5sum: e5074bfbbe60b343c39343ff6b3b9166 NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: devel git_last_commit: 0de9e8b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tradeSeq_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tradeSeq_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tradeSeq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tradeSeq_1.21.0.tgz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R dependencyCount: 86 Package: TrajectoryGeometry Version: 1.15.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: 6936b5d9f0ee35f642983270762073bb NeedsCompilation: no Title: This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns Description: Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: devel git_last_commit: b33d9f8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TrajectoryGeometry_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TrajectoryGeometry_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryGeometry_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryGeometry_1.15.0.tgz vignettes: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R dependencyCount: 60 Package: TrajectoryUtils Version: 1.15.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 861be6e6a334fd5679cfa6d96b428f2d NeedsCompilation: no Title: Single-Cell Trajectory Analysis Utilities Description: Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. biocViews: GeneExpression, SingleCell Author: Aaron Lun [aut, cre], Kelly Street [aut] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/TrajectoryUtils VignetteBuilder: knitr BugReports: https://github.com/LTLA/TrajectoryUtils/issues git_url: https://git.bioconductor.org/packages/TrajectoryUtils git_branch: devel git_last_commit: 80a5e71 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TrajectoryUtils_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TrajectoryUtils_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryUtils_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryUtils_1.15.0.tgz vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html vignetteTitles: Trajectory utilities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R dependsOnMe: slingshot, TSCAN importsMe: condiments, singleCellTK, tradeSeq dependencyCount: 47 Package: transcriptogramer Version: 1.29.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 4e46733f8c014de67ac385187ae98635 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: devel git_last_commit: c3aae60 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/transcriptogramer_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transcriptogramer_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transcriptogramer_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transcriptogramer_1.29.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 102 Package: transcriptR Version: 1.35.0 Depends: R (>= 3.5.0), methods Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: 9dab2c49e6a659cfa78b558909e3e0dc NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: devel git_last_commit: bd28191 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/transcriptR_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transcriptR_1.35.0.zip vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 151 Package: transformGamPoi Version: 1.13.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 2eed1e011c1d8e9cecdf828726c1dd1b NeedsCompilation: yes Title: Variance Stabilizing Transformation for Gamma-Poisson Models Description: Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals). biocViews: SingleCell, Normalization, Preprocessing, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/transformGamPoi VignetteBuilder: knitr BugReports: https://github.com/const-ae/transformGamPoi/issues git_url: https://git.bioconductor.org/packages/transformGamPoi git_branch: devel git_last_commit: c014302 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/transformGamPoi_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transformGamPoi_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transformGamPoi_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transformGamPoi_1.13.0.tgz vignettes: vignettes/transformGamPoi/inst/doc/transformGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transformGamPoi/inst/doc/transformGamPoi.R dependencyCount: 55 Package: transite Version: 1.25.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: deba913508a7157de83a43cae1f1bb74 NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), Anna Gattinger [aut] (ORCID: ), Michael Yaffe [ths, cph] (ORCID: ), Ian Cannell [ths] (ORCID: ) Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: devel git_last_commit: 9b520b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/transite_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transite_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transite_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transite_1.25.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 62 Package: tRanslatome Version: 1.45.0 Depends: R (>= 2.15.0), methods, limma, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 39c055eef59270538c2d533c8d553e01 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: devel git_last_commit: 7c3a2dc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tRanslatome_1.45.0.tar.gz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 123 Package: transmogR Version: 1.3.0 Depends: Biostrings, GenomicRanges Imports: BSgenome, dplyr, GenomeInfoDb, GenomicFeatures, ggplot2 (>= 3.5.0), IRanges, jsonlite, matrixStats, methods, parallel, rlang, scales, stats, S4Vectors, SummarizedExperiment, VariantAnnotation, vroom Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, ComplexUpset, extraChIPs, InteractionSet, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0) License: GPL-3 MD5sum: d3549484aa22f1f5386f2cf81c82a15d NeedsCompilation: no Title: Modify a set of reference sequences using a set of variants Description: transmogR provides the tools needed to crate a new reference genome or reference transcriptome, using a set of variants. Variants can be any combination of SNPs, Insertions and Deletions. The intended use-case is to enable creation of variant-modified reference transcriptomes for incorporation into transcriptomic pseudo-alignment workflows, such as salmon. biocViews: Alignment, GenomicVariation, Sequencing, TranscriptomeVariant Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/transmogR VignetteBuilder: knitr BugReports: https://github.com/smped/transmogR/issues git_url: https://git.bioconductor.org/packages/transmogR git_branch: devel git_last_commit: 9369e9f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/transmogR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transmogR_1.3.0.zip vignettes: vignettes/transmogR/inst/doc/creating_a_new_reference.html vignetteTitles: Creating a Variant-Modified Reference hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transmogR/inst/doc/creating_a_new_reference.R dependencyCount: 107 Package: transomics2cytoscape Version: 1.17.0 Imports: RCy3, KEGGREST, dplyr, purrr, tibble, pbapply Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 37512f505cb8e6fea4c36e23ebce6871 NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (ORCID: ), Katsuyuki Yugi [aut] (ORCID: ) Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.10.0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: devel git_last_commit: 85fc186 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/transomics2cytoscape_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/transomics2cytoscape_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/transomics2cytoscape_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/transomics2cytoscape_1.17.0.tgz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 70 Package: traseR Version: 1.37.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: f2dafa6660699e3620018bce24e240e3 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: devel git_last_commit: 93e91fe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/traseR_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/traseR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/traseR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/traseR_1.37.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 61 Package: TreeAndLeaf Version: 1.19.0 Depends: R(>= 4.0) Imports: RedeR(>= 1.40.4), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 MD5sum: 8899e853745cc65569301cbc22407019 NeedsCompilation: no Title: Displaying binary trees with focus on dendrogram leaves Description: The TreeAndLeaf package combines unrooted and force-directed graph algorithms in order to layout binary trees, aiming to represent multiple layers of information onto dendrogram leaves. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro Maintainer: Milena A. Cardoso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: devel git_last_commit: cc7657b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TreeAndLeaf_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TreeAndLeaf_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TreeAndLeaf_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TreeAndLeaf_1.19.0.tgz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an graph layout to dendrograms. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R suggestsMe: RedeR dependencyCount: 31 Package: treeclimbR Version: 1.3.0 Depends: R (>= 4.4.0) Imports: TreeSummarizedExperiment (>= 1.99.0), edgeR, methods, SummarizedExperiment, S4Vectors, dirmult, dplyr, tibble, tidyr, ape, diffcyt, ggnewscale, ggplot2 (>= 3.4.0), viridis, ggtree, stats, utils, rlang Suggests: knitr, rmarkdown, scales, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 MD5sum: 590fc43d3ac7bba1f3986ad1d6fb4ae8 NeedsCompilation: no Title: An algorithm to find optimal signal levels in a tree Description: The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one. biocViews: StatisticalMethod, CellBasedAssays Author: Ruizhu Huang [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/treeclimbR VignetteBuilder: knitr BugReports: https://github.com/csoneson/treeclimbR/issues git_url: https://git.bioconductor.org/packages/treeclimbR git_branch: devel git_last_commit: 653ec0b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/treeclimbR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/treeclimbR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treeclimbR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treeclimbR_1.3.0.tgz vignettes: vignettes/treeclimbR/inst/doc/treeclimbR.html vignetteTitles: treeclimbR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeclimbR/inst/doc/treeclimbR.R dependencyCount: 168 Package: treeio Version: 1.31.0 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, stats, tibble, tidytree (>= 0.4.5), utils, yulab.utils (>= 0.1.6) Suggests: Biostrings, cli, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, purrr, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: ded39e57431d05609228dc47c2855950 NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (ORCID: ), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (ORCID: ), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-tree-data/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: devel git_last_commit: 79e207c git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/treeio_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/treeio_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treeio_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treeio_1.31.0.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree, MicrobiotaProcess, TreeSummarizedExperiment, geneplast.data, BioVizSeq, EvoPhylo, RevGadgets, shinyTempSignal suggestsMe: ggtreeDendro, ggtreeExtra, rfaRm, FossilSim, idiogramFISH, MetaNet, nosoi dependencyCount: 39 Package: treekoR Version: 1.15.0 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 MD5sum: e39f993b54aedb38257c56c11043a474 NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations Description: treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results. biocViews: Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization Author: Adam Chan [aut, cre], Ellis Patrick [ctb] Maintainer: Adam Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: devel git_last_commit: f14c924 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/treekoR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/treekoR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/treekoR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/treekoR_1.15.0.tgz vignettes: vignettes/treekoR/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treekoR/inst/doc/vignette.R importsMe: Statial suggestsMe: spicyWorkflow dependencyCount: 180 Package: TreeSummarizedExperiment Version: 2.15.0 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18), Biostrings Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment, BiocParallel, IRanges, treeio Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: a2fffb1e338af26fbb0e9a0f1e46a6bb NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (ORCID: ), Felix G.M. Ernst [ctb] (ORCID: ) Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: devel git_last_commit: 20c5a8f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TreeSummarizedExperiment_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TreeSummarizedExperiment_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TreeSummarizedExperiment_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TreeSummarizedExperiment_2.15.0.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R dependsOnMe: ExperimentSubset, HoloFoodR, MGnifyR, mia, miaSim, miaViz, curatedMetagenomicData, MicrobiomeBenchmarkData, microbiomeDataSets importsMe: iSEEtree, microSTASIS, PLSDAbatch, treeclimbR suggestsMe: ANCOMBC, philr, LegATo, file2meco, parafac4microbiome dependencyCount: 76 Package: TREG Version: 1.11.0 Depends: R (>= 4.2), SummarizedExperiment Imports: Matrix, purrr, rafalib Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap, sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0), tibble, tidyr, SingleCellExperiment License: Artistic-2.0 MD5sum: bf6d790a2f7175356f180b0126664d8b NeedsCompilation: no Title: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Description: RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, Transcription, Sequencing Author: Louise Huuki-Myers [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/TREG, http://research.libd.org/TREG/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/TREG git_url: https://git.bioconductor.org/packages/TREG git_branch: devel git_last_commit: 2c1d897 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TREG_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TREG_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TREG_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TREG_1.11.0.tgz vignettes: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html vignetteTitles: How to find Total RNA Expression Genes (TREGs) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R dependencyCount: 46 Package: Trendy Version: 1.29.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: 257d938733f1bfdb35775061a4fa0160 NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: devel git_last_commit: e2f06d3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Trendy_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Trendy_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Trendy_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Trendy_1.29.0.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 99 Package: TRESS Version: 1.13.0 Depends: R (>= 4.1.0), parallel, S4Vectors Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi Suggests: knitr, rmarkdown,BiocStyle License: GPL-3 + file LICENSE MD5sum: 4aab0f8c710dd33d1f13c335cbe75b43 NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions. biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: devel git_last_commit: a40c256 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TRESS_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TRESS_1.13.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: magpie dependencyCount: 78 Package: tricycle Version: 1.15.0 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater, GenomicRanges, IRanges, S4Vectors, scattermore, dplyr, RColorBrewer, grDevices, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats, cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 956ec285d4cf7351db8410fb0deefb0b NeedsCompilation: no Title: tricycle: Transferable Representation and Inference of cell cycle Description: The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference. biocViews: SingleCell, Software, Transcriptomics, RNASeq, Transcription, BiologicalQuestion, DimensionReduction, ImmunoOncology Author: Shijie Zheng [aut, cre] Maintainer: Shijie Zheng URL: https://github.com/hansenlab/tricycle VignetteBuilder: knitr BugReports: https://github.com/hansenlab/tricycle/issues git_url: https://git.bioconductor.org/packages/tricycle git_branch: devel git_last_commit: f801f57 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tricycle_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tricycle_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tricycle_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tricycle_1.15.0.tgz vignettes: vignettes/tricycle/inst/doc/tricycle.html vignetteTitles: tricycle: Transferable Representation and Inference of Cell Cycle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tricycle/inst/doc/tricycle.R dependencyCount: 126 Package: trigger Version: 1.53.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 81d8b5dcc872c3fc9ea73b3f22b8c546 NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: devel git_last_commit: 4da3165 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/trigger_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/trigger_1.53.0.zip vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R dependencyCount: 98 Package: trio Version: 3.45.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: eb07bd4adc28a307db8dc9fa771c857a NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: devel git_last_commit: 68d3fd9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/trio_3.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/trio_3.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/trio_3.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/trio_3.45.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 19 Package: triplex Version: 1.47.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE MD5sum: 1ffb2b76980d2326292913d628ab1f06 NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: devel git_last_commit: cc51819 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/triplex_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/triplex_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/triplex_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/triplex_1.47.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 28 Package: tripr Version: 1.13.0 Depends: shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices, graphics, stats, utils, vegan Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager, Biostrings, xtable, rlist, motifStack, knitr, rmarkdown, testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis, pryr Enhances: parallel License: MIT + file LICENSE MD5sum: f548589ab9bb93b86346363c236bce31 NeedsCompilation: no Title: T-cell Receptor/Immunoglobulin Profiler (TRIP) Description: TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly. biocViews: BatchEffect, MultipleComparison, GeneExpression, ImmunoOncology, TargetedResequencing Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos Pechlivanis [cre], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [aut], Aspasia Orfanou [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/tripr VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues git_url: https://git.bioconductor.org/packages/tripr git_branch: devel git_last_commit: 138f3c9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tripr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tripr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tripr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tripr_1.13.0.tgz vignettes: vignettes/tripr/inst/doc/tripr_guide.html vignetteTitles: tripr User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tripr/inst/doc/tripr_guide.R dependencyCount: 103 Package: tRNA Version: 1.25.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: f9018a13693262425de16f80242e4535 NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] (ORCID: ) Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: devel git_last_commit: 41b4ba9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tRNA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRNA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRNA_1.25.0.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 60 Package: tRNAdbImport Version: 1.25.0 Depends: R (>= 3.6), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, stringr, httr2, xml2, S4Vectors, methods, IRanges, utils Suggests: BiocGenerics, knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: 40097ce34d60a04af468d70d8af48d31 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: devel git_last_commit: 6e54ea5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tRNAdbImport_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNAdbImport_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tRNAdbImport_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tRNAdbImport_1.25.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 64 Package: tRNAscanImport Version: 1.27.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: b73f42b07666e33324a70b30fb1b58ad NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: devel git_last_commit: 5910f73 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tRNAscanImport_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tRNAscanImport_1.27.0.zip vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 91 Package: TRONCO Version: 2.39.0 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways, magick License: GPL-3 MD5sum: de178955e77754ac3fc6ff9f50a26900 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut], Luca De Sano [cre, aut] (ORCID: ), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] (ORCID: ) Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: devel git_last_commit: 61d87f7 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TRONCO_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TRONCO_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TRONCO_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TRONCO_2.39.0.tgz vignettes: vignettes/TRONCO/inst/doc/f1_introduction.html, vignettes/TRONCO/inst/doc/f2_loading_data.html, vignettes/TRONCO/inst/doc/f3_data_visualization.html, vignettes/TRONCO/inst/doc/f4_data_manipulation.html, vignettes/TRONCO/inst/doc/f5_model_inference.html, vignettes/TRONCO/inst/doc/f6_post_reconstruction.html, vignettes/TRONCO/inst/doc/f7_import_export.html vignetteTitles: f1_introduction.html, Loading data, Data visualization, Data manipulation, Model inference, Post reconstruction, Import/export from other tools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/f1_introduction.R, vignettes/TRONCO/inst/doc/f2_loading_data.R, vignettes/TRONCO/inst/doc/f3_data_visualization.R, vignettes/TRONCO/inst/doc/f4_data_manipulation.R, vignettes/TRONCO/inst/doc/f5_model_inference.R, vignettes/TRONCO/inst/doc/f6_post_reconstruction.R, vignettes/TRONCO/inst/doc/f7_import_export.R dependencyCount: 48 Package: TSAR Version: 1.5.0 Depends: R (>= 4.3.0) Imports: dplyr (>= 1.0.7), ggplot2 (>= 3.3.5), ggpubr (>= 0.4.0), magrittr (>= 2.0.3), mgcv (>= 1.8.38), readxl (>= 1.4.0), stringr (>= 1.4.0), tidyr (>= 1.1.4), utils (>= 4.3.1), shiny (>= 1.7.4.1), plotly (>= 4.10.2), shinyjs (>= 2.1.0), jsonlite (>= 1.8.7), rhandsontable (>= 0.3.8), openxlsx (>= 4.2.5.2), shinyWidgets (>= 0.7.6), minpack.lm (>= 1.2.3) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 MD5sum: 0157e88a25bb052ffcb1dbbe0a174709 NeedsCompilation: no Title: Thermal Shift Analysis in R Description: This package automates analysis workflow for Thermal Shift Analysis (TSA) data. Processing, analyzing, and visualizing data through both shiny applications and command lines. Package aims to simplify data analysis and offer front to end workflow, from raw data to multiple trial analysis. biocViews: Software, ShinyApps, Visualization, qPCR Author: Xinlin Gao [aut, cre] (ORCID: ), William M. McFadden [aut, fnd] (ORCID: ), Stefan G. Sarafianos [fnd, aut, ths] (ORCID: ) Maintainer: Xinlin Gao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSAR git_branch: devel git_last_commit: c609932 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TSAR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TSAR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TSAR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TSAR_1.5.0.tgz vignettes: vignettes/TSAR/inst/doc/FAQ_assistance.html, vignettes/TSAR/inst/doc/TSAR_Package_Structure.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.html vignetteTitles: Frequently Asked Questions, TSAR Package Structure, TSAR Workflow by Command, TSAR Workflow by Shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSAR/inst/doc/FAQ_assistance.R, vignettes/TSAR/inst/doc/TSAR_Package_Structure.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.R dependencyCount: 128 Package: TSCAN Version: 1.45.0 Depends: R (>= 4.4.0), SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, SparseArray (>= 1.5.23), DelayedArray (>= 0.31.9), S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 69e623ecb162c79af3f6db86f06c06e6 NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: devel git_last_commit: a82164a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TSCAN_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TSCAN_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TSCAN_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TSCAN_1.45.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R importsMe: FEAST, singleCellTK, DIscBIO suggestsMe: condiments dependencyCount: 96 Package: ttgsea Version: 1.15.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 285055aa72511e4102029ff531df9b33 NeedsCompilation: no Title: Tokenizing Text of Gene Set Enrichment Analysis Description: Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: devel git_last_commit: 603fe2d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ttgsea_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ttgsea_1.15.0.tgz vignettes: vignettes/ttgsea/inst/doc/ttgsea.html vignetteTitles: ttgsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R importsMe: DeepPINCS, GenProSeq dependencyCount: 125 Package: TTMap Version: 1.29.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: 22daaf962871e1d7b01f88eb66c398ce NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: devel git_last_commit: f7d844b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TTMap_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TTMap_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TTMap_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TTMap_1.29.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 64 Package: TurboNorm Version: 1.55.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata, hgu95av2cdf License: LGPL Archs: x64 MD5sum: 065d87e0c94d41b691e44e10d5290e03 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: devel git_last_commit: 0c41566 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TurboNorm_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/TurboNorm_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TurboNorm_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/TurboNorm_1.55.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 19 Package: TVTB Version: 1.33.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 567ae04c945bc0d7d6599c24750e5a24 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: devel git_last_commit: 9b5085b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/TVTB_1.33.0.tar.gz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 165 Package: tweeDEseq Version: 1.53.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 1.0.10), MASS, limma, edgeR, parallel, cqn, grDevices, graphics, stats, utils LinkingTo: Rcpp Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: x64 MD5sum: 1021653b4898869fac7e47200e22c466 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq, DNASeq Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Mikel Esnaola [aut], Robert Castelo [aut] Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/tweeDEseq/ BugReports: https://github.com/isglobal-brge/tweeDEseq/issues git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: devel git_last_commit: 3a20c3b git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tweeDEseq_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tweeDEseq_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tweeDEseq_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tweeDEseq_1.53.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 23 Package: twilight Version: 1.83.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) MD5sum: 4f1329caa618200a7b003f235dedad92 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: devel git_last_commit: 98cd9b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/twilight_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/twilight_1.83.0.zip vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 9 Package: twoddpcr Version: 1.31.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 0e58a5c41d8f3c6db83a3b5260c39f48 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: devel git_last_commit: c2e2c95 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/twoddpcr_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/twoddpcr_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/twoddpcr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/twoddpcr_1.31.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 65 Package: txcutr Version: 1.13.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, GenomicFeatures, txdbmaker, IRanges, GenomicRanges, BiocGenerics, Biostrings, S4Vectors, rtracklayer, BiocParallel, stats, methods, utils Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown, testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 Archs: x64 MD5sum: ded857485f2031fd3f4d4852163161f3 NeedsCompilation: no Title: Transcriptome CUTteR Description: Various mRNA sequencing library preparation methods generate sequencing reads specifically from the transcript ends. Analyses that focus on quantification of isoform usage from such data can be aided by using truncated versions of transcriptome annotations, both at the alignment or pseudo-alignment stage, as well as in downstream analysis. This package implements some convenience methods for readily generating such truncated annotations and their corresponding sequences. biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics Author: Mervin Fansler [aut, cre] (ORCID: ) Maintainer: Mervin Fansler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/txcutr git_branch: devel git_last_commit: 68a3c9e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/txcutr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/txcutr_1.13.0.zip vignettes: vignettes/txcutr/inst/doc/intro.html vignetteTitles: Introduction to txcutr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txcutr/inst/doc/intro.R dependencyCount: 102 Package: txdbmaker Version: 1.3.1 Depends: BiocGenerics, S4Vectors, GenomeInfoDb (>= 1.39.9), GenomicRanges, GenomicFeatures Imports: methods, utils, stats, tools, httr, rjson, DBI, RSQLite (>= 2.0), IRanges, UCSC.utils, AnnotationDbi, Biobase, BiocIO, rtracklayer, biomaRt (>= 2.59.1) Suggests: RMariaDB, mirbase.db, ensembldb, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 223bba60d510b0f57d6a1c8fb761bb18 NeedsCompilation: no Title: Tools for making TxDb objects from genomic annotations Description: A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. biocViews: Infrastructure, DataImport, Annotation, GenomeAnnotation, GenomeAssembly, Genetics, Sequencing Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], R. Castelo [ctb], M. Lawrence [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], L. Shepherd [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/txdbmaker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/txdbmaker/issues git_url: https://git.bioconductor.org/packages/txdbmaker git_branch: devel git_last_commit: 05d278f git_last_commit_date: 2024-11-21 Date/Publication: 2024-11-22 source.ver: src/contrib/txdbmaker_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/txdbmaker_1.3.1.zip vignettes: vignettes/txdbmaker/inst/doc/txdbmaker.html vignetteTitles: Making TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txdbmaker/inst/doc/txdbmaker.R dependsOnMe: mygene importsMe: ASpli, BgeeCall, crisprDesign, crisprViz, customProDB, DegNorm, EpiTxDb, FLAMES, GenomicPlot, metaseqR2, ORFik, OrganismDbi, OUTRIDER, proActiv, proBAMr, QuasR, RCAS, recoup, Rhisat2, RiboDiPA, ribosomeProfilingQC, RNAmodR, scanMiRApp, scruff, sitadela, trackViewer, txcutr, tximeta, GenomicState, geneLenDataBase suggestsMe: AnnotationHub, BindingSiteFinder, bumphunter, BUSpaRse, doubletrouble, eisaR, GenomicFeatures, GenomicRanges, raer, recount, SplicingGraphs, systemPipeR dependencyCount: 101 Package: tximeta Version: 1.25.0 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, GenomicFeatures, txdbmaker, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, fishpond, edgeR, limma, devtools License: GPL-2 MD5sum: 0da89c8c21edf03498b37a28f63c5f77 NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: devel git_last_commit: eab5dbe git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tximeta_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tximeta_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tximeta_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tximeta_1.25.0.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: Transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R dependsOnMe: rnaseqGene suggestsMe: DESeq2, fishpond, fluentGenomics dependencyCount: 110 Package: tximport Version: 1.35.0 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), arrow, limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, eds License: LGPL (>=2) MD5sum: 85f907c97443d37fbee475de8467a384 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/thelovelab/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: devel git_last_commit: 6275b97 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/tximport_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/tximport_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/tximport_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/tximport_1.35.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, CleanUpRNAseq, DifferentialRegulation, EventPointer, singleCellTK, TDbasedUFE, tximeta, ExpHunterSuite, seeker suggestsMe: BANDITS, DESeq2, variancePartition dependencyCount: 3 Package: UCell Version: 2.11.1 Depends: R(>= 4.3.0) Imports: methods, data.table(>= 1.13.6), Matrix, stats, BiocParallel, BiocNeighbors, SingleCellExperiment, SummarizedExperiment Suggests: scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle, Seurat(>= 5.0.0), SeuratObject(>= 5.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 00919e6937bbc94a355145cd55a79592 NeedsCompilation: no Title: Rank-based signature enrichment analysis for single-cell data Description: UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, GeneExpression, CellBasedAssays Author: Massimo Andreatta [aut, cre] (ORCID: ), Santiago Carmona [aut] (ORCID: ) Maintainer: Massimo Andreatta URL: https://github.com/carmonalab/UCell VignetteBuilder: knitr BugReports: https://github.com/carmonalab/UCell/issues git_url: https://git.bioconductor.org/packages/UCell git_branch: devel git_last_commit: 10bccc9 git_last_commit_date: 2024-10-31 Date/Publication: 2024-11-01 source.ver: src/contrib/UCell_2.11.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/UCell_2.11.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UCell_2.11.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UCell_2.11.1.tgz vignettes: vignettes/UCell/inst/doc/UCell_sce.html, vignettes/UCell/inst/doc/UCell_Seurat.html, vignettes/UCell/inst/doc/UCell_vignette_basic.html vignetteTitles: 2. Using UCell with SingleCellExperiment, 3. Using UCell with Seurat, 1. Gene signature scoring with UCell hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UCell/inst/doc/UCell_sce.R, vignettes/UCell/inst/doc/UCell_Seurat.R, vignettes/UCell/inst/doc/UCell_vignette_basic.R importsMe: escape, scGate suggestsMe: SCpubr dependencyCount: 52 Package: UCSC.utils Version: 1.3.0 Imports: methods, stats, httr, jsonlite, S4Vectors Suggests: DBI, RMariaDB, GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b89a3ffa673a703bb546dda8e8c99440 NeedsCompilation: no Title: Low-level utilities to retrieve data from the UCSC Genome Browser Description: A set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker. biocViews: Infrastructure, GenomeAssembly, Annotation, GenomeAnnotation, DataImport Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/UCSC.utils VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UCSC.utils/issues git_url: https://git.bioconductor.org/packages/UCSC.utils git_branch: devel git_last_commit: 1fe2970 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/UCSC.utils_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UCSC.utils_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UCSC.utils_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UCSC.utils_1.3.0.tgz vignettes: vignettes/UCSC.utils/inst/doc/UCSC.utils.html vignetteTitles: The UCSC.utils package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UCSC.utils/inst/doc/UCSC.utils.R importsMe: GenomeInfoDb, txdbmaker dependencyCount: 17 Package: Ularcirc Version: 1.25.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments, Organism.dplyr, plotgardener, R.utils, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: a21cc2804ddf67d21f644ef8b054b030 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: devel git_last_commit: a6da876 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Ularcirc_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Ularcirc_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Ularcirc_1.25.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 161 Package: UMI4Cats Version: 1.17.0 Depends: R (>= 4.0.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate, TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: 363f3d7ea72feba68401a1806672140f NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (ORCID: ), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: devel git_last_commit: 7016307 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/UMI4Cats_1.17.0.tar.gz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 151 Package: uncoverappLib Version: 1.17.0 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: bbf6da69aab77d8b40a14546c19c01ff NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: devel git_last_commit: cfd7ff6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/uncoverappLib_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/uncoverappLib_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/uncoverappLib_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/uncoverappLib_1.17.0.tgz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 189 Package: UNDO Version: 1.49.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 51def95b5d9fb9dd21c5f24b3602a3d4 NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: devel git_last_commit: 0f29ad6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/UNDO_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UNDO_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/UNDO_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/UNDO_1.49.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 11 Package: unifiedWMWqPCR Version: 1.43.0 Depends: methods Imports: BiocGenerics, limma, stats, graphics License: GPL (>=2) MD5sum: fd5fd733afba1cba93dc4704e3b27bcd NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: devel git_last_commit: f2c0c05 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/unifiedWMWqPCR_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/unifiedWMWqPCR_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/unifiedWMWqPCR_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/unifiedWMWqPCR_1.43.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 9 Package: UniProt.ws Version: 2.47.1 Depends: BiocGenerics, methods, RSQLite, utils Imports: AnnotationDbi, BiocFileCache, BiocBaseUtils, httr, httpcache, jsonlite, progress, rjsoncons Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 67e5612afdd5c9584b9f05a8d872ebc4 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. This package provides a collection of functions for retrieving, processing, and re-packaging UniProt web services. The package makes use of UniProt's modernized REST API and allows mapping of identifiers accross different databases. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/UniProt.ws VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UniProt.ws/issues git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: devel git_last_commit: 2a8c6ae git_last_commit_date: 2024-11-05 Date/Publication: 2024-11-06 source.ver: src/contrib/UniProt.ws_2.47.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/UniProt.ws_2.47.1.zip vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.html vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo, drugTargetInteractions, ginmappeR, immunogenViewer suggestsMe: autonomics, cleaver, qPLEXanalyzer dependencyCount: 68 Package: Uniquorn Version: 2.27.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation, data.table Suggests: testthat, knitr, rmarkdown, BiocGenerics License: Artistic-2.0 MD5sum: fc77181b6bbeb6753d3486b5803d4a68 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: 'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: devel git_last_commit: a1c9b41 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Uniquorn_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Uniquorn_2.27.0.zip vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 91 Package: universalmotif Version: 1.25.1 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid, MatrixGenerics LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio Enhances: PWMEnrich, rGADEM License: GPL-3 MD5sum: e91fcc1ca23a5da6e3711417d7e1799d NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (ORCID: ), Spencer Nystrom [ctb] (ORCID: ) Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: devel git_last_commit: 8fc0c9e git_last_commit_date: 2024-11-11 Date/Publication: 2024-11-11 source.ver: src/contrib/universalmotif_1.25.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/universalmotif_1.25.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/universalmotif_1.25.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/universalmotif_1.25.1.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: ChIPpeakAnno, circRNAprofiler, memes, MotifPeeker, motifTestR dependencyCount: 60 Package: updateObject Version: 1.11.0 Depends: R (>= 4.2.0), methods, BiocGenerics (>= 0.51.1), S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, SingleCellExperiment, MultiAssayExperiment, BiSeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 6312bf740fd11a9b1637621d273556c6 NeedsCompilation: no Title: Find/fix old serialized S4 instances Description: A set of tools built around updateObject() to work with old serialized S4 instances. The package is primarily useful to package maintainers who want to update the serialized S4 instances included in their package. This is still work-in-progress. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues git_url: https://git.bioconductor.org/packages/updateObject git_branch: devel git_last_commit: 5a18c6d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/updateObject_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/updateObject_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/updateObject_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/updateObject_1.11.0.tgz vignettes: vignettes/updateObject/inst/doc/updateObject.html vignetteTitles: A quick introduction to the updateObject package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/updateObject/inst/doc/updateObject.R dependencyCount: 9 Package: UPDhmm Version: 1.3.0 Depends: R (>= 4.3.0) Imports: HMM, utils, VariantAnnotation, GenomicRanges, S4Vectors, IRanges, stats Suggests: knitr, testthat (>= 2.1.0), BiocStyle, rmarkdown, markdown, karyoploteR, regioneR, dplyr License: MIT + file LICENSE MD5sum: 9db56f22f707cda6ebc9ba5bec3950f6 NeedsCompilation: no Title: Detecting Uniparental Disomy through NGS trio data Description: Uniparental disomy (UPD) is a genetic condition where an individual inherits both copies of a chromosome or part of it from one parent, rather than one copy from each parent. This package contains a HMM for detecting UPDs through HTS (High Throughput Sequencing) data from trio assays. By analyzing the genotypes in the trio, the model infers a hidden state (normal, father isodisomy, mother isodisomy, father heterodisomy and mother heterodisomy). biocViews: Software, HiddenMarkovModel, Genetics Author: Marta Sevilla [aut, cre] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ) Maintainer: Marta Sevilla URL: https://github.com/martasevilla/UPDhmm VignetteBuilder: knitr BugReports: https://github.com/martasevilla/UPDhmm/issues git_url: https://git.bioconductor.org/packages/UPDhmm git_branch: devel git_last_commit: 025c593 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/UPDhmm_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/UPDhmm_1.3.0.zip vignettes: vignettes/UPDhmm/inst/doc/UPDhmm.html vignetteTitles: Detection of UPDs in HTS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UPDhmm/inst/doc/UPDhmm.R dependencyCount: 81 Package: uSORT Version: 1.33.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: e6b73fdd0201a28b925b12332c02d174 NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: devel git_last_commit: 9c366f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/uSORT_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/uSORT_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/uSORT_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/uSORT_1.33.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 96 Package: VAExprs Version: 1.13.0 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: cc4fe1e1499b1399710a207769e0e756 NeedsCompilation: no Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones. biocViews: Software, GeneExpression, SingleCell Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: devel git_last_commit: ed47049 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VAExprs_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VAExprs_1.13.0.tgz vignettes: vignettes/VAExprs/inst/doc/VAExprs.html vignetteTitles: VAExprs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R suggestsMe: GenProSeq dependencyCount: 211 Package: VanillaICE Version: 1.69.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 584557cfbe222533f7377edfbe9cae2d NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: devel git_last_commit: 6af9e70 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VanillaICE_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VanillaICE_1.69.0.zip vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 96 Package: VarCon Version: 1.15.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 8c82e6389f456bafaf40468f4d511800 NeedsCompilation: no Title: VarCon: an R package for retrieving neighboring nucleotides of an SNV Description: VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] Maintainer: Johannes Ptok VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: devel git_last_commit: b4f3a14 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VarCon_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VarCon_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VarCon_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VarCon_1.15.0.tgz vignettes: vignettes/VarCon/inst/doc/VarCon.html vignetteTitles: Analysing SNVs with VarCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VarCon/inst/doc/VarCon.R dependencyCount: 108 Package: variancePartition Version: 1.37.1 Depends: R (>= 4.3.0), ggplot2, limma, BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix (>= 1.4.0), iterators, gplots, corpcor, matrixStats, RhpcBLASctl, reshape2, remaCor (>= 0.0.15), fANCOVA, aod, scales, Rdpack, rlang, lme4 (>= 1.1.33), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, cowplot, Rfast, zenith, statmod, BiocGenerics, r2glmm, readr License: GPL-2 MD5sum: b6e5a21c596e61c6f174e1afc11ac56d NeedsCompilation: no Title: Quantify and interpret drivers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel E. Hoffman URL: http://bioconductor.org/packages/variancePartition, https://DiseaseNeuroGenomics.github.io/variancePartition VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: devel git_last_commit: f3d7711 git_last_commit_date: 2024-11-07 Date/Publication: 2024-11-12 source.ver: src/contrib/variancePartition_1.37.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/variancePartition_1.37.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/variancePartition_1.37.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/variancePartition_1.37.1.tgz vignettes: vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/errors.html, vignettes/variancePartition/inst/doc/FAQ.html, vignettes/variancePartition/inst/doc/mvtests.html, vignettes/variancePartition/inst/doc/rnd_effects.html, vignettes/variancePartition/inst/doc/variancePartition.html vignetteTitles: 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Error handling, 6) Frequently asked questions, 7) Multivariate tests, 3) Theory and practice of random effects and REML, 1) Variance partitioning analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/errors.R, vignettes/variancePartition/inst/doc/FAQ.R, vignettes/variancePartition/inst/doc/mvtests.R, vignettes/variancePartition/inst/doc/rnd_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R dependsOnMe: dreamlet importsMe: muscat, zenith suggestsMe: GRaNIE dependencyCount: 94 Package: VariantAnnotation Version: 1.53.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 2.19.1) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 2.99.0) Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle, knitr, magick, jsonlite, httr, rjsoncons License: Artistic-2.0 MD5sum: 7c457fa8802309f88dea2b4dace263d4 NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: devel git_last_commit: 478ea77 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VariantAnnotation_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantAnnotation_1.53.0.zip vignettes: vignettes/VariantAnnotation/inst/doc/ensemblVEP.html, vignettes/VariantAnnotation/inst/doc/filterVcf.html, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.html vignetteTitles: ensemblVEP: using the REST API with Bioconductor, 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/ensemblVEP.R, vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: alabaster.vcf, CNVrd2, deepSNV, demuxSNP, HelloRanges, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector, PlasmaMutationDetector2 importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, cardelino, CNVfilteR, CopyNumberPlots, crisprDesign, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, fcScan, GA4GHclient, GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, katdetectr, lineagespot, MADSEQ, motifbreakR, MungeSumstats, musicatk, MutationalPatterns, ProteoDisco, RAIDS, scoreInvHap, SigsPack, SNPhood, svaRetro, tadar, TitanCNA, tLOH, transmogR, TVTB, Uniquorn, UPDhmm, VCFArray, YAPSA, ZygosityPredictor, COSMIC.67, gpcp suggestsMe: alabaster.files, AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, epialleleR, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, igvShiny, ldblock, omicsPrint, podkat, RVS, SeqArray, shiny.gosling, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, ldsep, polyRAD, SNPassoc, updog dependencyCount: 79 Package: VariantExperiment Version: 1.21.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown, BiocStyle License: GPL-3 MD5sum: c2afba06a82dfd830eda984ddca97509 NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: devel git_last_commit: f3af19f git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VariantExperiment_1.21.0.tar.gz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html vignetteTitles: VariantExperiment-class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R dependencyCount: 44 Package: VariantFiltering Version: 1.43.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 MD5sum: 04b204854a60b51e6d8ba22b2ae113d4 NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: devel git_last_commit: 779427d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VariantFiltering_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantFiltering_1.43.0.zip vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 179 Package: VariantTools Version: 1.49.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3), TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: Artistic-2.0 MD5sum: 04fc8c9a302c888dc3e55a4d7f376ba2 NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: devel git_last_commit: e6dbe11 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VariantTools_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VariantTools_1.49.0.zip vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R suggestsMe: VariantToolsData dependencyCount: 80 Package: VaSP Version: 1.19.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 076a3408200d9eb08ebe6bd2ad090678 NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.) biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (ORCID: ), Qian Du [aut] (ORCID: ), Chi Zhang [aut] (ORCID: ) Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues git_url: https://git.bioconductor.org/packages/VaSP git_branch: devel git_last_commit: f2e104d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VaSP_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VaSP_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VaSP_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VaSP_1.19.0.tgz vignettes: vignettes/VaSP/inst/doc/VaSP.html vignetteTitles: user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VaSP/inst/doc/VaSP.R dependencyCount: 92 Package: vbmp Version: 1.75.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: ffe0330eb972e06eb3bdcabd794ccfa0 NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: devel git_last_commit: 8c41b83 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vbmp_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vbmp_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vbmp_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vbmp_1.75.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.23.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 02fd6a1339ba27cde9b5835870f372e9 NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: devel git_last_commit: 2926f6d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VCFArray_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VCFArray_1.23.0.zip vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 81 Package: VDJdive Version: 1.9.0 Depends: R (>= 4.2) Imports: BiocParallel, cowplot, ggplot2, gridExtra, IRanges, Matrix, methods, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, utils LinkingTo: Rcpp Suggests: breakaway, covr, knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 401eda6efe28d95a363d7c15d6eccc77 NeedsCompilation: yes Title: Analysis Tools for 10X V(D)J Data Description: This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification. biocViews: Software, ImmunoOncology, SingleCell, Annotation, RNASeq, TargetedResequencing Author: Kelly Street [aut, cre] (ORCID: ), Mercedeh Movassagh [aut] (ORCID: ), Jill Lundell [aut] (ORCID: ), Jared Brown [ctb], Linglin Huang [ctb], Mingzhi Ye [ctb] Maintainer: Kelly Street URL: https://github.com/kstreet13/VDJdive VignetteBuilder: knitr BugReports: https://github.com/kstreet13/VDJdive/issues git_url: https://git.bioconductor.org/packages/VDJdive git_branch: devel git_last_commit: 9a1d595 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VDJdive_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VDJdive_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VDJdive_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VDJdive_1.9.0.tgz vignettes: vignettes/VDJdive/inst/doc/workflow.html vignetteTitles: VDJdive Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VDJdive/inst/doc/workflow.R dependencyCount: 77 Package: VegaMC Version: 3.45.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 Archs: x64 MD5sum: 8c24e7a935bb9c5e41a602053efcded8 NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: devel git_last_commit: 77ecd7a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VegaMC_3.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VegaMC_3.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VegaMC_3.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VegaMC_3.45.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 69 Package: velociraptor Version: 1.17.0 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran, scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot, GGally, patchwork, metR License: MIT + file LICENSE MD5sum: b283ee3a3111445827b14b4d801cdf54 NeedsCompilation: no Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Aaron Lun [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues git_url: https://git.bioconductor.org/packages/velociraptor git_branch: devel git_last_commit: d1c9824 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/velociraptor_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/velociraptor_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/velociraptor_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/velociraptor_1.17.0.tgz vignettes: vignettes/velociraptor/inst/doc/velociraptor.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R dependencyCount: 70 Package: veloviz Version: 1.13.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 31260a7b7152c5b26da7f9500e08efe2 NeedsCompilation: yes Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories Description: VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories. biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing, RNASeq, DimensionReduction Author: Lyla Atta [aut, cre] (ORCID: ), Jean Fan [aut] (ORCID: ), Arpan Sahoo [aut] (ORCID: ) Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: devel git_last_commit: 53cb575 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/veloviz_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/veloviz_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/veloviz_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/veloviz_1.13.0.tgz vignettes: vignettes/veloviz/inst/doc/vignette.html vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using VeloViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/veloviz/inst/doc/vignette.R dependencyCount: 23 Package: VennDetail Version: 1.23.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat, markdown License: GPL-2 MD5sum: 742a0b6661552f1d118929c590b1bfad NeedsCompilation: no Title: A package for visualization and extract details Description: A set of functions to generate high-resolution Venn,Vennpie plot,extract and combine details of these subsets with user datasets in data frame is available. biocViews: DataRepresentation,GraphAndNetwork Author: Kai Guo, Brett McGregor Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: devel git_last_commit: cecf8f1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VennDetail_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VennDetail_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VennDetail_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VennDetail_1.23.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 48 Package: VERSO Version: 1.17.0 Depends: R (>= 4.1.0) Imports: utils, data.tree, ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 7448adb9f4031da3fabf898c061ca7fc NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ) Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: devel git_last_commit: bd1b576 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/VERSO_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VERSO_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VERSO_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VERSO_1.17.0.tgz vignettes: vignettes/VERSO/inst/doc/v1_introduction.html, vignettes/VERSO/inst/doc/v2_running_VERSO.html vignetteTitles: Introduction, Running VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/v1_introduction.R, vignettes/VERSO/inst/doc/v2_running_VERSO.R dependencyCount: 21 Package: vidger Version: 1.27.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: f9c2831a0bd8ba1511c7cb6677457e55 NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: devel git_last_commit: 030cd02 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vidger_1.27.0.tar.gz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 114 Package: viper Version: 1.41.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: f1b5ae441ebec13d64676e7a8ab978a6 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: devel git_last_commit: 6d8ed69 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/viper_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/viper_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/viper_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/viper_1.41.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan, aracne.networks importsMe: diggit, RTN, diggitdata suggestsMe: decoupleR, easier, MethReg, MOMA, dorothea, vulcandata dependencyCount: 89 Package: ViSEAGO Version: 1.21.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt, dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim, ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph, methods, plotly, processx, topGO, RColorBrewer, R.utils, scales, stats, UpSetR, utils Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager License: GPL-3 bioconductor.org MD5sum: 26174710a2e1fef209177bdd484f8e55 NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: devel git_last_commit: 7633da9 git_last_commit_date: 2024-12-02 Date/Publication: 2024-12-02 source.ver: src/contrib/ViSEAGO_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ViSEAGO_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/ViSEAGO_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/ViSEAGO_1.21.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 170 Package: VisiumIO Version: 1.3.2 Depends: R (>= 4.4.0), TENxIO Imports: BiocBaseUtils, BiocGenerics, BiocIO (>= 1.15.1), jsonlite, methods, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment Suggests: arrow, BiocStyle, data.table, knitr, readr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: dc6abc6296134b3d337695e833775760 NeedsCompilation: no Title: Import Visium data from the 10X Space Ranger pipeline Description: The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects. biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial Author: Marcel Ramos [aut, cre] (ORCID: ), Dario Righelli [aut, ctb], Helena Crowell [aut, ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/VisiumIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/VisiumIO/issues git_url: https://git.bioconductor.org/packages/VisiumIO git_branch: devel git_last_commit: 3588697 git_last_commit_date: 2024-12-05 Date/Publication: 2024-12-06 source.ver: src/contrib/VisiumIO_1.3.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/VisiumIO_1.3.2.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VisiumIO_1.3.2.tgz vignettes: vignettes/VisiumIO/inst/doc/VisiumIO.html vignetteTitles: VisiumIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VisiumIO/inst/doc/VisiumIO.R dependencyCount: 92 Package: visiumStitched Version: 0.99.15 Depends: R (>= 4.4), SpatialExperiment Imports: BiocBaseUtils, BiocGenerics, dplyr, DropletUtils, grDevices, imager, Matrix, methods, pkgcond, readr, rjson, S4Vectors, SingleCellExperiment, spatialLIBD (>= 1.17.8), stringr, SummarizedExperiment, tibble, tidyr, xml2 Suggests: BiocFileCache, BiocStyle, ggplot2, knitr, RefManageR, rmarkdown, sessioninfo, Seurat, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 6e7bd087b0a233e0b85755b2057b5568 NeedsCompilation: no Title: Enable downstream analysis of Visium capture areas stitched together with Fiji Description: This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects. biocViews: Software, Spatial, Transcriptomics, Transcription, GeneExpression, Visualization, DataImport Author: Nicholas J. Eagles [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Nicholas J. Eagles URL: https://github.com/LieberInstitute/visiumStitched VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/visiumStitched git_url: https://git.bioconductor.org/packages/visiumStitched git_branch: devel git_last_commit: 4c54019 git_last_commit_date: 2024-12-13 Date/Publication: 2024-12-18 source.ver: src/contrib/visiumStitched_0.99.15.tar.gz win.binary.ver: bin/windows/contrib/4.5/visiumStitched_0.99.15.zip vignettes: vignettes/visiumStitched/inst/doc/misc.html, vignettes/visiumStitched/inst/doc/visiumStitched.html vignetteTitles: Miscellaneous notes, Introduction to visiumStitched hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/visiumStitched/inst/doc/misc.R, vignettes/visiumStitched/inst/doc/visiumStitched.R dependencyCount: 231 Package: vissE Version: 1.15.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr License: GPL-3 MD5sum: 6c495062d411be4d6541cb8089fb309c NeedsCompilation: no Title: Visualising Set Enrichment Analysis Results Description: This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches. biocViews: Software, GeneExpression, GeneSetEnrichment, NetworkEnrichment, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Ahmed Mohamed [ctb] Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/vissE VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/vissE/issues git_url: https://git.bioconductor.org/packages/vissE git_branch: devel git_last_commit: 090cdb9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vissE_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vissE_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vissE_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vissE_1.15.0.tgz vignettes: vignettes/vissE/inst/doc/vissE.html vignetteTitles: vissE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vissE/inst/doc/vissE.R suggestsMe: msigdb dependencyCount: 143 Package: Voyager Version: 1.9.1 Depends: R (>= 4.2.0), SpatialFeatureExperiment (>= 1.7.3) Imports: BiocParallel, bluster, DelayedArray, ggnewscale, ggplot2 (>= 3.4.0), grDevices, grid, lifecycle, Matrix, matrixStats, memuse, methods, patchwork, rlang, RSpectra, S4Vectors, scales, scico, sf, SingleCellExperiment, SpatialExperiment, spdep, stats, SummarizedExperiment, terra, utils, zeallot Suggests: arrow, automap, BiocSingular, BiocStyle, cowplot, data.table, EBImage, ExperimentHub, ggh4x, gstat, hexbin, knitr, pheatmap, RBioFormats, rhdf5, rmarkdown, scater, scattermore, scran, sfarrow, SFEData, testthat (>= 3.0.0), vdiffr, xml2 License: Artistic-2.0 MD5sum: 81cb8330482fffa4931d0ea2dbd4e829 NeedsCompilation: no Title: From geospatial to spatial omics Description: SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results. biocViews: GeneExpression, Spatial, Transcriptomics, Visualization Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Kayla Jackson [aut] (ORCID: ), Laura Luebbert [aut] (ORCID: ), Lior Pachter [aut, rev] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/voyager VignetteBuilder: knitr BugReports: https://github.com/pachterlab/voyager/issues git_url: https://git.bioconductor.org/packages/Voyager git_branch: devel git_last_commit: 4e6d24e git_last_commit_date: 2024-11-08 Date/Publication: 2024-11-08 source.ver: src/contrib/Voyager_1.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/Voyager_1.9.1.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Voyager_1.9.1.tgz vignettes: vignettes/Voyager/inst/doc/overview.html vignetteTitles: Functionality overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Voyager/inst/doc/overview.R suggestsMe: SpatialFeatureExperiment dependencyCount: 178 Package: VplotR Version: 1.17.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL (>= 3) MD5sum: a118085335952c14511d6a032c3eaf2e NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: devel git_last_commit: 388e664 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/VplotR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/VplotR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/VplotR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/VplotR_1.17.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 85 Package: vsclust Version: 1.9.3 Depends: R (>= 4.2.0) Imports: matrixStats, limma, parallel, shiny, qvalue, grDevices, stats, MultiAssayExperiment, graphics LinkingTo: Rcpp Suggests: knitr, yaml, testthat (>= 3.0.0), rmarkdown, BiocStyle, clusterProfiler License: GPL-2 MD5sum: 5d874784425df56986b695178fd4d2d2 NeedsCompilation: yes Title: Feature-based variance-sensitive quantitative clustering Description: Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis. biocViews: Clustering, Annotation, PrincipalComponent, DifferentialExpression, Visualization, Proteomics, Metabolomics Author: Veit Schwammle [aut, cre] Maintainer: Veit Schwammle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsclust git_branch: devel git_last_commit: d51f658 git_last_commit_date: 2024-11-13 Date/Publication: 2024-11-14 source.ver: src/contrib/vsclust_1.9.3.tar.gz win.binary.ver: bin/windows/contrib/4.5/vsclust_1.9.3.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vsclust_1.9.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vsclust_1.9.3.tgz vignettes: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.html, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.html vignetteTitles: VSClust on Bioconductor object, VSClust workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.R, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.R dependencyCount: 98 Package: vsn Version: 3.75.0 Depends: R (>= 4.0.0), methods, Biobase Imports: affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr, testthat License: Artistic-2.0 Archs: x64 MD5sum: e41ee936701ec65753a4efdfb182c124 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: devel git_last_commit: 79d34f6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vsn_3.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vsn_3.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vsn_3.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vsn_3.75.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: webbioc, rnaseqGene importsMe: arrayQualityMetrics, autonomics, bnem, DEP, Doscheda, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, PRONE, pvca, tilingArray, ExpressionNormalizationWorkflow, lfproQC suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, ribosomeProfilingQC, scp, twilight, estrogen, wrMisc dependencyCount: 45 Package: vtpnet Version: 0.47.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 21fac43230cc46ddd7fb57c3b22c426d NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: devel git_last_commit: 822bd57 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vtpnet_0.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/vtpnet_0.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/vtpnet_0.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/vtpnet_0.47.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 114 Package: vulcan Version: 1.29.0 Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq2, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: c90bd2f5735f443085999fd5203a1334 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: devel git_last_commit: 45481f2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/vulcan_1.29.0.tar.gz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R dependencyCount: 201 Package: waddR Version: 1.21.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache (>= 2.6.0), BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: 52d98d860ddf48368adc02cb0fd32511 NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: devel git_last_commit: fc50998 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/waddR_1.21.0.tar.gz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 104 Package: wateRmelon Version: 2.13.0 Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BiocStyle, knitr, rmarkdown, IlluminaHumanMethylationEPICmanifest, irlba, FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore Enhances: minfi License: GPL-3 MD5sum: 89dcfa6a01468c7ad4c1a68f6cfc62b8 NeedsCompilation: no Title: Illumina DNA methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb], Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb], Alexandria Andrayas [ctb] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: devel git_last_commit: 980e6cd git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/wateRmelon_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wateRmelon_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wateRmelon_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wateRmelon_2.13.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html vignetteTitles: wateRmelon User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 172 Package: wavClusteR Version: 2.41.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: 37cf9c0ee07c28e91d2e0dbd3eb686b5 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: devel git_last_commit: bd37a40 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/wavClusteR_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wavClusteR_2.41.0.zip vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 139 Package: weaver Version: 1.73.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 Archs: x64 MD5sum: 5bf6b6760e01d30cc35a8e63f11d8c3f NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: devel git_last_commit: 8e20e96 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/weaver_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/weaver_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/weaver_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/weaver_1.73.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R dependencyCount: 4 Package: webbioc Version: 1.79.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: bd445d134d315b150a41a3eaacd523b4 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: devel git_last_commit: ab9af2a git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/webbioc_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/webbioc_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/webbioc_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/webbioc_1.79.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 87 Package: weitrix Version: 1.19.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: da901d913905515b178002a1b165bbd1 NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: devel git_last_commit: 9762181 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/weitrix_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/weitrix_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/weitrix_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/weitrix_1.19.0.tgz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 92 Package: widgetTools Version: 1.85.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL Archs: x64 MD5sum: 185bae757044573d842fd9c009e83acb NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: devel git_last_commit: b5b1198 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/widgetTools_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/widgetTools_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/widgetTools_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/widgetTools_1.85.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.31.0 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 Archs: x64 MD5sum: a2474fb9a4fa965fd693102ada4be3a5 NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: devel git_last_commit: aad27cc git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/wiggleplotr_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wiggleplotr_1.31.0.zip vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R importsMe: factR suggestsMe: MARVEL dependencyCount: 90 Package: wpm Version: 1.17.0 Depends: R (>= 4.1.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: b83709c23aa91ff32e9d2d39e5bc4c24 NeedsCompilation: no Title: Well Plate Maker Description: The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges URL: https://github.com/HelBor/wpm, https://bioconductor.org/packages/release/bioc/html/wpm.html VignetteBuilder: knitr BugReports: https://github.com/HelBor/wpm/issues git_url: https://git.bioconductor.org/packages/wpm git_branch: devel git_last_commit: a741648 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/wpm_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wpm_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wpm_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wpm_1.17.0.tgz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 107 Package: wppi Version: 1.15.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: 52ac0c572df18ce8ac0f4af77786c6c7 NeedsCompilation: no Title: Weighting protein-protein interactions Description: Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Author: Ana Galhoz [cre, aut] (ORCID: ), Denes Turei [aut] (ORCID: ), Michael P. Menden [aut] (ORCID: ), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues git_url: https://git.bioconductor.org/packages/wppi git_branch: devel git_last_commit: 321e17d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/wppi_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/wppi_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/wppi_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/wppi_1.15.0.tgz vignettes: vignettes/wppi/inst/doc/wppi_workflow.html vignetteTitles: WPPI workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R dependencyCount: 92 Package: Wrench Version: 1.25.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: 9892c984775e31706960cfdfe7ac9067 NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: devel git_last_commit: 4f4b817 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Wrench_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Wrench_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/Wrench_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/Wrench_1.25.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq dependencyCount: 11 Package: xcms Version: 4.5.2 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0) Imports: MSnbase (>= 2.29.3), mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.37.1), lattice, MassSpecWavelet (>= 1.66.0), S4Vectors, IRanges, SummarizedExperiment, MsCoreUtils (>= 1.15.5), MsFeatures, MsExperiment (>= 1.5.4), Spectra (>= 1.15.7), progress, RColorBrewer, MetaboCoreUtils (>= 1.11.2) Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat (>= 3.1.9), pander, rmarkdown, MALDIquant, pheatmap, RANN, multtest, MsBackendMgf, signal, mgcv Enhances: Rgraphviz, rgl License: GPL (>= 2) + file LICENSE MD5sum: c4c474f2bc7427f2bc15e3f6037b4c49 NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith [aut], Ralf Tautenhahn [aut], Steffen Neumann [aut, cre] (ORCID: ), Paul Benton [aut], Christopher Conley [aut], Johannes Rainer [aut] (ORCID: ), Michael Witting [ctb], William Kumler [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Pablo Vangeenderhuysen [ctb] (ORCID: ), Carl Brunius [ctb] (ORCID: ) Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: devel git_last_commit: 143f3b9f git_last_commit_date: 2024-12-15 Date/Publication: 2024-12-15 source.ver: src/contrib/xcms_4.5.2.tar.gz win.binary.ver: bin/windows/contrib/4.5/xcms_4.5.2.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xcms_4.5.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xcms_4.5.2.tgz vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LC-MS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/LC-MS-feature-grouping.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, metaMS, ncGTW, PtH2O2lipids importsMe: CAMERA, cliqueMS, cosmiq, squallms, faahKO suggestsMe: CluMSID, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, LCMSQA, MetabolomicsBasics, RAMClustR dependencyCount: 146 Package: xcore Version: 1.11.0 Depends: R (>= 4.2) Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>= 1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges (>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix (>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>= 1.18.0), stats, S4Vectors (>= 0.30.0), utils Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0), BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>= 1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16), ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>= 1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>= 2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis (>= 2.0.1), xcoredata License: GPL-2 Archs: x64 MD5sum: 42f4ea7eefa4180de8cd5f758e3b99a3 NeedsCompilation: no Title: xcore expression regulators inference Description: xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes. biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression, Sequencing Author: Maciej Migdał [aut, cre] (ORCID: ), Bogumił Kaczkowski [aut] (ORCID: ) Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: devel git_last_commit: 1a80b69 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/xcore_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xcore_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xcore_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xcore_1.11.0.tgz vignettes: vignettes/xcore/inst/doc/xcore_vignette.html vignetteTitles: xcore vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R suggestsMe: xcoredata dependencyCount: 71 Package: XDE Version: 2.53.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 Archs: x64 MD5sum: 02c7270b21333e86c4f6e02d5d2405da NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: devel git_last_commit: ef93912 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/XDE_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XDE_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XDE_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XDE_2.53.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 65 Package: xenLite Version: 1.1.0 Depends: R (>= 4.1) Imports: SpatialExperiment, BiocFileCache, Matrix, S4Vectors, SummarizedExperiment, methods, utils, EBImage, shiny, HDF5Array, arrow, ggplot2, SingleCellExperiment, TENxIO, dplyr, graphics, stats Suggests: knitr, testthat, BiocStyle, yesno, terra, SpatialFeatureExperiment, SFEData, tiff License: Artistic-2.0 MD5sum: 3b8fcc41591ce447ac4f935fdb80011e NeedsCompilation: no Title: Simple classes and methods for managing Xenium datasets Description: Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/xenLite VignetteBuilder: knitr BugReports: https://github.com/vjcitn/xenLite/issues git_url: https://git.bioconductor.org/packages/xenLite git_branch: devel git_last_commit: 921c057 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/xenLite_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xenLite_1.1.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xenLite_1.1.0.tgz vignettes: vignettes/xenLite/inst/doc/xenLite.html vignetteTitles: xenLite: exploration of a class for Xenium demonstration data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xenLite/inst/doc/xenLite.R dependencyCount: 138 Package: Xeva Version: 1.23.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: d89e0bacda7d137e4867da02ea710a06 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: The Xeva package provides efficient and powerful functions for patient-drived xenograft (PDX) based pharmacogenomic data analysis. This package contains a set of functions to perform analysis of patient-derived xenograft data. This package was developed by the BHKLab, for further information please see our documentation. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: devel git_last_commit: ceb426d git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/Xeva_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/Xeva_1.23.0.zip vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 169 Package: XINA Version: 1.25.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: db61f27579625ccde5d74a153ba1909a NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: devel git_last_commit: 19afdec git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/XINA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XINA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XINA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XINA_1.25.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 72 Package: xmapbridge Version: 1.65.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: f95f1854702b67c9b83e38281d795562 NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: devel git_last_commit: 13dcfde git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/xmapbridge_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/xmapbridge_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/xmapbridge_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/xmapbridge_1.65.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: XNAString Version: 1.15.0 Depends: R (>= 4.1) Imports: utils, Biostrings, pwalign, BSgenome, data.table, GenomicRanges, IRanges, methods, Rcpp, stringi, S4Vectors, future.apply, stringr, formattable, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, BSgenome.Hsapiens.UCSC.hg38, pander License: GPL-2 MD5sum: 4dab584da0b70d4e85e5ca03dfbd3df0 NeedsCompilation: yes Title: Efficient Manipulation of Modified Oligonucleotide Sequences Description: The XNAString package allows for description of base sequences and associated chemical modifications in a single object. XNAString is able to capture single stranded, as well as double stranded molecules. Chemical modifications are represented as independent strings associated with different features of the molecules (base sequence, sugar sequence, backbone sequence, modifications) and can be read or written to a HELM notation. It also enables secondary structure prediction using RNAfold from ViennaRNA. XNAString is designed to be efficient representation of nucleic-acid based therapeutics, therefore it stores information about target sequences and provides interface for matching and alignment functions from Biostrings and pwalign packages. biocViews: SequenceMatching, Alignment, Sequencing, Genetics Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut], Peter H. Hagedorn [aut] Maintainer: Marianna Plucinska VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XNAString git_branch: devel git_last_commit: 6cd7edb git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/XNAString_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/XNAString_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XNAString_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XNAString_1.15.0.tgz vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html vignetteTitles: XNAString classes and functionalities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R dependencyCount: 96 Package: XVector Version: 0.47.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) Imports: methods, utils, tools, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 MD5sum: e582db12ec74480fab499cb028e6e1d7 NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: devel git_last_commit: 3fa081b git_last_commit_date: 2024-12-19 Date/Publication: 2024-12-20 source.ver: src/contrib/XVector_0.47.1.tar.gz win.binary.ver: bin/windows/contrib/4.5/XVector_0.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/XVector_0.47.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/XVector_0.47.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2, DECIPHER, gcrma, GenomAutomorphism, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, SparseArray, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation, simMP suggestsMe: IRanges, musicatk linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, ShortRead, SparseArray, triplex, VariantAnnotation, VariantFiltering dependencyCount: 11 Package: yamss Version: 1.33.0 Depends: R (>= 4.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: d625f838264b524250e24eff8a3f2855 NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. At the core of these methods is a peak detection phase that pools information across all samples simultaneously. This is in contrast to other methods that detect peaks in a sample-by-sample basis. biocViews: MassSpectrometry, Metabolomics, PeakDetection, Software Author: Leslie Myint [cre, aut] (ORCID: ), Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: devel git_last_commit: 2ec1eaf git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/yamss_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/yamss_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/yamss_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/yamss_1.33.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 77 Package: YAPSA Version: 1.33.0 Depends: R (>= 4.0.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, parallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 1dcfc83f3b2ec8974ad4fda596ee6c95 NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann [aut], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut, cre], Matthias Schlesner [aut] Maintainer: Zuguang Gu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: devel git_last_commit: d41177e git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/YAPSA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/YAPSA_1.33.0.zip vignettes: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html, vignettes/YAPSA/inst/doc/vignette_exomes.html, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html, vignettes/YAPSA/inst/doc/vignettes_Indel.html, vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: 3. Confidence Intervals, 6. Usage of YAPSA for WES data, 2. Signature-specific cutoffs, 4. Stratified Analysis of Mutational Signatures, 5. Indel signature analysis, 1. Usage of YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R, vignettes/YAPSA/inst/doc/vignette_exomes.R, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R, vignettes/YAPSA/inst/doc/vignettes_Indel.R, vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 199 Package: yarn Version: 1.33.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: 8c744e8a6446a582b88fcca664a621e4 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: devel git_last_commit: 7e48450 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-30 source.ver: src/contrib/yarn_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/yarn_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/yarn_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/yarn_1.33.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependencyCount: 167 Package: zellkonverter Version: 1.17.0 Imports: basilisk, cli, DelayedArray, Matrix, methods, reticulate, S4Vectors, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, utils Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr, pkgload, rhdf5 (>= 2.45.1), rmarkdown, scRNAseq, spelling, testthat, withr License: MIT + file LICENSE MD5sum: cb5409acefdd8a9f8f811d5284b5af93 NeedsCompilation: no Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (ORCID: ), Aaron Lun [aut] (ORCID: ), Jack Kamm [ctb] (ORCID: ), Robrecht Cannoodt [ctb] (ORCID: , github: rcannood), Gabriel Hoffman [ctb] (ORCID: , github: GabrielHoffman) Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues git_url: https://git.bioconductor.org/packages/zellkonverter git_branch: devel git_last_commit: 6aa0261 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/zellkonverter_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zellkonverter_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zellkonverter_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zellkonverter_1.17.0.tgz vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html vignetteTitles: Converting to/from AnnData to SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R dependsOnMe: scATAC.Explorer, OSCA.intro importsMe: BgeeDB, singleCellTK, velociraptor suggestsMe: cellxgenedp, CuratedAtlasQueryR, GloScope, HDF5Array, HCATonsilData dependencyCount: 53 Package: zenith Version: 1.9.0 Depends: R (>= 4.2.0), limma, methods Imports: variancePartition (>= 1.26.0), EnrichmentBrowser (>= 2.22.0), GSEABase (>= 1.54.0), msigdbr (>= 7.5.1), Rfast, ggplot2, tidyr, reshape2, progress, utils, Rdpack, stats Suggests: BiocStyle, BiocGenerics, knitr, pander, rmarkdown, tweeDEseqCountData, edgeR, kableExtra, RUnit License: Artistic-2.0 MD5sum: 085d76a00fd480d14321bde5836b9ee7 NeedsCompilation: no Title: Gene set analysis following differential expression using linear (mixed) modeling with dream Description: Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream(). biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] Maintainer: Gabriel Hoffman URL: https://DiseaseNeuroGenomics.github.io/zenith VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/zenith/issues git_url: https://git.bioconductor.org/packages/zenith git_branch: devel git_last_commit: 5422050 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-12 source.ver: src/contrib/zenith_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zenith_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zenith_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zenith_1.9.0.tgz vignettes: vignettes/zenith/inst/doc/loading_genesets.html, vignettes/zenith/inst/doc/zenith.html vignetteTitles: Example usage of zenith on GEUVAIDIS RNA-seq, Example usage of zenith on RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zenith/inst/doc/loading_genesets.R, vignettes/zenith/inst/doc/zenith.R importsMe: dreamlet suggestsMe: variancePartition dependencyCount: 164 Package: zFPKM Version: 1.29.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE Archs: x64 MD5sum: 7e254537dcea3e436ae0038536107b6f NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: devel git_last_commit: 463b7d1 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/zFPKM_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zFPKM_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zFPKM_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zFPKM_1.29.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R suggestsMe: DGEobj.utils dependencyCount: 72 Package: zinbwave Version: 1.29.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, sparseMatrixStats License: Artistic-2.0 Archs: x64 MD5sum: eb7f67aaeb71cd4efa6e0b22f10ecfd4 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: devel git_last_commit: 4cf7edc git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-05 source.ver: src/contrib/zinbwave_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zinbwave_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zinbwave_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zinbwave_1.29.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: clusterExperiment, scBFA, singleCellTK, SpatialDDLS suggestsMe: MAST, splatter dependencyCount: 78 Package: zlibbioc Version: 1.53.0 Suggests: BiocStyle, knitr License: Artistic-2.0 + file LICENSE MD5sum: e7e2281813c4523f308049af00d80414 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/zlibbioc VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/zlibbioc/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: devel git_last_commit: cd57560 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/zlibbioc_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/zlibbioc_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/zlibbioc_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/zlibbioc_1.53.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.html vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zlibbioc/inst/doc/UsingZlibbioc.R importsMe: affy, bamsignals, ChemmineOB, GrafGen, MADSEQ, makecdfenv, NanoMethViz, oligo, qckitfastq, rtracklayer, snpStats, VariantAnnotation, XVector, jackalope linksToMe: bamsignals, ChemmineOB, maftools, methylKit, Rfastp, seqTools, jackalope dependencyCount: 0 Package: ZygosityPredictor Version: 1.7.0 Depends: R (>= 4.3.0) Imports: GenomicAlignments, GenomicRanges, Rsamtools, IRanges, VariantAnnotation, DelayedArray, dplyr, stringr, purrr, tibble, methods, knitr, igraph, readr, stats, magrittr, rlang Suggests: rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: 9500368a1acb148363195eda0ad1c55b NeedsCompilation: no Title: Package for prediction of zygosity for variants/genes in NGS data Description: The ZygosityPredictor allows to predict how many copies of a gene are affected by small variants. In addition to the basic calculations of the affected copy number of a variant, the Zygosity-Predictor can integrate the influence of several variants on a gene and ultimately make a statement if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, the Zygosity-Predictor can address whether unmutated copies of tumor-suppressor genes are present. Beyond this, it is possible to make this statement for all genes of an organism. The Zygosity-Predictor was primarily developed to handle SNVs and INDELs (later addressed as small-variants) of somatic and germline origin. In order not to overlook severe effects outside of the small-variant context, it has been extended with the assessment of large scale deletions, which cause losses of whole genes or parts of them. biocViews: BiomedicalInformatics, FunctionalPrediction, SomaticMutation, GenePrediction Author: Marco Rheinnecker [aut, cre] (ORCID: ), Marc Ruebsam [aut], Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara Hutter [aut] Maintainer: Marco Rheinnecker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ZygosityPredictor git_branch: devel git_last_commit: 1c2d5b8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-10-29 source.ver: src/contrib/ZygosityPredictor_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.5/ZygosityPredictor_1.7.0.zip vignettes: vignettes/ZygosityPredictor/inst/doc/Usage.html vignetteTitles: Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ZygosityPredictor/inst/doc/Usage.R dependencyCount: 103 Package: ADAPT Version: 1.1.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.8), RcppArmadillo (>= 0.10.8), RcppParallel (>= 5.1.5), phyloseq (>= 1.39.0), methods, stats, ggplot2 (>= 3.4.1), ggrepel (>= 0.9.1) LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0) License: MIT + file LICENSE NeedsCompilation: yes Title: Analysis of Microbiome Differential Abundance by Pooling Tobit Models Description: ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Metagenomics, Software, MultipleComparison Author: Mukai Wang [aut, cre] (ORCID: ), Simon Fontaine [ctb], Hui Jiang [ctb], Gen Li [aut, ctb] Maintainer: Mukai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAPT git_branch: devel git_last_commit: ec4beea git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/ADAPT_1.1.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bgx Version: 1.73.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: devel git_last_commit: 6430db5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/bgx_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/bgx_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/bgx_1.73.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: chromstaR Version: 1.33.1 Depends: R (>= 3.5.0), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: ImmunoOncology, Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/chromstaR git_branch: devel git_last_commit: 225ab60 git_last_commit_date: 2024-12-17 Date/Publication: 2024-12-18 win.binary.ver: bin/windows/contrib/4.5/chromstaR_1.33.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: combi Version: 1.19.0 Depends: R (>= 4.0), DBI Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix (>= 1.6.0), BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 NeedsCompilation: no Title: Compositional omics model based visual integration Description: This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: devel git_last_commit: fb1252a git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/combi_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/combi_1.19.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: dar Version: 1.3.0 Depends: R (>= 4.4.0) Imports: cli, ComplexHeatmap, crayon, dplyr, generics, ggplot2, glue, gplots, heatmaply, magrittr, methods, mia, phyloseq, purrr, readr, rlang (>= 0.4.11), scales, stringr, tibble, tidyr, UpSetR, waldo Suggests: ALDEx2, ANCOMBC, apeglm, ashr, Biobase, corncob, covr, DESeq2, devtools, furrr, future, knitr, lefser, limma, Maaslin2, metagenomeSeq, microbiome, rmarkdown, roxygen2, roxyglobals, roxytest, rstatix, SummarizedExperiment, TreeSummarizedExperiment, testthat (>= 3.0.0) License: MIT + file LICENSE NeedsCompilation: no Title: Differential Abundance Analysis by Consensus Description: Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way. biocViews: Software, Sequencing, Microbiome, Metagenomics, MultipleComparison, Normalization Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar, https://microbialgenomics-irsicaixaorg.github.io/dar/ VignetteBuilder: knitr BugReports: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues git_url: https://git.bioconductor.org/packages/dar git_branch: devel git_last_commit: fceb04a git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/dar_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/dar_1.3.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DMRScan Version: 1.29.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: devel git_last_commit: b96365d git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/DMRScan_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DMRScan_1.29.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GraphPAC Version: 1.49.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: devel git_last_commit: fbd6916 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/GraphPAC_1.49.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/GraphPAC_1.49.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: groHMM Version: 1.41.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: devel git_last_commit: 8d0b05a git_last_commit_date: 2024-12-03 Date/Publication: 2024-12-05 win.binary.ver: bin/windows/contrib/4.5/groHMM_1.41.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: immApex Version: 1.1.0 Depends: R (>= 4.3.0) Imports: hash, httr, keras3, magrittr, matrixStats, methods, reticulate, rvest, SingleCellExperiment, stats, stringi, stringr, tensorflow, utils Suggests: BiocStyle, ggplot2, knitr, markdown, rmarkdown, scRepertoire, spelling, testthat, viridis License: MIT + file LICENSE NeedsCompilation: no Title: Tools for Adaptive Immune Receptor Sequence-Based Keras Modeling Description: A set of tools to build tensorflow/keras3-based models in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing, MotifAnnotation Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding URL: https://github.com/ncborcherding/immApex/ VignetteBuilder: knitr BugReports: https://github.com/ncborcherding/immApex/issues git_url: https://git.bioconductor.org/packages/immApex git_branch: devel git_last_commit: 858429f git_last_commit_date: 2024-10-30 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/immApex_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/immApex_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/immApex_1.1.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iPAC Version: 1.51.0 Depends: R(>= 2.15), scatterplot3d, Biostrings, pwalign, multtest Imports: grDevices, graphics, stats, gdata License: GPL-2 Archs: x64 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: devel git_last_commit: 8338c57 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/iPAC_1.51.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/iPAC_1.51.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MBECS Version: 1.11.0 Depends: R (>= 4.1) Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap, rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva, tibble, tidyr, vegan, stats, utils, Matrix Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 NeedsCompilation: no Title: Evaluation and correction of batch effects in microbiome data-sets Description: The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects. biocViews: BatchEffect, Microbiome, ReportWriting, Visualization, Normalization, QualityControl Author: Michael Olbrich [aut, cre] (ORCID: ) Maintainer: Michael Olbrich URL: https://github.com/rmolbrich/MBECS VignetteBuilder: knitr BugReports: https://github.com/rmolbrich/MBECS/issues/new git_url: https://git.bioconductor.org/packages/MBECS git_branch: devel git_last_commit: f568758 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/MBECS_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/MBECS_1.11.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: microbiome Version: 1.29.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE Archs: x64 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (ORCID: ), Sudarshan Shetty [aut] (ORCID: ) Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: devel git_last_commit: be82090 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/microbiome_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microbiome_1.29.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: microbiomeDASim Version: 1.21.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: devel git_last_commit: a5f0a43 git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-18 win.binary.ver: bin/windows/contrib/4.5/microbiomeDASim_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/microbiomeDASim_1.21.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: phyloseq Version: 1.51.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: devel git_last_commit: fca5959 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/phyloseq_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/phyloseq_1.51.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: prebs Version: 1.47.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: devel git_last_commit: 711f895 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/prebs_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/prebs_1.47.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: QuartPAC Version: 1.39.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Imports: Biostrings, pwalign Suggests: RUnit, BiocGenerics, rgl License: GPL-2 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: devel git_last_commit: d448196 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/QuartPAC_1.39.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RCM Version: 1.23.0 Depends: R (>= 4.0), DBI Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: devel git_last_commit: 0cde287 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/RCM_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RCM_1.23.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: reconsi Version: 1.19.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: devel git_last_commit: ebca5ca git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/reconsi_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/reconsi_1.19.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rmspc Version: 1.13.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 NeedsCompilation: no Title: Multiple Sample Peak Calling Description: The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples. biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre] Maintainer: Meriem Bahda URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 6.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: devel git_last_commit: 9632a1d git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/rmspc_1.13.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RPA Version: 1.63.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: knitr, parallel License: BSD_2_clause + file LICENSE NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] (ORCID: ) Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: devel git_last_commit: b608015 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/RPA_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/RPA_1.63.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SIAMCAT Version: 2.11.0 Depends: R (>= 4.2.0), mlr3, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot, lmerTest, mlr3learners, mlr3tuning, paradox, lgr Suggests: BiocStyle, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (ORCID: ), Jakob Wirbel [aut, cre] (ORCID: ), Georg Zeller [aut] (ORCID: ), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: devel git_last_commit: 56df787 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/SIAMCAT_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SIAMCAT_2.11.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SimBu Version: 1.9.0 Imports: basilisk, BiocParallel, data.table, dplyr, ggplot2, tools, Matrix (>= 1.3.3), methods, phyloseq, proxyC, RColorBrewer, RCurl, reticulate, sparseMatrixStats, SummarizedExperiment, tidyr Suggests: curl, knitr, matrixStats, rmarkdown, Seurat (>= 5.0.0), SeuratObject (>= 5.0.0), testthat (>= 3.0.0) License: GPL-3 + file LICENSE NeedsCompilation: no Title: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets Description: SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations. biocViews: Software, RNASeq, SingleCell Author: Alexander Dietrich [aut, cre] Maintainer: Alexander Dietrich URL: https://github.com/omnideconv/SimBu VignetteBuilder: knitr BugReports: https://github.com/omnideconv/SimBu/issues git_url: https://git.bioconductor.org/packages/SimBu git_branch: devel git_last_commit: 3836594 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/SimBu_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SimBu_1.9.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SpacePAC Version: 1.45.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: devel git_last_commit: 1b3b2da git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/SpacePAC_1.45.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/SpacePAC_1.45.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SPLINTER Version: 1.33.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, pwalign, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: txdbmaker, BiocStyle, knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: devel git_last_commit: 15c4210 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/SPLINTER_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPLINTER_1.33.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SPsimSeq Version: 1.17.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: devel git_last_commit: 36e9409 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/SPsimSeq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/SPsimSeq_1.17.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TransView Version: 1.51.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.99.1) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 Archs: x64 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/TransView git_branch: devel git_last_commit: c3729dd git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/TransView_1.51.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: traviz Version: 1.13.0 Depends: R (>= 4.0) Imports: ggplot2, viridis, mgcv, SingleCellExperiment, slingshot, princurve, Biobase, methods, RColorBrewer, SummarizedExperiment, grDevices, graphics, rgl Suggests: scater, dplyr, testthat (>= 3.0.0), covr, S4Vectors, rmarkdown, knitr License: MIT + file LICENSE NeedsCompilation: no Title: Trajectory functions for visualization and interpretation. Description: traviz provides a suite of functions to plot trajectory related objects from Bioconductor packages. It allows plotting trajectories in reduced dimension, as well as averge gene expression smoothers as a function of pseudotime. Asides from general utility functions, traviz also allows plotting trajectories estimated by Slingshot, as well as smoothers estimated by tradeSeq. Furthermore, it allows for visualization of Slingshot trajectories using ggplot2. biocViews: GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, ctb], Kelly Street [aut, ctb], Koen Van den Berge [aut, cre] Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/traviz git_branch: devel git_last_commit: 76fcdb4 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-03 win.binary.ver: bin/windows/contrib/4.5/traviz_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/traviz_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/traviz_1.13.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DeProViR Version: 1.3.0 Depends: keras Imports: caret, data.table, dplyr, fmsb, ggplot2, grDevices, pROC, PRROC, readr, stats, BiocFileCache, utils Suggests: rmarkdown, tensorflow, BiocStyle, RUnit, knitr, BiocGenerics License: MIT+ file LICENSE MD5sum: d94fb11ecf623eb7129e08bf38c4eb98 NeedsCompilation: no Title: A Deep-Learning Framework Based on Pre-trained Sequence Embeddings for Predicting Host-Viral Protein-Protein Interactions Description: Emerging infectious diseases, exemplified by the zoonotic COVID-19 pandemic caused by SARS-CoV-2, are grave global threats. Understanding protein-protein interactions (PPIs) between host and viral proteins is essential for therapeutic targets and insights into pathogen replication and immune evasion. While experimental methods like yeast two-hybrid screening and mass spectrometry provide valuable insights, they are hindered by experimental noise and costs, yielding incomplete interaction maps. Computational models, notably DeProViR, predict PPIs from amino acid sequences, incorporating semantic information with GloVe embeddings. DeProViR employs a Siamese neural network, integrating convolutional and Bi-LSTM networks to enhance accuracy. It overcomes the limitations of feature engineering, offering an efficient means to predict host-virus interactions, which holds promise for antiviral therapies and advancing our understanding of infectious diseases. biocViews: Proteomics, SystemsBiology, NetworkInference, NeuralNetwork, Network Author: Matineh Rahmatbakhsh [aut, trl, cre] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/DeProViR VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/DeProViR/issues git_url: https://git.bioconductor.org/packages/DeProViR git_branch: devel git_last_commit: 612f1d9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/DeProViR_1.3.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: netZooR Version: 1.11.0 Depends: R (>= 4.2.0), igraph, reticulate, pandaR, yarn, matrixcalc Imports: RCy3, viridisLite, STRINGdb, Biobase, GOstats, AnnotationDbi, matrixStats, GO.db, org.Hs.eg.db, Matrix, gplots, nnet, data.table, vegan, stats, utils, reshape, reshape2, penalized, parallel, doParallel, foreach, ggplot2, ggdendro, grid, MASS, assertthat, tidyr, methods, dplyr, graphics Suggests: testthat (>= 2.1.0), knitr, rmarkdown, pkgdown License: GPL-3 MD5sum: ccc0842d642691bb77ecd9bb0aab8aa4 NeedsCompilation: no Title: Unified methods for the inference and analysis of gene regulatory networks Description: netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information. biocViews: NetworkInference, Network, GeneRegulation, GeneExpression, Transcription, Microarray, GraphAndNetwork Author: Marouen Ben Guebila [aut, cre] (ORCID: ), Tian Wang [aut] (ORCID: ), John Platig [aut], Marieke Kuijjer [aut] (ORCID: ), Megha Padi [aut] (ORCID: ), Rebekka Burkholz [aut], Des Weighill [aut] (ORCID: ), Kate Shutta [aut] (ORCID: ) Maintainer: Marouen Ben Guebila URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/ VignetteBuilder: knitr BugReports: https://github.com/netZoo/netZooR/issues git_url: https://git.bioconductor.org/packages/netZooR git_branch: devel git_last_commit: fcf288f git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/netZooR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/netZooR_1.11.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: NeuCA Version: 1.13.0 Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment, kableExtra Suggests: BiocStyle, knitr, rmarkdown, networkD3 License: GPL-2 MD5sum: 0f1f65796b0048ff5895c8f0c4ec73ca NeedsCompilation: no Title: NEUral network-based single-Cell Annotation tool Description: NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types. biocViews: SingleCell, Software, Classification, NeuralNetwork, RNASeq, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Ziyi Li [aut], Hao Feng [aut, cre] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NeuCA git_branch: devel git_last_commit: a59dea2 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/NeuCA_1.13.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: seqArchR Version: 1.11.0 Depends: R (>= 4.2.0) Imports: utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods, stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2 (>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings, grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1) Suggests: cowplot, hopach (>= 2.42.0), BiocStyle, knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>= 0.3.0) License: GPL-3 | file LICENSE MD5sum: 984850b55f7f6b5c6652ec471294b26f NeedsCompilation: no Title: Identify Different Architectures of Sequence Elements Description: seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos. biocViews: MotifDiscovery, GeneRegulation, MathematicalBiology, SystemsBiology, Transcriptomics, Genetics, Clustering, DimensionReduction, FeatureExtraction, DNASeq Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID: ) Maintainer: Sarvesh Nikumbh URL: https://snikumbh.github.io/seqArchR/, https://github.com/snikumbh/seqArchR SystemRequirements: Python (>= 3.5), scikit-learn (>= 0.21.2), packaging VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchR/issues git_url: https://git.bioconductor.org/packages/seqArchR git_branch: devel git_last_commit: 2981ab5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqArchR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqArchR_1.11.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: seqArchRplus Version: 1.7.0 Depends: R (>= 4.2), GenomicRanges, IRanges, S4Vectors Imports: BiocParallel, Biostrings, BSgenome, ChIPseeker, cli, clusterProfiler, cowplot, factoextra, GenomeInfoDb, ggplot2, ggimage, graphics, grDevices, gridExtra, heatmaps, magick, methods, RColorBrewer, scales, seqArchR, seqPattern, stats, utils Suggests: BSgenome.Dmelanogaster.UCSC.dm6, BiocStyle, CAGEr (>= 2.0.2), covr, knitr (>= 1.22), org.Dm.eg.db, pdftools, rmarkdown (>= 1.12), slickR, TxDb.Dmelanogaster.UCSC.dm6.ensGene, xfun License: GPL-3 MD5sum: 5248dada5ae3747fb97702dbcfa2ce3e NeedsCompilation: no Title: Downstream analyses of promoter sequence architectures and HTML report generation Description: seqArchRplus facilitates downstream analyses of promoter sequence architectures/clusters identified by seqArchR (or any other tool/method). With additional available information such as the TPM values and interquantile widths (IQWs) of the CAGE tag clusters, seqArchRplus can order the input promoter clusters by their shape (IQWs), and write the cluster information as browser/IGV track files. Provided visualizations are of two kind: per sample/stage and per cluster visualizations. Those of the first kind include: plot panels for each sample showing per cluster shape, TPM and other score distributions, sequence logos, and peak annotations. The second include per cluster chromosome-wise and strand distributions, motif occurrence heatmaps and GO term enrichments. Additionally, seqArchRplus can also generate HTML reports for easy viewing and comparison of promoter architectures between samples/stages. biocViews: Annotation, Visualization, ReportWriting, GO, MotifAnnotation, Clustering Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID: ) Maintainer: Sarvesh Nikumbh URL: https://github.com/snikumbh/seqArchRplus VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchRplus/issues git_url: https://git.bioconductor.org/packages/seqArchRplus git_branch: devel git_last_commit: 782da3e git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/seqArchRplus_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/seqArchRplus_1.7.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TypeInfo Version: 1.73.0 Depends: methods Suggests: Biobase License: BSD_2_clause MD5sum: 9fcbb46572ed4468b586af04f54216ce NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: devel git_last_commit: 507b15f git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-22 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.5/TypeInfo_1.73.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CyTOFpower Version: 1.13.0 Depends: R (>= 4.1) Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods, rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix, SummarizedExperiment, tibble, tidyr Suggests: testthat (>= 3.0.0), BiocStyle, knitr License: LGPL-3 MD5sum: c317476f6d718c15c2766f76715d573f NeedsCompilation: no Title: Power analysis for CyTOF experiments Description: This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed. biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod, Software Author: Anne-Maud Ferreira [cre, aut] (ORCID: ), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CyTOFpower git_branch: devel git_last_commit: 46f9bdd git_last_commit_date: 2024-10-29 Date/Publication: 2024-12-01 mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.5/CyTOFpower_1.13.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: lapmix Version: 1.73.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch Package: DEXSeq Version: 1.53.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.39.6), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures, txdbmaker, pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat, pasillaBamSubset, GenomicAlignments, roxygen2, glmGamPoi License: GPL (>= 3) Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr Package: staRank Version: 1.49.1 Depends: methods, cellHTS2, R (>= 2.10) License: GPL Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: ImmunoOncology, MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg PackageStatus: Deprecated Package: HTSeqGenie Version: 4.37.1 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), pwalign, chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db, RUnit License: Artistic-2.0 Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder PackageStatus: Deprecated Package: supraHex Version: 1.45.1 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org PackageStatus: Deprecated Package: nondetects Version: 2.37.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr Package: regionReport Version: 1.41.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ Package: paxtoolsr Version: 1.41.1 Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr, rappdirs Suggests: testthat, knitr, BiocStyle, formatR, rmarkdown, RColorBrewer, foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler License: LGPL-3 Title: Access Pathways from Multiple Databases Through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database. Pathway Commons is a project by the Memorial Sloan-Kettering Cancer Center (MSKCC), Dana-Farber Cancer Institute (DFCI), and the University of Toronto. Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna [aut, cre] Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr PackageStatus: Deprecated Package: SigCheck Version: 2.39.0 Depends: R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark Package: gespeR Version: 1.39.1 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr PackageStatus: Deprecated Package: crossmeta Version: 1.33.1 Depends: R (>= 4.0) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0), data.table (>= 1.10.4), edgeR, fdrtool (>= 1.2.15), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), miniUI (>= 0.1.1), methods, oligo (>= 1.38.0), reader(>= 1.0.6), RCurl (>= 1.95.4.11), RSQLite (>= 2.1.1), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), shinyjs (>= 2.0.0), shinyBS (>= 0.61), shinyWidgets (>= 0.5.3), shinypanel (>= 0.1.0), tibble, XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat License: MIT + file LICENSE Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering URL: https://github.com/alexvpickering/crossmeta SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm) libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm) openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev (csw), openssl@1.1 (brew) VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/crossmeta/issues PackageStatus: Deprecated Package: PathoStat Version: 1.33.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues Package: IntEREst Version: 1.31.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors, GenomicFiles Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), txdbmaker, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMariaDB, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian VignetteBuilder: knitr Package: IsoformSwitchAnalyzeR Version: 2.7.0 Depends: R (>= 4.2), limma, DEXSeq, satuRn (>= 1.7.0), sva, ggplot2 (>= 3.3.5), pfamAnalyzeR Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl, Biobase, SummarizedExperiment, tidyr, S4Vectors, BiocParallel, pwalign Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) Title: Identify, Annotate and Visualize Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data Description: Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ), Jeroen Gilis [ctb] (ORCID: ) Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues Package: MAGeCKFlute Version: 2.11.1 Depends: R (>= 4.1) Imports: Biobase, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils, DOSE, clusterProfiler, pathview, enrichplot, msigdbr, depmap Suggests: biomaRt, BiocStyle, dendextend, graphics, knitr, pheatmap, png, scales, sva, BiocManager License: GPL (>=3) NeedsCompilation: no Title: Integrative Analysis Pipeline for Pooled CRISPR Functional Genetic Screens Description: CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl, Normalization, GeneSetEnrichment, Pathways, Visualization, GeneTarget, KEGG Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu Maintainer: Wubing Zhang VignetteBuilder: knitr PackageStatus: Deprecated Package: RGMQL Version: 1.27.1 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, ImmunoOncology, SingleCell Author: Simone Pallotta [aut, cre], Marco Masseroli [aut] Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr PackageStatus: Deprecated Package: srnadiff Version: 1.27.1 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), stats, methods, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocManager, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr Package: BEARscc Version: 1.27.1 Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr PackageStatus: Deprecated Package: DIAlignR Version: 2.15.1 Depends: methods, stats, R (>= 4.0) Imports: zoo (>= 1.8-3), data.table, magrittr, dplyr, tidyr, rlang, mzR (>= 2.18), signal, bit64, reticulate, ggplot2, RSQLite, DBI, ape, phangorn, pracma, RMSNumpress, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: knitr, akima, lattice, scales, gridExtra, latticeExtra, rmarkdown, BiocStyle, BiocParallel, testthat (>= 2.1.0) License: GPL-3 Title: Dynamic Programming Based Alignment of MS2 Chromatograms Description: To obtain unbiased proteome coverage from a biological sample, mass-spectrometer is operated in Data Independent Acquisition (DIA) mode. Alignment of these DIA runs establishes consistency and less missing values in complete data-matrix. This package implements dynamic programming with affine gap penalty based approach for pair-wise alignment of analytes. A hybrid approach of global alignment (through MS2 features) and local alignment (with MS2 chromatograms) is implemented in this tool. biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment, Software Author: Shubham Gupta [aut, cre] (ORCID: ), Hannes Rost [aut] (ORCID: ), Justin Sing [aut] Maintainer: Shubham Gupta SystemRequirements: C++14 VignetteBuilder: knitr BugReports: https://github.com/shubham1637/DIAlignR/issues PackageStatus: Deprecated Package: netDx Version: 1.19.1 Depends: R (>= 3.6) Imports: ROCR,pracma,ggplot2,glmnet,igraph,reshape2, parallel,stats,utils,MultiAssayExperiment,graphics,grDevices, methods,BiocFileCache,GenomicRanges, bigmemory,doParallel,foreach, combinat,rappdirs,GenomeInfoDb,S4Vectors, IRanges,RColorBrewer,Rtsne,httr,plotrix Suggests: curatedTCGAData, rmarkdown, testthat, knitr, BiocStyle, RCy3, clusterExperiment, netSmooth, scater License: MIT + file LICENSE Title: Network-based patient classifier Description: netDx is a general-purpose algorithm to build a patient classifier from heterogenous patient data. The method converts data into patient similarity networks at the level of features. Feature selection identifies features of predictive value to each class. Methods are provided for versatile predictor design and performance evaluation using standard measures. netDx natively groups molecular data into pathway-level features and connects with Cytoscape for network visualization of pathway themes. For method details see: Pai et al. (2019). netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology. 15, e8497 biocViews: Classification, BiomedicalInformatics, Network, SystemsBiology Author: Shraddha Pai [aut, cre] (ORCID: ), Philipp Weber [aut], Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Anne Nøhr [ctb], Indy Ng [ctb], Ruth Isserlin [aut], Hussam Kaka [aut], Gary Bader [aut] Maintainer: Shraddha Pai URL: http://netdx.org VignetteBuilder: knitr PackageStatus: Deprecated Package: ReactomeContentService4R Version: 1.15.0 Imports: httr, jsonlite, utils, magick (>= 2.5.1), data.table, doParallel, foreach, parallel Suggests: pdftools, testthat, knitr, rmarkdown License: Apache License (>= 2.0) | file LICENSE Title: Interface for the Reactome Content Service Description: Reactome is a free, open-source, open access, curated and peer-reviewed knowledgebase of bio-molecular pathways. This package is to interact with the Reactome Content Service API. Pre-built functions would allow users to retrieve data and images that consist of proteins, pathways, and other molecules related to a specific gene or entity in Reactome. biocViews: DataImport, Pathways, Reactome Author: Chi-Lam Poon [aut, cre] (ORCID: ), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeContentService4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeContentService4R/issues Package: SOMNiBUS Version: 1.15.0 Depends: R (>= 4.1.0) Imports: Matrix, mgcv, stats, VGAM, IRanges, GenomeInfoDb, GenomicRanges, rtracklayer, S4Vectors, BiocManager, annotatr, yaml, utils, bsseq, reshape2, data.table, ggplot2, tidyr, Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE Title: Smooth modeling of bisulfite sequencing Description: This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture. biocViews: DNAMethylation, Regression, Epigenetics, DifferentialMethylation, Sequencing, FunctionalPrediction Author: Kaiqiong Zhao [aut], Kathleen Klein [cre], Audrey Lemaçon [ctb, ctr], Simon Laurin-Lemay [ctb, ctr], My Intelligent Machines Inc. [ctr], Celia Greenwood [ths, aut] Maintainer: Kathleen Klein URL: https://github.com/kaiqiong/SOMNiBUS VignetteBuilder: knitr BugReports: https://github.com/kaiqiong/SOMNiBUS/issues Package: diffUTR Version: 1.15.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq, GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap, ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr, matrixStats, IRanges, ensembldb, viridisLite Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Title: diffUTR: Streamlining differential exon and 3' UTR usage Description: The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR Package: ReactomeGraph4R Version: 1.15.0 Depends: R (>= 4.1) Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table, rlang, ReactomeContentService4R, doParallel, parallel, foreach Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork, wesanderson License: Apache License (>= 2) Title: Interface for the Reactome Graph Database Description: Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries. biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork Author: Chi-Lam Poon [aut, cre] (ORCID: ), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeGraph4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGraph4R/issues Package: benchdamic Version: 1.13.2 Depends: R (>= 4.3.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST, Seurat, ANCOMBC, microbiome, mixOmics, lme4, NOISeq, dearseq, MicrobiomeStat, Maaslin2, GUniFrac, metagenomeSeq, MGLM, ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges, graphics, cowplot, grDevices, tidytext Suggests: knitr, rmarkdown, kableExtra, BiocStyle, magick, SPsimSeq, testthat License: Artistic-2.0 Title: Benchmark of differential abundance methods on microbiome data Description: Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization. biocViews: Metagenomics, Microbiome, DifferentialExpression, MultipleComparison, Normalization, Preprocessing, Software Author: Matteo Calgaro [aut, cre] (ORCID: ), Chiara Romualdi [aut] (ORCID: ), Davide Risso [aut] (ORCID: ), Nicola Vitulo [aut] (ORCID: ) Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues Package: CBEA Version: 1.7.0 Depends: R (>= 4.2.0) Imports: BiocParallel, BiocSet, dplyr, lmom, fitdistrplus, magrittr, methods, mixtools, Rcpp (>= 1.0.7), stats, SummarizedExperiment, tibble, TreeSummarizedExperiment, tidyr, glue, generics, rlang, goftest LinkingTo: Rcpp Suggests: phyloseq, BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), tidyverse, roxygen2, mia, purrr License: MIT + file LICENSE Title: Competitive Balances for Taxonomic Enrichment Analysis in R Description: This package implements CBEA, a method to perform set-based analysis for microbiome relative abundance data. This approach constructs a competitive balance between taxa within the set and remainder taxa per sample. More details can be found in the Nguyen et al. 2021+ manuscript. Additionally, this package adds support functions to help users perform taxa-set enrichment analyses using existing gene set analysis methods. In the future we hope to also provide curated knowledge driven taxa sets. biocViews: Software, Microbiome, Metagenomics, GeneSetEnrichment, DataImport Author: Quang Nguyen [aut, cre] (ORCID: ) Maintainer: Quang Nguyen URL: https://github.com/qpmnguyen/CBEA, https://qpmnguyen.github.io/CBEA/ VignetteBuilder: knitr BugReports: https://github.com/qpmnguyen/CBEA//issues Package: pareg Version: 1.11.1 Depends: R (>= 4.2), tensorflow (>= 2.2.0), tfprobability (>= 0.10.0) Imports: stats, tidyr, purrr, future, doFuture, foreach, doRNG, tibble, glue, tidygraph, igraph, proxy, dplyr, magrittr, ggplot2, ggraph, rlang, progress, Matrix, keras, nloptr, ggrepel, methods, DOSE, stringr, reticulate, logger, hms, devtools, basilisk Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, plotROC, PRROC, mgsa, topGO, msigdbr, betareg, fgsea, ComplexHeatmap, GGally, ggsignif, circlize, enrichplot, ggnewscale, tidyverse, cowplot, ggfittext, simplifyEnrichment, GSEABenchmarkeR, BiocParallel, ggupset, latex2exp, org.Hs.eg.db, GO.db License: GPL-3 Title: Pathway enrichment using a regularized regression approach Description: Compute pathway enrichment scores while accounting for term-term relations. This package uses a regularized multiple linear regression to regress differential expression p-values obtained from multi-condition experiments on a pathway membership matrix. By doing so, it is able to incorporate additional biological knowledge into the enrichment analysis and to estimate pathway enrichment scores more robustly. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network Author: Kim Philipp Jablonski [aut, cre] (ORCID: ) Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/pareg VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/pareg/issues PackageStatus: Deprecated Package: STdeconvolve Version: 1.11.1 Depends: R (>= 4.1) Imports: topicmodels, BiocParallel, Matrix, methods, mgcv, ggplot2, scatterpie, viridis, slam, stats, clue, liger, reshape2, graphics, grDevices, utils Suggests: knitr, BiocStyle, rmarkdown, testthat, rcmdcheck, gplots, gridExtra, hash, dplyr, parallel License: GPL-3 Title: Reference-free Cell-Type Deconvolution of Multi-Cellular Spatially Resolved Transcriptomics Data Description: STdeconvolve as an unsupervised, reference-free approach to infer latent cell-type proportions and transcriptional profiles within multi-cellular spatially-resolved pixels from spatial transcriptomics (ST) datasets. STdeconvolve builds on latent Dirichlet allocation (LDA), a generative statistical model commonly used in natural language processing for discovering latent topics in collections of documents. In the context of natural language processing, given a count matrix of words in documents, LDA infers the distribution of words for each topic and the distribution of topics in each document. In the context of ST data, given a count matrix of gene expression in multi-cellular ST pixels, STdeconvolve applies LDA to infer the putative transcriptional profile for each cell-type and the proportional representation of each cell-type in each multi-cellular ST pixel. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression Author: Brendan Miller [aut, cre] (ORCID: ), Jean Fan [aut] (ORCID: ) Maintainer: Brendan Miller URL: https://jef.works/STdeconvolve/ VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/STdeconvolve/issues PackageStatus: Deprecated Package: PanViz Version: 1.9.1 Depends: R (>= 4.2.0) Imports: tidyr, stringr, dplyr, tibble, magrittr, futile.logger, utils, easycsv, rentrez, igraph, RColorBrewer, data.table, colorspace, grDevices, rlang, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, networkD3, License: Artistic-2.0 Title: Integrating Multi-Omic Network Data With Summay-Level GWAS Data Description: This pacakge integrates data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) with summary-level genome-wide association (GWAS) data, such as that provided by the GWAS Catalog or GWAS Central databases, or a user's own study or dataset, in order to produce biological networks, termed IMONs (Integrated Multi-Omic Networks). IMONs can be used to analyse trait-specific polymorphic data within the context of biochemical and metabolic reaction networks, providing greater biological interpretability for GWAS data. biocViews: GenomeWideAssociation, Reactome, Metabolomics, SNP, GraphAndNetwork, Network, KEGG Author: Luca Anholt [cre, aut] Maintainer: Luca Anholt URL: https://github.com/LucaAnholt/PanViz VignetteBuilder: knitr BugReports: https://github.com/LucaAnholt/PanViz/issues PackageStatus: Deprecated Package: signifinder Version: 1.9.1 Depends: R (>= 4.3.0) Imports: AnnotationDbi, BiocGenerics, ComplexHeatmap, consensusOV, cowplot, DGEobj.utils, dplyr, ensembldb, ggplot2, ggridges, GSVA, IRanges, magrittr, matrixStats, maxstat, methods, openair, org.Hs.eg.db, patchwork, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, sparrow, SpatialExperiment, stats, SummarizedExperiment, survival, survminer, viridis Suggests: BiocStyle, edgeR, grid, kableExtra, knitr, limma, testthat (>= 3.0.0) License: AGPL-3 Title: Collection and implementation of public transcriptional cancer signatures Description: signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes. biocViews: GeneExpression, GeneTarget, ImmunoOncology, BiomedicalInformatics, RNASeq, Microarray, ReportWriting, Visualization, SingleCell, Spatial, GeneSignaling Author: Stefania Pirrotta [cre, aut] (ORCID: ), Enrica Calura [aut] (ORCID: ) Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/signifinder VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/signifinder/issues PackageStatus: Deprecated Package: coMET Version: 1.39.0 Depends: R (>= 4.1.0), grid, utils, biomaRt, Gviz, psych Imports: hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics, showtext License: GPL (>= 2) NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as lon:g as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr Package: pairedGSEA Version: 1.7.0 Depends: R (>= 4.3.0) Imports: DESeq2, DEXSeq, limma, fgsea, sva, SummarizedExperiment, S4Vectors, BiocParallel, ggplot2, aggregation, stats, utils, methods Suggests: writexl, readxl, readr, rhdf5, msigdbr, plotly, testthat (>= 3.0.0), knitr, rmarkdown, covr, BiocStyle License: MIT + file LICENSE Title: Paired DGE and DGS analysis for gene set enrichment analysis Description: pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results. biocViews: DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, ImmunoOncology, GeneSetEnrichment, Pathways, RNASeq, Software, Transcription, Author: Søren Helweg Dam [cre, aut] (ORCID: ), Lars Rønn Olsen [aut] (ORCID: ), Kristoffer Vitting-Seerup [aut] (ORCID: ) Maintainer: Søren Helweg Dam URL: https://github.com/shdam/pairedGSEA VignetteBuilder: knitr BugReports: https://github.com/shdam/pairedGSEA/issues Package: saseR Version: 1.3.0 Depends: R (>= 4.3.0) Imports: ASpli, S4Vectors, BiocGenerics, GenomicFeatures, MASS, PRROC, SummarizedExperiment, edgeR, pracma, precrec, BiocParallel, DESeq2, DEXSeq, data.table, limma, methods, GenomicRanges, GenomicAlignments, rrcov, MatrixGenerics, stats, IRanges, knitr, dplyr, igraph, parallel License: Artistic-2.0 Title: Scalable Aberrant Splicing and Expression Retrieval Description: saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}. biocViews: DifferentialExpression, DifferentialSplicing, Regression, GeneExpression, AlternativeSplicing, RNASeq, Sequencing, Software Author: Alexandre Segers [aut, cre], Jeroen Gilis [ctb], Mattias Van Heetvelde [ctb], Elfride De Baere [ctb], Lieven Clement [ctb] Maintainer: Alexandre Segers URL: https://github.com/statOmics/saseR, https://doi.org/10.1101/2023.06.29.547014 VignetteBuilder: knitr BugReports: https://github.com/statOmics/saseR/issues Package: methodical Version: 1.3.0 Depends: GenomicRanges, ggplot2, R (>= 4.0), SummarizedExperiment Imports: AnnotationHub, annotatr, BiocCheck, BiocManager, BiocParallel, BiocStyle, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, cowplot, data.table, DelayedArray, devtools, dplyr, ExperimentHub, foreach, GenomeInfoDb, HDF5Array, IRanges, knitr, MatrixGenerics, R.utils, rcmdcheck, RcppRoll, remotes, rhdf5, rtracklayer, S4Vectors, scales, tibble, tidyr, tools, TumourMethData, usethis Suggests: BSgenome.Athaliana.TAIR.TAIR9, DESeq2, methrix, rmarkdown License: GPL (>= 3) Title: Discovering genomic regions where methylation is strongly associated with transcriptional activity Description: DNA methylation is generally considered to be associated with transcriptional silencing. However, comprehensive, genome-wide investigation of this relationship requires the evaluation of potentially millions of correlation values between the methylation of individual genomic loci and expression of associated transcripts in a relatively large numbers of samples. Methodical makes this process quick and easy while keeping a low memory footprint. It also provides a novel method for identifying regions where a number of methylation sites are consistently strongly associated with transcriptional expression. In addition, Methodical enables housing DNA methylation data from diverse sources (e.g. WGBS, RRBS and methylation arrays) with a common framework, lifting over DNA methylation data between different genome builds and creating base-resolution plots of the association between DNA methylation and transcriptional activity at transcriptional start sites. biocViews: DNAMethylation, MethylationArray, Transcription, GenomeWideAssociation, Software Author: Richard Heery [aut, cre] (ORCID: ) Maintainer: Richard Heery URL: https://github.com/richardheery/methodical SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/richardheery/methodical/issues Package: SurfR Version: 1.3.5 Depends: R (>= 4.3.0) Imports: httr, BiocFileCache, BiocStyle, SPsimSeq, DESeq2, edgeR, openxlsx, stringr, rhdf5, ggplot2, ggrepel, stats, magrittr, assertr, tidyr, dplyr, TCGAbiolinks, biomaRt, metaRNASeq, scales, venn, gridExtra, SummarizedExperiment, knitr, rjson, grDevices, graphics, curl, utils Suggests: testthat (>= 3.0.0) License: GPL-3 + file LICENSE Title: Surface Protein Prediction and Identification Description: Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots. biocViews: Software, Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, PrincipalComponent, GeneSetEnrichment, Pathways, BatchEffect, FunctionalGenomics, Visualization, DataImport, FunctionalPrediction, GenePrediction, GO Author: Aurora Maurizio [aut, cre] (ORCID: ), Anna Sofia Tascini [aut, ctb] (ORCID: ) Maintainer: Aurora Maurizio URL: https://github.com/auroramaurizio/SurfR VignetteBuilder: knitr BugReports: https://github.com/auroramaurizio/SurfR/issues Package: zitools Version: 1.1.0 Depends: R (>= 4.4.0), methods Imports: phyloseq, pscl, ggplot2, MatrixGenerics, SummarizedExperiment, stats, VGAM, matrixStats, tidyr, tibble, dplyr, DESeq2, reshape2, RColorBrewer, magrittr, BiocGenerics, graphics, utils Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), tidyverse, microbiome License: BSD_3_clause + file LICENSE Title: Analysis of zero-inflated count data Description: zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging. biocViews: Software, StatisticalMethod, Microbiome Author: Carlotta Meyring [aut, cre] (ORCID: ) Maintainer: Carlotta Meyring URL: https://github.com/kreutz-lab/zitools VignetteBuilder: knitr BugReports: https://github.com/kreutz-lab/zitools/issues