chevreulPlotR is an open-source statistical environment which can be
easily modified to enhance its functionality via packages. chevreulPlot
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
chevreulPlot
by using the following commands in your R session:
The chevreulPlot
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
chevreulPlot
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
chevreulPlot tag and check the older
posts.
chevreulPlotThe chevreulPlot 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:
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 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] chevreulPlot_1.2.0 chevreulProcess_1.3.0
#> [3] scater_1.39.0 ggplot2_4.0.0
#> [5] scuttle_1.21.0 SingleCellExperiment_1.33.0
#> [7] SummarizedExperiment_1.41.0 Biobase_2.71.0
#> [9] GenomicRanges_1.63.0 Seqinfo_1.1.0
#> [11] IRanges_2.45.0 S4Vectors_0.49.0
#> [13] BiocGenerics_0.57.0 generics_0.1.4
#> [15] MatrixGenerics_1.23.0 matrixStats_1.5.0
#> [17] BiocStyle_2.39.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3
#> [3] jsonlite_2.0.0 shape_1.4.6.1
#> [5] magrittr_2.0.4 ggbeeswarm_0.7.2
#> [7] GenomicFeatures_1.63.1 farver_2.1.2
#> [9] rmarkdown_2.30 GlobalOptions_0.1.2
#> [11] fs_1.6.6 BiocIO_1.21.0
#> [13] vctrs_0.6.5 memoise_2.0.1
#> [15] Rsamtools_2.27.0 DelayedMatrixStats_1.33.0
#> [17] RCurl_1.98-1.17 forcats_1.0.1
#> [19] htmltools_0.5.8.1 S4Arrays_1.11.0
#> [21] curl_7.0.0 BiocNeighbors_2.5.0
#> [23] SparseArray_1.11.1 sass_0.4.10
#> [25] bslib_0.9.0 htmlwidgets_1.6.4
#> [27] plotly_4.11.0 cachem_1.1.0
#> [29] ResidualMatrix_1.21.0 buildtools_1.0.0
#> [31] GenomicAlignments_1.47.0 igraph_2.2.1
#> [33] iterators_1.0.14 lifecycle_1.0.4
#> [35] pkgconfig_2.0.3 rsvd_1.0.5
#> [37] Matrix_1.7-4 R6_2.6.1
#> [39] fastmap_1.2.0 clue_0.3-66
#> [41] digest_0.6.37 colorspace_2.1-2
#> [43] patchwork_1.3.2 AnnotationDbi_1.72.0
#> [45] dqrng_0.4.1 irlba_2.3.5.1
#> [47] RSQLite_2.4.4 beachmat_2.26.0
#> [49] httr_1.4.7 abind_1.4-8
#> [51] compiler_4.5.2 doParallel_1.0.17
#> [53] bit64_4.6.0-1 withr_3.0.2
#> [55] S7_0.2.0 BiocParallel_1.45.0
#> [57] viridis_0.6.5 DBI_1.2.3
#> [59] DelayedArray_0.37.0 rjson_0.2.23
#> [61] bluster_1.21.0 tools_4.5.2
#> [63] vipor_0.4.7 beeswarm_0.4.0
#> [65] glue_1.8.0 restfulr_0.0.16
#> [67] batchelor_1.26.0 grid_4.5.2
#> [69] cluster_2.1.8.1 megadepth_1.21.0
#> [71] gtable_0.3.6 tzdb_0.5.0
#> [73] tidyr_1.3.1 ensembldb_2.35.0
#> [75] data.table_1.17.8 hms_1.1.4
#> [77] metapod_1.19.0 BiocSingular_1.27.0
#> [79] ScaledMatrix_1.19.0 XVector_0.51.0
#> [81] foreach_1.5.2 stringr_1.6.0
#> [83] ggrepel_0.9.6 pillar_1.11.1
#> [85] limma_3.67.0 circlize_0.4.16
#> [87] dplyr_1.1.4 lattice_0.22-7
#> [89] rtracklayer_1.69.1 bit_4.6.0
#> [91] tidyselect_1.2.1 ComplexHeatmap_2.27.0
#> [93] locfit_1.5-9.12 maketools_1.3.2
#> [95] Biostrings_2.79.2 knitr_1.50
#> [97] gridExtra_2.3 ProtGenerics_1.43.0
#> [99] edgeR_4.9.0 cmdfun_1.0.2
#> [101] xfun_0.54 statmod_1.5.1
#> [103] stringi_1.8.7 UCSC.utils_1.7.0
#> [105] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.2
#> [107] yaml_2.3.10 evaluate_1.0.5
#> [109] codetools_0.2-20 cigarillo_1.1.0
#> [111] tibble_3.3.0 wiggleplotr_1.35.0
#> [113] BiocManager_1.30.26 cli_3.6.5
#> [115] jquerylib_0.1.4 Rcpp_1.1.0
#> [117] GenomeInfoDb_1.47.0 png_0.1-8
#> [119] XML_3.99-0.20 parallel_4.5.2
#> [121] readr_2.1.5 blob_1.2.4
#> [123] AnnotationFilter_1.34.0 scran_1.39.0
#> [125] sparseMatrixStats_1.23.0 bitops_1.0-9
#> [127] viridisLite_0.4.2 scales_1.4.0
#> [129] purrr_1.2.0 crayon_1.5.3
#> [131] GetoptLong_1.0.5 rlang_1.1.6
#> [133] KEGGREST_1.51.0