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_1.3.0 chevreulProcess_1.3.0
#> [3] scater_1.39.2 ggplot2_4.0.1
#> [5] scuttle_1.21.0 SingleCellExperiment_1.33.0
#> [7] SummarizedExperiment_1.41.0 Biobase_2.71.0
#> [9] GenomicRanges_1.63.1 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_0.7.3
#> [7] GenomicFeatures_1.63.1 farver_2.1.2
#> [9] rmarkdown_2.30 GlobalOptions_0.1.3
#> [11] fs_1.6.6 BiocIO_1.21.0
#> [13] vctrs_0.7.0 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_0.5.9 S4Arrays_1.11.1
#> [21] curl_7.0.0 BiocNeighbors_2.5.2
#> [23] SparseArray_1.11.10 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_1.0.5
#> [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_0.6.39 colorspace_2.1-2
#> [43] patchwork_1.3.2 AnnotationDbi_1.73.0
#> [45] dqrng_0.4.1 irlba_2.3.5.1
#> [47] RSQLite_2.4.5 beachmat_2.27.2
#> [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_0.2.1 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 otel_0.2.0
#> [65] beeswarm_0.4.0 glue_1.8.0
#> [67] restfulr_0.0.16 batchelor_1.27.0
#> [69] grid_4.5.2 cluster_2.1.8.1
#> [71] megadepth_1.21.0 gtable_0.3.6
#> [73] tzdb_0.5.0 tidyr_1.3.2
#> [75] ensembldb_2.35.0 data.table_1.18.0
#> [77] hms_1.1.4 metapod_1.19.1
#> [79] BiocSingular_1.27.1 ScaledMatrix_1.19.0
#> [81] XVector_0.51.0 foreach_1.5.2
#> [83] stringr_1.6.0 ggrepel_0.9.6
#> [85] pillar_1.11.1 limma_3.67.0
#> [87] circlize_0.4.17 dplyr_1.1.4
#> [89] lattice_0.22-7 rtracklayer_1.71.3
#> [91] bit_4.6.0 tidyselect_1.2.1
#> [93] ComplexHeatmap_2.27.0 locfit_1.5-9.12
#> [95] maketools_1.3.2 Biostrings_2.79.4
#> [97] knitr_1.51 gridExtra_2.3
#> [99] ProtGenerics_1.43.0 edgeR_4.9.2
#> [101] cmdfun_1.0.2 xfun_0.56
#> [103] statmod_1.5.1 stringi_1.8.7
#> [105] UCSC.utils_1.7.1 EnsDb.Hsapiens.v86_2.99.0
#> [107] lazyeval_0.2.2 yaml_2.3.12
#> [109] evaluate_1.0.5 codetools_0.2-20
#> [111] cigarillo_1.1.0 tibble_3.3.1
#> [113] wiggleplotr_1.35.0 BiocManager_1.30.27
#> [115] cli_3.6.5 jquerylib_0.1.4
#> [117] Rcpp_1.1.1 GenomeInfoDb_1.47.2
#> [119] png_0.1-8 XML_3.99-0.20
#> [121] parallel_4.5.2 readr_2.1.6
#> [123] blob_1.3.0 AnnotationFilter_1.35.0
#> [125] scran_1.39.0 sparseMatrixStats_1.23.0
#> [127] bitops_1.0-9 viridisLite_0.4.2
#> [129] scales_1.4.0 purrr_1.2.1
#> [131] crayon_1.5.3 GetoptLong_1.1.0
#> [133] rlang_1.1.7 KEGGREST_1.51.1