chevreulProcess 0.99.13
chevreulProcessR is an open-source statistical environment which can be easily modified
to enhance its functionality via packages. chevreulProcess
is a R package available via the Bioconductor
repository
for packages. R can be installed on any operating system from
CRAN after which you can install
chevreulProcess by using the following commands in your R
session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("chevreulProcess")
The chevreulProcess package is designed for single-cell RNA
sequencing data. The functions included within this package are derived from
other packages that have implemented the infrastructure needed for RNA-seq data
processing and analysis. Packages that have been instrumental in the
development of chevreulProcess include,
Biocpkg("SummarizedExperiment") and Biocpkg("scater").
R and Bioconductor have a steep learning curve so it is critical to
learn where to ask for help. The
Bioconductor support site is the main
resource for getting help: remember to use the chevreulProcess tag and check
the older posts.
chevreulProcessThe chevreulProcess package contains functions to preprocess, cluster,
visualize, and perform other analyses on scRNA-seq data. It also contains a
shiny app for easy
visualization and analysis of scRNA data.
chvereul uses SingelCellExperiment (SCE) object type
(from SingleCellExperiment)
to store expression and other metadata from single-cell experiments.
This package features functions capable of:
library("chevreulProcess")
# Load the data
library(chevreuldata)
chevreul_sce <- human_gene_transcript_sce()
chevreul_sce
#> class: SingleCellExperiment
#> dim: 9740 883
#> metadata(2): markers experiment
#> assays(3): counts logcounts scaledata
#> rownames(9740): 5-8S-rRNA A2M-AS1 ... HHIP-AS1 AC117490.2
#> rowData names(0):
#> colnames(883): ds20181001-0001 ds20181001-0002 ... ds20181001-1039
#> ds20181001-1040
#> colData names(49): orig.ident nCount_gene ... nFeature_transcript ident
#> reducedDimNames(2): PCA UMAP
#> mainExpName: gene
#> altExpNames(1): transcriptR session information.
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] chevreuldata_0.99.16 ExperimentHub_2.15.0
#> [3] AnnotationHub_3.15.0 BiocFileCache_2.15.0
#> [5] dbplyr_2.5.0 chevreulProcess_0.99.13
#> [7] scater_1.35.0 ggplot2_3.5.1
#> [9] scuttle_1.17.0 SingleCellExperiment_1.29.1
#> [11] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [13] GenomicRanges_1.59.1 GenomeInfoDb_1.43.1
#> [15] IRanges_2.41.1 S4Vectors_0.45.2
#> [17] BiocGenerics_0.53.3 generics_0.1.3
#> [19] MatrixGenerics_1.19.0 matrixStats_1.4.1
#> [21] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] shape_1.4.6.1 jsonlite_1.8.9
#> [3] magrittr_2.0.3 ggbeeswarm_0.7.2
#> [5] GenomicFeatures_1.59.1 rmarkdown_2.29
#> [7] fs_1.6.5 GlobalOptions_0.1.2
#> [9] BiocIO_1.17.0 zlibbioc_1.53.0
#> [11] vctrs_0.6.5 memoise_2.0.1
#> [13] Rsamtools_2.23.0 DelayedMatrixStats_1.29.0
#> [15] RCurl_1.98-1.16 htmltools_0.5.8.1
#> [17] S4Arrays_1.7.1 curl_6.0.1
#> [19] BiocNeighbors_2.1.0 SparseArray_1.7.2
#> [21] sass_0.4.9 bslib_0.8.0
#> [23] cachem_1.1.0 ResidualMatrix_1.17.0
#> [25] GenomicAlignments_1.43.0 igraph_2.1.1
#> [27] mime_0.12 lifecycle_1.0.4
#> [29] pkgconfig_2.0.3 rsvd_1.0.5
#> [31] Matrix_1.7-1 R6_2.5.1
#> [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
#> [35] digest_0.6.37 colorspace_2.1-1
#> [37] AnnotationDbi_1.69.0 dqrng_0.4.1
#> [39] irlba_2.3.5.1 RSQLite_2.3.7
#> [41] beachmat_2.23.1 filelock_1.0.3
#> [43] fansi_1.0.6 httr_1.4.7
#> [45] abind_1.4-8 compiler_4.5.0
#> [47] bit64_4.5.2 withr_3.0.2
#> [49] BiocParallel_1.41.0 viridis_0.6.5
#> [51] DBI_1.2.3 rappdirs_0.3.3
#> [53] DelayedArray_0.33.2 rjson_0.2.23
#> [55] bluster_1.17.0 tools_4.5.0
#> [57] vipor_0.4.7 beeswarm_0.4.0
#> [59] glue_1.8.0 restfulr_0.0.15
#> [61] batchelor_1.23.0 grid_4.5.0
#> [63] cluster_2.1.6 megadepth_1.17.0
#> [65] gtable_0.3.6 tzdb_0.4.0
#> [67] tidyr_1.3.1 ensembldb_2.31.0
#> [69] hms_1.1.3 metapod_1.15.0
#> [71] BiocSingular_1.23.0 ScaledMatrix_1.15.0
#> [73] utf8_1.2.4 XVector_0.47.0
#> [75] ggrepel_0.9.6 BiocVersion_3.21.1
#> [77] pillar_1.9.0 stringr_1.5.1
#> [79] limma_3.63.2 circlize_0.4.16
#> [81] dplyr_1.1.4 lattice_0.22-6
#> [83] rtracklayer_1.67.0 bit_4.5.0
#> [85] tidyselect_1.2.1 locfit_1.5-9.10
#> [87] Biostrings_2.75.1 knitr_1.49
#> [89] gridExtra_2.3 bookdown_0.41
#> [91] ProtGenerics_1.39.0 edgeR_4.5.0
#> [93] cmdfun_1.0.2 xfun_0.49
#> [95] statmod_1.5.0 stringi_1.8.4
#> [97] UCSC.utils_1.3.0 EnsDb.Hsapiens.v86_2.99.0
#> [99] lazyeval_0.2.2 yaml_2.3.10
#> [101] evaluate_1.0.1 codetools_0.2-20
#> [103] tibble_3.2.1 BiocManager_1.30.25
#> [105] cli_3.6.3 munsell_0.5.1
#> [107] jquerylib_0.1.4 Rcpp_1.0.13-1
#> [109] png_0.1-8 XML_3.99-0.17
#> [111] parallel_4.5.0 readr_2.1.5
#> [113] blob_1.2.4 scran_1.35.0
#> [115] AnnotationFilter_1.31.0 sparseMatrixStats_1.19.0
#> [117] bitops_1.0-9 viridisLite_0.4.2
#> [119] scales_1.3.0 purrr_1.0.2
#> [121] crayon_1.5.3 rlang_1.1.4
#> [123] KEGGREST_1.47.0