# chevreulProcess

Kevin Stachelek1* and Bhavana Bhat1**

1University of Southern California

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

#### 15 April 2025

#### Package

chevreulProcess 1.0.0

# 1 Basics

## 1.1 Install `chevreulProcess`

`R` is an open-source statistical environment which can be easily modified to enhance its functionality via packages. chevreulProcess 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 chevreulProcess by using the following commands in your `R` session:

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

    BiocManager::install("chevreulProcess")

## 1.2 Required knowledge

The chevreulProcess 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 chevreulProcess 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 `chevreulProcess` tag and check the older posts.

# 2 Quick start to using `chevreulProcess`

The `chevreulProcess` 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("chevreulProcess")

    # Load the data
    data("small_example_dataset")

`R` session information.

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