# Chevreul

# Basics

## 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")

## 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")`.

## 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.

# 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 4.5.1 (2025-06-13)
    #> 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_1.1.2
    #>  [3] scater_1.37.0               ggplot2_4.0.0
    #>  [5] scuttle_1.19.0              SingleCellExperiment_1.31.1
    #>  [7] SummarizedExperiment_1.39.2 Biobase_2.69.1
    #>  [9] GenomicRanges_1.61.8        Seqinfo_0.99.4
    #> [11] IRanges_2.43.8              S4Vectors_0.47.6
    #> [13] BiocGenerics_0.55.4         generics_0.1.4
    #> [15] MatrixGenerics_1.21.0       matrixStats_1.5.0
    #> [17] BiocStyle_2.37.1
    #>
    #> 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_0.7.2
    #>   [7] GenomicFeatures_1.61.8    farver_2.1.2
    #>   [9] rmarkdown_2.30            GlobalOptions_0.1.2
    #>  [11] fs_1.6.6                  BiocIO_1.19.0
    #>  [13] vctrs_0.6.5               memoise_2.0.1
    #>  [15] Rsamtools_2.25.3          DelayedMatrixStats_1.31.0
    #>  [17] RCurl_1.98-1.17           forcats_1.0.1
    #>  [19] htmltools_0.5.8.1         S4Arrays_1.9.3
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    #>  [25] bslib_0.9.0               htmlwidgets_1.6.4
    #>  [27] plotly_4.11.0             cachem_1.1.0
    #>  [29] ResidualMatrix_1.19.0     buildtools_1.0.0
    #>  [31] GenomicAlignments_1.45.5  igraph_2.2.1
    #>  [33] iterators_1.0.14          lifecycle_1.0.4
    #>  [35] pkgconfig_2.0.3           rsvd_1.0.5
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    #>  [47] RSQLite_2.4.3             beachmat_2.25.5
    #>  [49] httr_1.4.7                abind_1.4-8
    #>  [51] compiler_4.5.1            doParallel_1.0.17
    #>  [53] bit64_4.6.0-1             withr_3.0.2
    #>  [55] S7_0.2.0                  BiocParallel_1.43.4
    #>  [57] viridis_0.6.5             DBI_1.2.3
    #>  [59] DelayedArray_0.35.5       rjson_0.2.23
    #>  [61] bluster_1.19.1            tools_4.5.1
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    #>  [67] batchelor_1.25.0          grid_4.5.1
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    #>  [79] ScaledMatrix_1.17.0       XVector_0.49.3
    #>  [81] foreach_1.5.2             stringr_1.5.2
    #>  [83] ggrepel_0.9.6             pillar_1.11.1
    #>  [85] limma_3.65.7              circlize_0.4.16
    #>  [87] dplyr_1.1.4               lattice_0.22-7
    #>  [89] rtracklayer_1.69.1        bit_4.6.0
    #>  [91] tidyselect_1.2.1          ComplexHeatmap_2.25.2
    #>  [93] locfit_1.5-9.12           maketools_1.3.2
    #>  [95] Biostrings_2.77.2         knitr_1.50
    #>  [97] gridExtra_2.3             ProtGenerics_1.41.0
    #>  [99] edgeR_4.7.6               cmdfun_1.0.2
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    #> [111] tibble_3.3.0              wiggleplotr_1.33.2
    #> [113] BiocManager_1.30.26       cli_3.6.5
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    #> [133] KEGGREST_1.49.2