Package: annotation Version: 1.30.0 Depends: R (>= 3.3.0), VariantAnnotation, AnnotationHub, Organism.dplyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.ensGene, org.Hs.eg.db, org.Mm.eg.db, Homo.sapiens, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome, TxDb.Athaliana.BioMart.plantsmart22 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 38d539ed1832dfa2ae8bc2034865cecd NeedsCompilation: no Title: Genomic Annotation Resources Description: Annotation resources make up a significant proportion of the Bioconductor project. And there are also a diverse set of online resources available which are accessed using specific packages. This walkthrough will describe the most popular of these resources and give some high level examples on how to use them. biocViews: AnnotationWorkflow, Workflow Author: Marc RJ Carlson [aut], Herve Pages [aut], Sonali Arora [aut], Valerie Obenchain [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/annotation VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotation git_branch: RELEASE_3_20 git_last_commit: e0c27c9 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/annotation_1.30.0.tar.gz vignettes: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.html, vignettes/annotation/inst/doc/Annotation_Resources.html vignetteTitles: Annotating Genomic Ranges, Genomic Annotation Resources hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.R, vignettes/annotation/inst/doc/Annotation_Resources.R dependencyCount: 122 Package: arrays Version: 1.32.0 Depends: R (>= 3.0.0) Suggests: affy, limma, hgfocuscdf, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 864b488ba4ae3d083a29dd2589f833b2 NeedsCompilation: no Title: Using Bioconductor for Microarray Analysis Description: Using Bioconductor for Microarray Analysis workflow biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/arrays git_branch: RELEASE_3_20 git_last_commit: a6fd298 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/arrays_1.32.0.tar.gz vignettes: vignettes/arrays/inst/doc/arrays.html vignetteTitles: Using Bioconductor for Microarray Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrays/inst/doc/arrays.R dependencyCount: 0 Package: BiocMetaWorkflow Version: 1.28.0 Suggests: BiocStyle, knitr, rmarkdown, BiocWorkflowTools License: Artistic-2.0 MD5sum: 5722e0caa8aff512c47588e418adc30c NeedsCompilation: no Title: BioC Workflow about publishing a Bioc Workflow Description: Bioconductor Workflow describing how to use BiocWorkflowTools to work with a single R Markdown document to submit to both Bioconductor and F1000Research. biocViews: BasicWorkflow Author: Mike Smith [aut, cre], Andrzej Oleś [aut], Wolfgang Huber [ctb] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocMetaWorkflow git_branch: RELEASE_3_20 git_last_commit: ee879cf git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/BiocMetaWorkflow_1.28.0.tar.gz vignettes: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.html vignetteTitles: Bioc Meta Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.R dependencyCount: 0 Package: BP4RNAseq Version: 1.16.0 Depends: R (>= 4.0.0) Imports: dplyr, fastqcr, stringr, tidyr, stats, utils, magrittr, reticulate Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: fc0ab6f6eb3bee9458ec96cc4a04a53f NeedsCompilation: no Title: A babysitter's package for reproducible RNA-seq analysis Description: An automated pipe for reproducible RNA-seq analysis with the minimal efforts from researchers. The package can process bulk RNA-seq data and single-cell RNA-seq data. You can only provide the taxa name and the accession id of RNA-seq data deposited in the National Center for Biotechnology Information (NCBI). After a cup of tea or longer, you will get formated gene expression data as gene count and transcript count based on both alignment-based and alignment-free workflows. biocViews: GeneExpressionWorkflow Author: Shanwen Sun [cre, aut], Lei Xu [aut], Quan Zou [aut] Maintainer: Shanwen Sun SystemRequirements: UNIX, SRA Toolkit=2.10.3, Entrez Direct=13.3, FastQC=v0.11.9, Cutadapt=2.10, datasets, SAMtools=1.9, HISAT2=2.2.0, StringTie=2.1.1, Salmon=1.2.1 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BP4RNAseq git_branch: RELEASE_3_20 git_last_commit: 64287a3 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/BP4RNAseq_1.16.0.tar.gz vignettes: vignettes/BP4RNAseq/inst/doc/vignette.html vignetteTitles: BP4RNAseq vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BP4RNAseq/inst/doc/vignette.R dependencyCount: 91 Package: CAGEWorkflow Version: 1.22.0 Depends: R (>= 3.6.0), CAGEfightR, nanotubes Suggests: knitr, magick, rmarkdown, BiocStyle, BiocWorkflowTools, pheatmap, ggseqlogo, viridis, magrittr, ggforce, ggthemes, tidyverse, dplyr, GenomicRanges, SummarizedExperiment, GenomicFeatures, BiocParallel, InteractionSet, Gviz, DESeq2, limma, edgeR, statmod, BiasedUrn, sva, TFBSTools, motifmatchr, pathview, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, JASPAR2016, png License: GPL-3 MD5sum: 6058adc99103298e1b9ef4e39ddff52a NeedsCompilation: no Title: A step-by-step guide to analyzing CAGE data using R/Bioconductor Description: Workflow for analyzing Cap Analysis of Gene Expression (CAGE) data using R/Bioconductor. biocViews: GeneExpressionWorkflow, AnnotationWorkflow Author: Malte Thodberg [aut, cre] Maintainer: Malte Thodberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEWorkflow git_branch: RELEASE_3_20 git_last_commit: 5c2322c git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/CAGEWorkflow_1.22.0.tar.gz vignettes: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.html vignetteTitles: CAGEWorkflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.R dependencyCount: 166 Package: chipseqDB Version: 1.30.0 Suggests: chipseqDBData, BiocStyle, BiocFileCache, ChIPpeakAnno, Gviz, Rsamtools, TxDb.Mmusculus.UCSC.mm10.knownGene, csaw, edgeR, knitr, org.Mm.eg.db, rtracklayer, rmarkdown License: Artistic-2.0 MD5sum: 1d968a679efa162e9c2ab075e8dead18 NeedsCompilation: no Title: A Bioconductor Workflow to Detect Differential Binding in ChIP-seq Data Description: Describes a computational workflow for performing a DB analysis with sliding windows. The aim is to facilitate the practical implementation of window-based DB analyses by providing detailed code and expected output. The workflow described here applies to any ChIP-seq experiment with multiple experimental conditions and multiple biological samples in one or more of the conditions. It detects and summarizes DB regions between conditions in a de novo manner, i.e., without making any prior assumptions about the location or width of bound regions. Detected regions are then annotated according to their proximity to genes. biocViews: ImmunoOncologyWorkflow, Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/chipseqDB/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseqDB git_branch: RELEASE_3_20 git_last_commit: fd8bc68 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/chipseqDB_1.30.0.tar.gz vignettes: vignettes/chipseqDB/inst/doc/cbp.html, vignettes/chipseqDB/inst/doc/h3k27me3.html, vignettes/chipseqDB/inst/doc/h3k9ac.html, vignettes/chipseqDB/inst/doc/intro.html vignetteTitles: 3. Differential binding of CBP in fibroblasts, 4. Differential enrichment of H3K27me3 in lung epithelium, 2. Differential enrichment of H3K9ac in B cells, 1. Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseqDB/inst/doc/cbp.R dependencyCount: 0 Package: csawUsersGuide Version: 1.22.0 Suggests: knitr, BiocStyle, BiocManager License: GPL-3 MD5sum: 9b846d5964a5bc954f83779afbffc996 NeedsCompilation: no Title: csaw User's Guide Description: A user's guide for the csaw package for detecting differentially bound regions in ChIP-seq data. Describes how to read in BAM files to obtain a per-window count matrix, filtering to obtain high-abundance windows of interest, normalization of sample-specific biases, testing for differential binding, consolidation of per-window results to obtain per-region statistics, and annotation and visualization of the DB results. biocViews: Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csawUsersGuide git_branch: RELEASE_3_20 git_last_commit: ee4d46c git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/csawUsersGuide_1.22.0.tar.gz vignettes: vignettes/csawUsersGuide/inst/doc/csaw.pdf vignetteTitles: User's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csawUsersGuide/inst/doc/csaw.R dependencyCount: 0 Package: cytofWorkflow Version: 1.30.0 Depends: R (>= 3.6.0), BiocStyle, knitr, readxl, CATALYST, diffcyt, HDCytoData, uwot, cowplot Suggests: knitcitations, markdown, rmarkdown License: Artistic-2.0 MD5sum: af9b23a8b9c85e044e2f08121fcae008 NeedsCompilation: no Title: CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets Description: High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals). biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow Author: Malgorzata Nowicka [aut], Helena L. Crowell [aut], Mark D. Robinson [aut, cre] Maintainer: Mark D. Robinson URL: https://github.com/markrobinsonuzh/cytofWorkflow VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/cytofWorkflow/issues git_url: https://git.bioconductor.org/packages/cytofWorkflow git_branch: RELEASE_3_20 git_last_commit: 39a5d29 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/cytofWorkflow_1.30.0.tar.gz vignettes: vignettes/cytofWorkflow/inst/doc/cytofWorkflow.html vignetteTitles: A workflow for differential discovery in high-throughput high-dimensional cytometry datasets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 228 Package: EGSEA123 Version: 1.30.0 Depends: R (>= 3.4.0), EGSEA (>= 1.5.2), limma (>= 3.49.2), edgeR, illuminaio Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: a1ae6f35b2b72d549bdb5c52ee41e6d1 NeedsCompilation: no Title: Easy and efficient ensemble gene set testing with EGSEA Description: R package that supports the workflow article `Easy and efficient ensemble gene set testing with EGSEA', Alhamdoosh et al. (2017), F1000Research, 6:2010. biocViews: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow Author: Monther Alhamdoosh, Charity Law, Luyi Tian, Julie Sheridan, Milica Ng and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://f1000research.com/articles/6-2010 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA123 git_branch: RELEASE_3_20 git_last_commit: bb5a27e git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/EGSEA123_1.30.0.tar.gz vignettes: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.html vignetteTitles: Easy and efficient ensemble gene set testing with EGSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.R dependencyCount: 203 Package: ExpHunterSuite Version: 1.14.0 Depends: R (>= 4.1.0) Imports: ReactomePA, limma, edgeR, NOISeq, biomaRt, topGO, diffcoexp, DT, ggplot2, stringr, WGCNA, dplyr, AnnotationDbi, BiocGenerics, enrichplot, rmarkdown, stats, Biobase, DESeq2, ROCR, data.table, knitr, magrittr, SummarizedExperiment, miRBaseVersions.db, grDevices, graphics, utils, BiocParallel, MKinfer, matrixStats, ggupset, rlang, plyr, tidyr, GO.db, Matrix, fastcluster, DOSE, heatmaply, EnhancedVolcano, ggrepel, clusterProfiler, GenomicRanges, GenomicFeatures, tximport, annotatr, ggridges, FactoInvestigate, FactoMineR Suggests: optparse, PerformanceAnalytics, naivebayes, reshape2, org.Hs.eg.db, org.Mm.eg.db, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 8b6801c707b0c09025497440976d8c00 NeedsCompilation: no Title: Package For The Comprehensive Analysis Of Transcriptomic Data Description: The ExpHunterSuite implements a comprehensive protocol for the analysis of transcriptional data using established *R* packages and combining their results. It covers all key steps in DEG detection, CEG detection and functional analysis for RNA-seq data. It has been implemented as an R package containing functions that can be run interactively. In addition, it also contains scripts that wrap the functions and can be run directly from the command line. biocViews: GeneExpressionWorkflow Author: James Perkins [aut, cre] (), Pedro Seoane Zonjic [aut] (), Fernando Moreno Jabato [aut] (), José Córdoba Caballero [aut] (), Elena Rojano Rivera [aut] (), Rocio Bautista Moreno [aut] (), M. Gonzalo Claros [aut] (), Isabel Gonzalez Gayte [aut], Juan Antonio García Ranea [aut] () Maintainer: James Perkins VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpHunterSuite git_branch: RELEASE_3_20 git_last_commit: bee5918 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/ExpHunterSuite_1.14.0.tar.gz vignettes: vignettes/ExpHunterSuite/inst/doc/hunter.html vignetteTitles: The Expression Hunter Suite hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExpHunterSuite/inst/doc/hunter.R dependencyCount: 297 Package: ExpressionNormalizationWorkflow Version: 1.32.0 Imports: Biobase (>= 2.24.0), limma (>= 3.20.9), lme4 (>= 1.1.7), matrixStats (>= 0.10.3), pvca (>= 1.4.0), snm (>= 1.12.0), sva (>= 3.10.0), vsn (>= 3.32.0) Suggests: knitr, BiocStyle License: GPL (>=3) MD5sum: 9ae688ba3a55e9499c1463b535dbadde NeedsCompilation: no Title: Gene Expression Normalization Workflow Description: An extensive, customized expression normalization workflow incorporating Supervised Normalization of Microarryas(SNM), Surrogate Variable Analysis(SVA) and Principal Variance Component Analysis to identify batch effects and remove them from the expression data to enhance the ability to detect the underlying biological signals. biocViews: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow Author: Karthikeyan Murugesan [aut, cre], Greg Gibson [sad, ths] Maintainer: Karthikeyan Murugesan VignetteBuilder: knitr BugReports: https://github.com/ git_url: https://git.bioconductor.org/packages/ExpressionNormalizationWorkflow git_branch: RELEASE_3_20 git_last_commit: 2f5f1e6 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/ExpressionNormalizationWorkflow_1.32.0.tar.gz vignettes: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.html vignetteTitles: Gene Expression Normalization Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.R dependencyCount: 103 Package: fluentGenomics Version: 1.18.0 Depends: R (>= 4.0) Imports: plyranges (>= 1.7.7), dplyr, SummarizedExperiment, readr, stats, utils Suggests: knitr, rmarkdown, bookdown, rappdirs, BiocFileCache, DESeq2, limma, ggplot2, tidyr, tximeta (>= 1.4.2), macrophage (>= 1.2.0), License: MIT + file LICENSE MD5sum: 8b1094d60ef003502ec90ac51b269460 NeedsCompilation: no Title: A plyranges and tximeta workflow Description: An extended workflow using the plyranges and tximeta packages for fluent genomic data analysis. Use tximeta to correctly import RNA-seq transcript quantifications and summarize them to gene counts for downstream analysis. Use plyranges for clearly expressing operations over genomic coordinates and to combine results from differential expression and differential accessibility analyses. biocViews: Workflow, BasicWorkflow, GeneExpressionWorkflow Author: Stuart Lee [aut, cre] (), Michael Love [aut, ctb] Maintainer: Stuart Lee URL: https://github.com/sa-lee/fluentGenomics VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/sa-lee/fluentGenomics/issues git_url: https://git.bioconductor.org/packages/fluentGenomics git_branch: RELEASE_3_20 git_last_commit: 51c0b4f git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/fluentGenomics_1.18.0.tar.gz vignettes: vignettes/fluentGenomics/inst/doc/fluentGenomics.html vignetteTitles: fluentGenomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fluentGenomics/inst/doc/fluentGenomics.R dependencyCount: 83 Package: generegulation Version: 1.30.0 Depends: R (>= 3.3.0), BSgenome.Scerevisiae.UCSC.sacCer3, Biostrings, GenomicFeatures, MotifDb, S4Vectors, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, motifStack, org.Sc.sgd.db, seqLogo Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f082cf8d381558804023a760c5e59dca NeedsCompilation: no Title: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching Description: The binding of transcription factor proteins (TFs) to DNA promoter regions upstream of gene transcription start sites (TSSs) is one of the most important mechanisms by which gene expression, and thus many cellular processes, are controlled. Though in recent years many new kinds of data have become available for identifying transcription factor binding sites (TFBSs) -- ChIP-seq and DNase I hypersensitivity regions among them -- sequence matching continues to play an important role. In this workflow we demonstrate Bioconductor techniques for finding candidate TF binding sites in DNA sequence using the model organism Saccharomyces cerevisiae. The methods demonstrated here apply equally well to other organisms. biocViews: Workflow, EpigeneticsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/generegulation/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/generegulation git_branch: RELEASE_3_20 git_last_commit: 46eab17 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/generegulation_1.30.0.tar.gz vignettes: vignettes/generegulation/inst/doc/generegulation.html vignetteTitles: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/generegulation/inst/doc/generegulation.R dependencyCount: 154 Package: GeoMxWorkflows Version: 1.12.0 Depends: R (>= 4.0), NanoStringNCTools, GeomxTools Imports: Biobase, S4Vectors, rjson, readxl, EnvStats, dplyr, reshape2, methods, utils, stats, data.table, outliers, BiocGenerics, ggplot2, ggrepel, ggforce, cowplot, scales, umap, Rtsne, pheatmap, BiocStyle, networkD3 Suggests: rmarkdown, knitr License: MIT MD5sum: 9ced66d7091146c958b3a0dbbd71ac5e NeedsCompilation: no Title: GeoMx Digital Spatial Profiler (DSP) data analysis workflows Description: Workflows for use with NanoString Technologies GeoMx Technology. Package provides bioconductor focused workflows for leveraging existing packages (e.g. GeomxTools) to process, QC, and analyze the data. biocViews: GeneExpressionWorkflow, ImmunoOncologyWorkflow, SpatialWorkflow Author: Maddy Griswold [cre, aut], Jason Reeves [aut], Prajan Divakar [aut], Nicole Ortogero [aut], Zhi Yang [aut], Stephanie Zimmerman [aut], Rona Vitancol [aut], David Henderson [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeoMxWorkflows git_branch: RELEASE_3_20 git_last_commit: ca39c86 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/GeoMxWorkflows_1.12.0.tar.gz vignettes: vignettes/GeoMxWorkflows/inst/doc/GeomxTools_RNA-NGS_Analysis.html vignetteTitles: Analyzing GeoMx-NGS Data with GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoMxWorkflows/inst/doc/GeomxTools_RNA-NGS_Analysis.R dependencyCount: 148 Package: highthroughputassays Version: 1.30.0 Depends: R (>= 3.3.0), flowCore, flowStats, flowWorkspace Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b5754024a9ecb7e6103aa2316da75a97 NeedsCompilation: no Title: Using Bioconductor with High Throughput Assays Description: The workflow illustrates use of the flow cytometry packages to load, transform and visualize the flow data and gate certain populations in the dataset. The workflow loads the `flowCore`, `flowStats` and `flowWorkspace` packages and its dependencies. It loads the ITN data with 15 samples, each of which includes, in addition to FSC and SSC, 5 fluorescence channels: CD3, CD4, CD8, CD69 and HLADR. biocViews: ImmunoOncologyWorkflow, Workflow, ProteomicsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/highthroughputassays/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/highthroughputassays git_branch: RELEASE_3_20 git_last_commit: 45c7bdc git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/highthroughputassays_1.30.0.tar.gz vignettes: vignettes/highthroughputassays/inst/doc/high-throughput-assays.html vignetteTitles: Using Bioconductor with High Throughput Assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/highthroughputassays/inst/doc/high-throughput-assays.R dependencyCount: 102 Package: liftOver Version: 1.30.0 Depends: R (>= 3.3.0), gwascat, GenomicRanges, rtracklayer, Homo.sapiens, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 96d602fc58cadb59cbe5dd334d60d72c NeedsCompilation: no Title: Changing genomic coordinate systems with rtracklayer::liftOver Description: The liftOver facilities developed in conjunction with the UCSC browser track infrastructure are available for transforming data in GRanges formats. This is illustrated here with an image of the EBI/NHGRI GWAS catalog that is, as of May 10 2017, distributed with coordinates defined by NCBI build hg38. biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/liftOver/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/liftOver git_branch: RELEASE_3_20 git_last_commit: abb006b git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/liftOver_1.30.0.tar.gz vignettes: vignettes/liftOver/inst/doc/liftov.html vignetteTitles: Changing genomic coordinate systems with rtracklayer::liftOver hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/liftOver/inst/doc/liftov.R dependencyCount: 122 Package: maEndToEnd Version: 2.26.0 Depends: R (>= 3.5.0), Biobase, oligoClasses, ArrayExpress, pd.hugene.1.0.st.v1, hugene10sttranscriptcluster.db, oligo, arrayQualityMetrics, limma, topGO, ReactomePA, clusterProfiler, gplots, ggplot2, geneplotter, pheatmap, RColorBrewer, dplyr, tidyr, stringr, matrixStats, genefilter, openxlsx, Rgraphviz, enrichplot Suggests: BiocStyle, knitr, devtools, rmarkdown License: MIT + file LICENSE MD5sum: c95804388a19f03292ecd607fae9060c NeedsCompilation: no Title: An end to end workflow for differential gene expression using Affymetrix microarrays Description: In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene" type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis. biocViews: GeneExpressionWorkflow Author: Bernd Klaus [aut], Stefanie Reisenauer [aut, cre] Maintainer: Stefanie Reisenauer URL: https://www.bioconductor.org/help/workflows/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/maEndToEnd git_branch: RELEASE_3_20 git_last_commit: 69484ff git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/maEndToEnd_2.26.0.tar.gz vignettes: vignettes/maEndToEnd/inst/doc/MA-Workflow.html vignetteTitles: An end to end workflow for differential gene expression using Affymetrix microarrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maEndToEnd/inst/doc/MA-Workflow.R dependencyCount: 217 Package: methylationArrayAnalysis Version: 1.29.0 Depends: R (>= 3.3.0), knitr, rmarkdown, BiocStyle, limma, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, RColorBrewer, missMethyl, matrixStats, minfiData, Gviz, DMRcate, stringr, FlowSorted.Blood.450k License: Artistic-2.0 MD5sum: 502cab1033c9ccf24bee83d698470f57 NeedsCompilation: no Title: A cross-package Bioconductor workflow for analysing methylation array data. Description: Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This Bioconductor workflow uses multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data. biocViews: Workflow, EpigeneticsWorkflow Author: Jovana Maksimovic [aut, cre] Maintainer: Jovana Maksimovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylationArrayAnalysis git_branch: devel git_last_commit: a9a1a87 git_last_commit_date: 2024-04-30 Date/Publication: 2024-04-30 source.ver: src/contrib/methylationArrayAnalysis_1.29.0.tar.gz vignettes: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.html vignetteTitles: A cross-package Bioconductor workflow for analysing methylation array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.R dependencyCount: 229 Package: recountWorkflow Version: 1.30.0 Depends: R (>= 3.6.0) Imports: recount, GenomicRanges, limma, edgeR, DESeq2, pheatmap, regionReport, clusterProfiler, org.Hs.eg.db, gplots, derfinder, GenomicState, bumphunter, derfinderPlot Suggests: BiocStyle, BiocWorkflowTools, knitr, magick, sessioninfo, rmarkdown License: Artistic-2.0 MD5sum: 96d559d5b68ec31262af510700600681 NeedsCompilation: no Title: recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor Description: The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data. biocViews: Workflow, ResourceQueryingWorkflow Author: Leonardo Collado-Torres [aut, cre], Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recountWorkflow VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recountWorkflow/ git_url: https://git.bioconductor.org/packages/recountWorkflow git_branch: RELEASE_3_20 git_last_commit: 71c82c8 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/recountWorkflow_1.30.0.tar.gz vignettes: vignettes/recountWorkflow/inst/doc/recount-workflow.html vignetteTitles: recount workflow: accessing over 70,,000 human RNA-seq samples with Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountWorkflow/inst/doc/recount-workflow.R dependencyCount: 240 Package: RNAseq123 Version: 1.30.0 Depends: R (>= 3.3.0), Glimma (>= 1.1.9), limma, edgeR, gplots, RColorBrewer, Mus.musculus, R.utils, TeachingDemos, statmod, BiocWorkflowTools Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 8e89b0fb49b37097ad3ec0383134ea3e NeedsCompilation: no Title: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR Description: R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016). biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Charity Law, Monther Alhamdoosh, Shian Su, Xueyi Dong, Luyi Tian, Gordon Smyth and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://f1000research.com/articles/5-1408/v3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAseq123 git_branch: RELEASE_3_20 git_last_commit: 519935d git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/RNAseq123_1.30.0.tar.gz vignettes: vignettes/RNAseq123/inst/doc/designmatrices.html, vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.html, vignettes/RNAseq123/inst/doc/limmaWorkflow.html vignetteTitles: A guide to creating design matrices for gene expression experiments (English version), RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (Chinese version), RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (English version) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseq123/inst/doc/designmatrices.R, vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.R, vignettes/RNAseq123/inst/doc/limmaWorkflow.R dependencyCount: 170 Package: rnaseqDTU Version: 1.26.0 Depends: R (>= 3.5.0), DRIMSeq, DEXSeq, stageR, DESeq2, edgeR, rafalib, devtools Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 39e3d826423ab2cb9454c78f86ac4c72 NeedsCompilation: no Title: RNA-seq workflow for differential transcript usage following Salmon quantification Description: RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Michael Love [aut, cre], Charlotte Soneson [aut], Rob Patro [aut] Maintainer: Michael Love URL: https://github.com/thelovelab/rnaseqDTU/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqDTU git_branch: RELEASE_3_20 git_last_commit: a4bd01b git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/rnaseqDTU_1.26.0.tar.gz vignettes: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.html vignetteTitles: RNA-seq workflow for differential transcript usage following Salmon quantification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.R dependencyCount: 186 Package: rnaseqGene Version: 1.30.0 Depends: R (>= 3.3.0), BiocStyle, airway (>= 1.5.3), tximeta, magrittr, DESeq2, apeglm, vsn, dplyr, ggplot2, hexbin, pheatmap, RColorBrewer, PoiClaClu, glmpca, ggbeeswarm, genefilter, AnnotationDbi, org.Hs.eg.db, Gviz, sva, RUVSeq, fission Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 2a3e5ba7a278064ef25286fd8b03706d NeedsCompilation: no Title: RNA-seq workflow: gene-level exploratory analysis and differential expression Description: Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Michael Love [aut, cre] Maintainer: Michael Love URL: https://github.com/thelovelab/rnaseqGene/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqGene git_branch: RELEASE_3_20 git_last_commit: cf0c3fd git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/rnaseqGene_1.30.0.tar.gz vignettes: vignettes/rnaseqGene/inst/doc/rnaseqGene.html vignetteTitles: RNA-seq workflow at the gene level hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqGene/inst/doc/rnaseqGene.R dependencyCount: 208 Package: RnaSeqGeneEdgeRQL Version: 1.30.0 Depends: R (>= 3.3.0), edgeR (>= 4.3.6), gplots, org.Mm.eg.db, GO.db, BiocStyle Suggests: knitr, knitcitations, rmarkdown License: Artistic-2.0 MD5sum: 3c8cf4739e5e804c2b8b8b9aa3f82c2c NeedsCompilation: no Title: Gene-level RNA-seq differential expression and pathway analysis using Rsubread and the edgeR quasi-likelihood pipeline Description: This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis. The analysis includes publication quality plots, GO and KEGG analyses, and the analysis of a expression signature as generated by a prior experiment. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Yunshun Chen, Aaron Lun, Gordon Smyth Maintainer: Yunshun Chen URL: http://f1000research.com/articles/5-1438/v2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqGeneEdgeRQL git_branch: RELEASE_3_20 git_last_commit: 1be8f2e git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/RnaSeqGeneEdgeRQL_1.30.0.tar.gz vignettes: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html vignetteTitles: From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.R dependencyCount: 77 Package: seqpac Version: 1.6.0 Depends: R (>= 4.2.0) Imports: Biostrings (>= 2.46.0), foreach (>= 1.5.1), GenomicRanges (>= 1.30.3), Rbowtie (>= 1.18.0), ShortRead (>= 1.36.1), tibble (>= 3.1.2), BiocParallel (>= 1.12.0), cowplot (>= 0.9.4), data.table (>= 1.14.0), digest (>= 0.6.27), doParallel (>= 1.0.16), dplyr (>= 1.0.6), factoextra (>= 1.0.7), FactoMineR (>= 1.41), ggplot2 (>= 3.3.3), IRanges (>= 2.12.0), parallel (>= 3.4.4), reshape2 (>= 1.4.4), rtracklayer (>= 1.38.3), stringr (>= 1.4.0), stats (>= 3.4.4), methods, S4Vectors Suggests: benchmarkme (>= 0.6.0), DESeq2 (>= 1.18.1), GenomeInfoDb (>= 1.14.0), gginnards (>= 0.0.2), qqman (>= 0.1.8), rmarkdown, BiocStyle, knitr, testthat, UpSetR (>= 1.4.0), venneuler, R.utils, bigreadr, readr, vroom License: GPL-3 MD5sum: 1b47a108989e9a510609e8cda4236a9a NeedsCompilation: no Title: Seqpac: A Framework for smallRNA analysis in R using Sequence-Based Counts Description: Seqpac provides functions and workflows for analysis of short sequenced reads. It was originally developed for small RNA analysis, but can be implemented on any sequencing raw data (provided as a fastq-file), where the unit of measurement is counts of unique sequences. The core of the seqpac workflow is the generation and subsequence analysis/visualization of a standardized object called PAC. Using an innovative targeting system, Seqpac process, analyze and visualize sample or sequence group differences using the PAC object. A PAC object in its most basic form is a list containing three types of data frames. - Phenotype table (P): Sample names (rows) with associated metadata (columns) e.g. treatment. - Annotation table (A): Unique sequences (rows) with annotation (columns), eg. reference alignments. - Counts table (C): Counts of unique sequences (rows) for each sample (columns). The PAC-object follows the rule: - Row names in P must be identical with column names in C. - Row names in A must be identical with row names in C. Thus P and A describes the columns and rows in C, respectively. The targeting system, will either target specific samples in P (pheno_target) or sequences in A (anno_target) and group them according to a target column in P and A, respectively (see vignettes for more details). biocViews: Workflow, BasicWorkflow, GeneExpressionWorkflow, EpigeneticsWorkflow, AnnotationWorkflow Author: Daniel Natt [aut, cre, fnd], Lovisa Örkenby [ctb], Signe Skog [ctb], Anita Öst [aut, fnd] Maintainer: Daniel Natt URL: https://github.com/Danis102/seqpac VignetteBuilder: knitr BugReports: https://github.com/Danis102/seqpac/issues git_url: https://git.bioconductor.org/packages/seqpac git_branch: RELEASE_3_20 git_last_commit: 7c56287 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/seqpac_1.6.0.tar.gz vignettes: vignettes/seqpac/inst/doc/seqpac_-_A_guide_to_sRNA_analysis_using_sequence-based_counts.html vignetteTitles: A guide to small RNA analysis using Seqpac hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqpac/inst/doc/seqpac_-_A_guide_to_sRNA_analysis_using_sequence-based_counts.R dependencyCount: 176 Package: sequencing Version: 1.30.0 Depends: R (>= 3.3.0), GenomicRanges, GenomicAlignments, Biostrings, Rsamtools, ShortRead, BiocParallel, rtracklayer, VariantAnnotation, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, RNAseqData.HNRNPC.bam.chr14 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 556e5944a6f89d18d6e16980763a68bc NeedsCompilation: no Title: Introduction to Bioconductor for Sequence Data Description: Bioconductor enables the analysis and comprehension of high- throughput genomic data. We have a vast number of packages that allow rigorous statistical analysis of large data while keeping technological artifacts in mind. Bioconductor helps users place their analytic results into biological context, with rich opportunities for visualization. Reproducibility is an important goal in Bioconductor analyses. Different types of analysis can be carried out using Bioconductor, for example; Sequencing : RNASeq, ChIPSeq, variants, copy number etc.; Microarrays: expression, SNP, etc.; Domain specific analysis : Flow cytometry, Proteomics etc. For these analyses, one typically imports and works with diverse sequence-related file types, including fasta, fastq, BAM, gtf, bed, and wig files, among others. Bioconductor packages support import, common and advanced sequence manipulation operations such as trimming, transformation, and alignment including quality assessment. biocViews: ImmunoOncologyWorkflow, Workflow, BasicWorkflow Author: Sonali Arora [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/sequencing/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sequencing git_branch: RELEASE_3_20 git_last_commit: 0590f66 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/sequencing_1.30.0.tar.gz vignettes: vignettes/sequencing/inst/doc/sequencing.html vignetteTitles: Introduction to Bioconductor for Sequence Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sequencing/inst/doc/sequencing.R dependencyCount: 112 Package: simpleSingleCell Version: 1.30.0 Imports: utils, methods, knitr, callr, rmarkdown, CodeDepends, BiocStyle Suggests: readxl, R.utils, SingleCellExperiment, scater, scran, limma, BiocFileCache, org.Mm.eg.db License: Artistic-2.0 MD5sum: dba47b42f3e3b03cb1698e3edcb14cdb NeedsCompilation: no Title: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Description: Once a proud workflow package, this is now a shell of its former self. Almost all of its content has been cannibalized for use in the "Orchestrating Single-Cell Analyses with Bioconductor" book at https://osca.bioconductor.org. Most vignettes here are retained as reminders of the glory that once was, also providing redirection for existing external links to the relevant OSCA book chapters. biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow Author: Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/simpleSingleCell/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simpleSingleCell git_branch: RELEASE_3_20 git_last_commit: d4956ae git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/simpleSingleCell_1.30.0.tar.gz vignettes: vignettes/simpleSingleCell/inst/doc/batch.html, vignettes/simpleSingleCell/inst/doc/bigdata.html, vignettes/simpleSingleCell/inst/doc/de.html, vignettes/simpleSingleCell/inst/doc/doublets.html, vignettes/simpleSingleCell/inst/doc/intro.html, vignettes/simpleSingleCell/inst/doc/misc.html, vignettes/simpleSingleCell/inst/doc/multibatch.html, vignettes/simpleSingleCell/inst/doc/qc.html, vignettes/simpleSingleCell/inst/doc/reads.html, vignettes/simpleSingleCell/inst/doc/spike.html, vignettes/simpleSingleCell/inst/doc/tenx.html, vignettes/simpleSingleCell/inst/doc/umis.html, vignettes/simpleSingleCell/inst/doc/var.html vignetteTitles: 05. Correcting batch effects, 12. Scalability for big data, 10. Detecting differential expression, 08. Detecting doublets, 01. Introduction, 13. Further analysis strategies, 11. Advanced batch correction, 06. Quality control details, 02. Read count data, 07. Spike-in normalization, 04. Droplet-based data, 03. UMI count data, 09. Advanced variance modelling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSingleCell/inst/doc/misc.R dependencyCount: 44 Package: SingscoreAMLMutations Version: 1.22.0 Depends: R (>= 4.1.0) Imports: dcanr, edgeR, ggplot2, gridExtra, GSEABase, mclust, org.Hs.eg.db, plyr, reshape2, rtracklayer, singscore, SummarizedExperiment, TCGAbiolinks, BiocFileCache Suggests: knitr, rmarkdown, BiocStyle, BiocWorkflowTools, spelling License: Artistic-2.0 MD5sum: 68332c4b81d946273b740adab62d7a07 NeedsCompilation: no Title: Using singscore to predict mutations in AML from transcriptomic signatures Description: This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis. biocViews: GeneExpressionWorkflow, GenomicVariantsWorkflow, ImmunoOncologyWorkflow, Workflow Author: Dharmesh D. Bhuva [aut, cre] (), Momeneh Foroutan [aut] (), Yi Xie [aut] (), Ruqian Lyu [aut], Malvika Kharbanda [aut] (), Joseph Cursons [aut] (), Melissa J. Davis [aut] () Maintainer: Dharmesh D. Bhuva URL: https://github.com/DavisLaboratory/SingscoreAMLMutations VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/SingscoreAMLMutations/issues git_url: https://git.bioconductor.org/packages/SingscoreAMLMutations git_branch: RELEASE_3_20 git_last_commit: 6384720 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/SingscoreAMLMutations_1.22.0.tar.gz vignettes: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.html, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.html vignetteTitles: Using singscore to predict mutations in AML from transcriptomic signatures (Chinese version), Using singscore to predict mutations in AML from transcriptomic signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.R, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.R dependencyCount: 172 Package: spicyWorkflow Version: 1.6.0 Depends: R (>= 4.3.0) Suggests: knitr, rmarkdown, BiocStyle, EBImage, cytomapper, ggplot2, ggpubr, lisaClust, spicyR, ClassifyR, scater, dplyr, simpleSeg, FuseSOM, HDF5Array, parallel, tidySingleCellExperiment, SpatialDatasets, Statial, treekoR License: GPL-3 MD5sum: a5d38b9c5dd15d9a5048b853a27086ff NeedsCompilation: no Title: Performing a Spatial Analysis of Multiplexed Tissue Imaging Data Description: We have developed an analytical framework for analysing data from high dimensional in situ cytometry assays including CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. This framework makes use of functionality from our Bioconductor packages spicyR, lisaClust, scFeatures, FuseSOM, simpleSeg and ClassifyR and contains most of the key steps which are needed to interrogate the comprehensive spatial information generated by these exciting new technologies including cell segmentation, feature normalisation, cell type identification, micro-environment characterisation, spatial hypothesis testing and patient classification. Ultimately, our modular analysis framework provides a cohesive and accessible entry point into spatially resolved single cell data analysis for any R-based bioinformatician. biocViews: Workflow, SpatialWorkflow, ImmunoOncologyWorkflow Author: Alex Qin [aut], Alexander Nicholls [aut], Nicholas Robertson [aut], Nicolas Canete [aut], Elijah Willie [aut], Ellis Patrick [aut] (), SOMS Maintainer [aut, cre] Maintainer: SOMS Maintainer URL: https://github.com/SydneyBioX/spicyWorkflow VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/spicyWorkflow/issues git_url: https://git.bioconductor.org/packages/spicyWorkflow git_branch: RELEASE_3_20 git_last_commit: 74e88c5 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/spicyWorkflow_1.6.0.tar.gz vignettes: vignettes/spicyWorkflow/inst/doc/spicyWorkflow.html vignetteTitles: "Introduction to a spicy workflow" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyWorkflow/inst/doc/spicyWorkflow.R dependencyCount: 0 Package: TCGAWorkflow Version: 1.30.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, knitr, ELMER, biomaRt, BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker, ComplexHeatmap, ggpubr, clusterProfiler, downloader (>= 0.4), GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, stringr, SummarizedExperiment, dplyr, plyr, matlab, MultiAssayExperiment, TCGAbiolinks, TCGAWorkflowData (>= 1.25.3), DT, gt License: Artistic-2.0 MD5sum: 15f5ff8c5053eb8a884bfb11a93fc833 NeedsCompilation: no Title: TCGA Workflow Analyze cancer genomics and epigenomics data using Bioconductor packages Description: Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). biocViews: Workflow, ResourceQueryingWorkflow Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Fulvio D Angelo , Gianluca Bontempi , Michele Ceccarelli , Houtan Noushmehr Maintainer: Tiago Chedraoui Silva URL: https://f1000research.com/articles/5-1542/v2 VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues git_url: https://git.bioconductor.org/packages/TCGAWorkflow git_branch: RELEASE_3_20 git_last_commit: 65abf25 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/TCGAWorkflow_1.30.0.tar.gz vignettes: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.html vignetteTitles: 'TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages' hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.R dependencyCount: 299 Package: variants Version: 1.30.0 Depends: R (>= 3.3.0), VariantAnnotation, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f0477d9273128c7dccb02393de3ce8fd NeedsCompilation: no Title: Annotating Genomic Variants Description: Read and write VCF files. Identify structural location of variants and compute amino acid coding changes for non-synonymous variants. Use SIFT and PolyPhen database packages to predict consequence of amino acid coding changes. biocViews: ImmunoOncologyWorkflow, AnnotationWorkflow, Workflow Author: Valerie Obenchain [aut], Martin Morgan [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/help/workflows/variants/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/variants git_branch: RELEASE_3_20 git_last_commit: b0ab632 git_last_commit_date: 2024-10-29 Date/Publication: 2024-11-01 source.ver: src/contrib/variants_1.30.0.tar.gz vignettes: vignettes/variants/inst/doc/Annotating_Genomic_Variants.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variants/inst/doc/Annotating_Genomic_Variants.R dependencyCount: 83