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:
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: 56267 794
#> metadata(4): merge.info pca.info experiment markers
#> assays(3): reconstructed counts logcounts
#> rownames(56267): 5-8S-rRNA 5S-rRNA ... ZZEF1 ZZZ3
#> rowData names(1): rotation
#> colnames(794): hs20151130-SC1-26 hs20151130-SC1-28 ...
#> 20200312-DS-dissected-81 20200312-DS-dissected-83
#> colData names(33): batch Sequencing_Run ... gene_snn_res.0.8
#> gene_snn_res.1
#> reducedDimNames(3): corrected TSNE UMAP
#> mainExpName: integrated
#> altExpNames(2): gene transcriptR session information.
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] chevreuldata_0.99.26 ExperimentHub_2.15.0
#> [3] AnnotationHub_3.15.0 BiocFileCache_2.15.1
#> [5] dbplyr_2.5.0 chevreulProcess_0.99.23
#> [7] scater_1.35.1 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_1.43.4
#> [15] IRanges_2.41.3 S4Vectors_0.45.4
#> [17] BiocGenerics_0.53.6 generics_0.1.3
#> [19] MatrixGenerics_1.19.1 matrixStats_1.5.0
#> [21] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] sys_3.4.3 jsonlite_1.9.0
#> [3] shape_1.4.6.1 magrittr_2.0.3
#> [5] ggbeeswarm_0.7.2 GenomicFeatures_1.59.1
#> [7] rmarkdown_2.29 GlobalOptions_0.1.2
#> [9] fs_1.6.5 BiocIO_1.17.1
#> [11] vctrs_0.6.5 memoise_2.0.1
#> [13] Rsamtools_2.23.1 DelayedMatrixStats_1.29.1
#> [15] RCurl_1.98-1.16 htmltools_0.5.8.1
#> [17] S4Arrays_1.7.3 curl_6.2.1
#> [19] BiocNeighbors_2.1.2 SparseArray_1.7.6
#> [21] sass_0.4.9 bslib_0.9.0
#> [23] cachem_1.1.0 ResidualMatrix_1.17.0
#> [25] buildtools_1.0.0 GenomicAlignments_1.43.0
#> [27] igraph_2.1.4 mime_0.12
#> [29] lifecycle_1.0.4 pkgconfig_2.0.3
#> [31] rsvd_1.0.5 Matrix_1.7-2
#> [33] R6_2.6.1 fastmap_1.2.0
#> [35] GenomeInfoDbData_1.2.13 digest_0.6.37
#> [37] colorspace_2.1-1 AnnotationDbi_1.69.0
#> [39] dqrng_0.4.1 irlba_2.3.5.1
#> [41] RSQLite_2.3.9 beachmat_2.23.6
#> [43] filelock_1.0.3 httr_1.4.7
#> [45] abind_1.4-8 compiler_4.4.2
#> [47] bit64_4.6.0-1 withr_3.0.2
#> [49] BiocParallel_1.41.2 viridis_0.6.5
#> [51] DBI_1.2.3 rappdirs_0.3.3
#> [53] DelayedArray_0.33.6 rjson_0.2.23
#> [55] bluster_1.17.0 tools_4.4.2
#> [57] vipor_0.4.7 beeswarm_0.4.0
#> [59] glue_1.8.0 restfulr_0.0.15
#> [61] batchelor_1.23.0 grid_4.4.2
#> [63] cluster_2.1.8 megadepth_1.17.0
#> [65] gtable_0.3.6 tzdb_0.4.0
#> [67] ensembldb_2.31.0 hms_1.1.3
#> [69] metapod_1.15.0 BiocSingular_1.23.0
#> [71] ScaledMatrix_1.15.0 XVector_0.47.2
#> [73] BiocVersion_3.21.1 stringr_1.5.1
#> [75] ggrepel_0.9.6 pillar_1.10.1
#> [77] limma_3.63.4 circlize_0.4.16
#> [79] dplyr_1.1.4 lattice_0.22-6
#> [81] rtracklayer_1.67.1 bit_4.5.0.1
#> [83] tidyselect_1.2.1 locfit_1.5-9.11
#> [85] maketools_1.3.2 Biostrings_2.75.4
#> [87] knitr_1.49 gridExtra_2.3
#> [89] ProtGenerics_1.39.2 edgeR_4.5.2
#> [91] cmdfun_1.0.2 xfun_0.51
#> [93] statmod_1.5.0 stringi_1.8.4
#> [95] UCSC.utils_1.3.1 EnsDb.Hsapiens.v86_2.99.0
#> [97] lazyeval_0.2.2 yaml_2.3.10
#> [99] evaluate_1.0.3 codetools_0.2-20
#> [101] tibble_3.2.1 BiocManager_1.30.25
#> [103] cli_3.6.4 munsell_0.5.1
#> [105] jquerylib_0.1.4 Rcpp_1.0.14
#> [107] png_0.1-8 XML_3.99-0.18
#> [109] parallel_4.4.2 readr_2.1.5
#> [111] blob_1.2.4 AnnotationFilter_1.31.0
#> [113] scran_1.35.0 sparseMatrixStats_1.19.0
#> [115] bitops_1.0-9 viridisLite_0.4.2
#> [117] scales_1.3.0 purrr_1.0.4
#> [119] crayon_1.5.3 rlang_1.1.5
#> [121] KEGGREST_1.47.0