# Chevreul

Kevin Stachelek1* and Bhavana Bhat1**

1University of Southern California

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

#### 15 April 2025

#### Package

chevreulPlot 1.0.0

# 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 4.5.0 RC (2025-04-04 r88126 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows Server 2022 x64 (build 20348)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> 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_1.0.0          chevreulProcess_1.0.0
    #>  [3] scater_1.36.0               ggplot2_3.5.2
    #>  [5] scuttle_1.18.0              SingleCellExperiment_1.30.0
    #>  [7] SummarizedExperiment_1.38.0 Biobase_2.68.0
    #>  [9] GenomicRanges_1.60.0        GenomeInfoDb_1.44.0
    #> [11] IRanges_2.42.0              S4Vectors_0.46.0
    #> [13] BiocGenerics_0.54.0         generics_0.1.3
    #> [15] MatrixGenerics_1.20.0       matrixStats_1.5.0
    #> [17] BiocStyle_2.36.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3        jsonlite_2.0.0
    #>   [3] shape_1.4.6.1             magrittr_2.0.3
    #>   [5] ggbeeswarm_0.7.2          GenomicFeatures_1.60.0
    #>   [7] farver_2.1.2              rmarkdown_2.29
    #>   [9] GlobalOptions_0.1.2       fs_1.6.6
    #>  [11] BiocIO_1.18.0             vctrs_0.6.5
    #>  [13] memoise_2.0.1             Rsamtools_2.24.0
    #>  [15] DelayedMatrixStats_1.30.0 RCurl_1.98-1.17
    #>  [17] forcats_1.0.0             htmltools_0.5.8.1
    #>  [19] S4Arrays_1.8.0            curl_6.2.2
    #>  [21] BiocNeighbors_2.2.0       SparseArray_1.8.0
    #>  [23] sass_0.4.10               bslib_0.9.0
    #>  [25] htmlwidgets_1.6.4         plotly_4.10.4
    #>  [27] cachem_1.1.0              ResidualMatrix_1.18.0
    #>  [29] GenomicAlignments_1.44.0  igraph_2.1.4
    #>  [31] iterators_1.0.14          lifecycle_1.0.4
    #>  [33] pkgconfig_2.0.3           rsvd_1.0.5
    #>  [35] Matrix_1.7-3              R6_2.6.1
    #>  [37] fastmap_1.2.0             clue_0.3-66
    #>  [39] GenomeInfoDbData_1.2.14   digest_0.6.37
    #>  [41] colorspace_2.1-1          patchwork_1.3.0
    #>  [43] AnnotationDbi_1.70.0      dqrng_0.4.1
    #>  [45] irlba_2.3.5.1             RSQLite_2.3.9
    #>  [47] beachmat_2.24.0           httr_1.4.7
    #>  [49] abind_1.4-8               compiler_4.5.0
    #>  [51] doParallel_1.0.17         bit64_4.6.0-1
    #>  [53] withr_3.0.2               BiocParallel_1.42.0
    #>  [55] viridis_0.6.5             DBI_1.2.3
    #>  [57] DelayedArray_0.34.0       rjson_0.2.23
    #>  [59] bluster_1.18.0            tools_4.5.0
    #>  [61] vipor_0.4.7               beeswarm_0.4.0
    #>  [63] glue_1.8.0                restfulr_0.0.15
    #>  [65] batchelor_1.24.0          grid_4.5.0
    #>  [67] cluster_2.1.8.1           megadepth_1.18.0
    #>  [69] gtable_0.3.6              tzdb_0.5.0
    #>  [71] tidyr_1.3.1               ensembldb_2.32.0
    #>  [73] data.table_1.17.0         hms_1.1.3
    #>  [75] metapod_1.16.0            BiocSingular_1.24.0
    #>  [77] ScaledMatrix_1.16.0       XVector_0.48.0
    #>  [79] foreach_1.5.2             stringr_1.5.1
    #>  [81] ggrepel_0.9.6             pillar_1.10.2
    #>  [83] limma_3.64.0              circlize_0.4.16
    #>  [85] dplyr_1.1.4               lattice_0.22-7
    #>  [87] rtracklayer_1.68.0        bit_4.6.0
    #>  [89] tidyselect_1.2.1          ComplexHeatmap_2.24.0
    #>  [91] locfit_1.5-9.12           Biostrings_2.76.0
    #>  [93] knitr_1.50                gridExtra_2.3
    #>  [95] bookdown_0.43             ProtGenerics_1.40.0
    #>  [97] edgeR_4.6.0               cmdfun_1.0.2
    #>  [99] xfun_0.52                 statmod_1.5.0
    #> [101] stringi_1.8.7             UCSC.utils_1.4.0
    #> [103] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.2
    #> [105] yaml_2.3.10               evaluate_1.0.3
    #> [107] codetools_0.2-20          tibble_3.2.1
    #> [109] wiggleplotr_1.32.0        BiocManager_1.30.25
    #> [111] cli_3.6.4                 munsell_0.5.1
    #> [113] jquerylib_0.1.4           Rcpp_1.0.14
    #> [115] png_0.1-8                 XML_3.99-0.18
    #> [117] parallel_4.5.0            readr_2.1.5
    #> [119] blob_1.2.4                AnnotationFilter_1.32.0
    #> [121] scran_1.36.0              sparseMatrixStats_1.20.0
    #> [123] bitops_1.0-9              viridisLite_0.4.2
    #> [125] scales_1.3.0              purrr_1.0.4
    #> [127] crayon_1.5.3              GetoptLong_1.0.5
    #> [129] rlang_1.1.6               KEGGREST_1.48.0