Date: 2026-04-28

# Chevreul

Kevin Stachelek1* and Bhavana Bhat1**

1University of Southern California

*kevin.stachelek@gmail.com
**bbhat@usc.edu

#### 28 April 2026

#### Package

chevreulPlot 1.4.0

# 1 Basics

## 1.1 Install `chevreulPlot`

`R` 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:

    if (!requireNamespace("BiocManager", quietly = TRUE)) {
        install.packages("BiocManager")
    }

    BiocManager::install("chevreulPlot")

## 1.2 Required knowledge

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")`.

## 1.3 Asking for help

`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.

# 2 Quick start to using `chevreulPlot`

The `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:

- Performing Clustering at a range of resolutions and Dimensional reduction of Raw Sequencing Data.
- Visualizing scRNA data using different plotting functions.
- Integration of multiple datasets for consistent analyses.
- Cell cycle state regression and labeling.

    library("chevreulPlot")

    # Load the data
    data("small_example_dataset")

    sessionInfo()
    #> R version 4.6.0 Patched (2026-04-24 r89963)
    #> Platform: aarch64-apple-darwin23
    #> Running under: macOS Tahoe 26.3.1
    #>
    #> Matrix products: default
    #> BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] chevreulPlot_1.4.0          chevreulProcess_1.4.0
    #>  [3] scater_1.40.0               ggplot2_4.0.3
    #>  [5] scuttle_1.22.0              SingleCellExperiment_1.34.0
    #>  [7] SummarizedExperiment_1.42.0 Biobase_2.72.0
    #>  [9] GenomicRanges_1.64.0        Seqinfo_1.2.0
    #> [11] IRanges_2.46.0              S4Vectors_0.50.0
    #> [13] BiocGenerics_0.58.0         generics_0.1.4
    #> [15] MatrixGenerics_1.24.0       matrixStats_1.5.0
    #> [17] BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3        jsonlite_2.0.0
    #>   [3] shape_1.4.6.1             magrittr_2.0.5
    #>   [5] ggbeeswarm_0.7.3          GenomicFeatures_1.64.0
    #>   [7] farver_2.1.2              rmarkdown_2.31
    #>   [9] GlobalOptions_0.1.4       fs_2.1.0
    #>  [11] BiocIO_1.22.0             vctrs_0.7.3
    #>  [13] memoise_2.0.1             Rsamtools_2.28.0
    #>  [15] DelayedMatrixStats_1.34.0 RCurl_1.98-1.18
    #>  [17] forcats_1.0.1             htmltools_0.5.9
    #>  [19] S4Arrays_1.12.0           curl_7.1.0
    #>  [21] BiocNeighbors_2.6.0       SparseArray_1.12.0
    #>  [23] sass_0.4.10               bslib_0.10.0
    #>  [25] htmlwidgets_1.6.4         plotly_4.12.0
    #>  [27] cachem_1.1.0              ResidualMatrix_1.22.0
    #>  [29] GenomicAlignments_1.48.0  igraph_2.3.0
    #>  [31] iterators_1.0.14          lifecycle_1.0.5
    #>  [33] pkgconfig_2.0.3           rsvd_1.0.5
    #>  [35] Matrix_1.7-5              R6_2.6.1
    #>  [37] fastmap_1.2.0             clue_0.3-68
    #>  [39] digest_0.6.39             colorspace_2.1-2
    #>  [41] patchwork_1.3.2           AnnotationDbi_1.74.0
    #>  [43] dqrng_0.4.1               irlba_2.3.7
    #>  [45] RSQLite_2.4.6             beachmat_2.28.0
    #>  [47] httr_1.4.8                abind_1.4-8
    #>  [49] compiler_4.6.0            doParallel_1.0.17
    #>  [51] bit64_4.8.0               withr_3.0.2
    #>  [53] S7_0.2.2                  BiocParallel_1.46.0
    #>  [55] viridis_0.6.5             DBI_1.3.0
    #>  [57] DelayedArray_0.38.0       rjson_0.2.23
    #>  [59] bluster_1.22.0            tools_4.6.0
    #>  [61] vipor_0.4.7               otel_0.2.0
    #>  [63] beeswarm_0.4.0            glue_1.8.1
    #>  [65] restfulr_0.0.16           batchelor_1.28.0
    #>  [67] grid_4.6.0                cluster_2.1.8.2
    #>  [69] megadepth_1.22.0          gtable_0.3.6
    #>  [71] tzdb_0.5.0                tidyr_1.3.2
    #>  [73] ensembldb_2.36.0          data.table_1.18.2.1
    #>  [75] hms_1.1.4                 metapod_1.20.0
    #>  [77] BiocSingular_1.28.0       ScaledMatrix_1.20.0
    #>  [79] XVector_0.52.0            foreach_1.5.2
    #>  [81] stringr_1.6.0             ggrepel_0.9.8
    #>  [83] pillar_1.11.1             limma_3.68.0
    #>  [85] circlize_0.4.18           dplyr_1.2.1
    #>  [87] lattice_0.22-9            rtracklayer_1.72.0
    #>  [89] bit_4.6.0                 tidyselect_1.2.1
    #>  [91] ComplexHeatmap_2.28.0     locfit_1.5-9.12
    #>  [93] Biostrings_2.80.0         knitr_1.51
    #>  [95] gridExtra_2.3             bookdown_0.46
    #>  [97] ProtGenerics_1.44.0       edgeR_4.10.0
    #>  [99] cmdfun_1.0.2              xfun_0.57
    #> [101] statmod_1.5.1             stringi_1.8.7
    #> [103] UCSC.utils_1.8.0          EnsDb.Hsapiens.v86_2.99.0
    #> [105] lazyeval_0.2.3            yaml_2.3.12
    #> [107] evaluate_1.0.5            codetools_0.2-20
    #> [109] cigarillo_1.2.0           tibble_3.3.1
    #> [111] wiggleplotr_1.36.0        BiocManager_1.30.27
    #> [113] cli_3.6.6                 jquerylib_0.1.4
    #> [115] dichromat_2.0-0.1         Rcpp_1.1.1-1.1
    #> [117] GenomeInfoDb_1.48.0       png_0.1-9
    #> [119] XML_3.99-0.23             parallel_4.6.0
    #> [121] readr_2.2.0               blob_1.3.0
    #> [123] AnnotationFilter_1.36.0   scran_1.40.0
    #> [125] sparseMatrixStats_1.24.0  bitops_1.0-9
    #> [127] viridisLite_0.4.3         scales_1.4.0
    #> [129] purrr_1.2.2               crayon_1.5.3
    #> [131] GetoptLong_1.1.1          rlang_1.2.0
    #> [133] KEGGREST_1.52.0