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 4.4.2 (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] chevreulPlot_0.99.34 chevreulProcess_0.99.27
#> [3] scater_1.35.1 ggplot2_3.5.1
#> [5] scuttle_1.17.0 SingleCellExperiment_1.29.1
#> [7] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [9] GenomicRanges_1.59.1 GenomeInfoDb_1.43.4
#> [11] IRanges_2.41.3 S4Vectors_0.45.4
#> [13] BiocGenerics_0.53.6 generics_0.1.3
#> [15] MatrixGenerics_1.19.1 matrixStats_1.5.0
#> [17] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3
#> [3] jsonlite_1.9.0 shape_1.4.6.1
#> [5] magrittr_2.0.3 ggbeeswarm_0.7.2
#> [7] GenomicFeatures_1.59.1 farver_2.1.2
#> [9] rmarkdown_2.29 GlobalOptions_0.1.2
#> [11] fs_1.6.5 BiocIO_1.17.1
#> [13] vctrs_0.6.5 memoise_2.0.1
#> [15] Rsamtools_2.23.1 DelayedMatrixStats_1.29.1
#> [17] RCurl_1.98-1.16 forcats_1.0.0
#> [19] htmltools_0.5.8.1 S4Arrays_1.7.3
#> [21] curl_6.2.1 BiocNeighbors_2.1.2
#> [23] SparseArray_1.7.6 sass_0.4.9
#> [25] bslib_0.9.0 htmlwidgets_1.6.4
#> [27] plotly_4.10.4 cachem_1.1.0
#> [29] ResidualMatrix_1.17.0 buildtools_1.0.0
#> [31] GenomicAlignments_1.43.0 igraph_2.1.4
#> [33] iterators_1.0.14 lifecycle_1.0.4
#> [35] pkgconfig_2.0.3 rsvd_1.0.5
#> [37] Matrix_1.7-2 R6_2.6.1
#> [39] fastmap_1.2.0 clue_0.3-66
#> [41] GenomeInfoDbData_1.2.13 digest_0.6.37
#> [43] colorspace_2.1-1 patchwork_1.3.0
#> [45] AnnotationDbi_1.69.0 dqrng_0.4.1
#> [47] irlba_2.3.5.1 RSQLite_2.3.9
#> [49] beachmat_2.23.6 httr_1.4.7
#> [51] abind_1.4-8 compiler_4.4.2
#> [53] doParallel_1.0.17 bit64_4.6.0-1
#> [55] withr_3.0.2 BiocParallel_1.41.2
#> [57] viridis_0.6.5 DBI_1.2.3
#> [59] DelayedArray_0.33.6 rjson_0.2.23
#> [61] bluster_1.17.0 tools_4.4.2
#> [63] vipor_0.4.7 beeswarm_0.4.0
#> [65] glue_1.8.0 restfulr_0.0.15
#> [67] batchelor_1.23.0 grid_4.4.2
#> [69] cluster_2.1.8 megadepth_1.17.0
#> [71] gtable_0.3.6 tzdb_0.4.0
#> [73] tidyr_1.3.1 ensembldb_2.31.0
#> [75] data.table_1.17.0 hms_1.1.3
#> [77] metapod_1.15.0 BiocSingular_1.23.0
#> [79] ScaledMatrix_1.15.0 XVector_0.47.2
#> [81] foreach_1.5.2 stringr_1.5.1
#> [83] ggrepel_0.9.6 pillar_1.10.1
#> [85] limma_3.63.5 circlize_0.4.16
#> [87] dplyr_1.1.4 lattice_0.22-6
#> [89] rtracklayer_1.67.1 bit_4.5.0.1
#> [91] tidyselect_1.2.1 ComplexHeatmap_2.23.0
#> [93] locfit_1.5-9.11 maketools_1.3.2
#> [95] Biostrings_2.75.4 knitr_1.49
#> [97] gridExtra_2.3 ProtGenerics_1.39.2
#> [99] edgeR_4.5.2 cmdfun_1.0.2
#> [101] xfun_0.51 statmod_1.5.0
#> [103] stringi_1.8.4 UCSC.utils_1.3.1
#> [105] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.2
#> [107] yaml_2.3.10 evaluate_1.0.3
#> [109] codetools_0.2-20 tibble_3.2.1
#> [111] wiggleplotr_1.31.0 BiocManager_1.30.25
#> [113] cli_3.6.4 munsell_0.5.1
#> [115] jquerylib_0.1.4 Rcpp_1.0.14
#> [117] png_0.1-8 XML_3.99-0.18
#> [119] parallel_4.4.2 readr_2.1.5
#> [121] blob_1.2.4 AnnotationFilter_1.31.0
#> [123] scran_1.35.0 sparseMatrixStats_1.19.0
#> [125] bitops_1.0-9 viridisLite_0.4.2
#> [127] scales_1.3.0 purrr_1.0.4
#> [129] crayon_1.5.3 GetoptLong_1.0.5
#> [131] rlang_1.1.5 KEGGREST_1.47.0