chevreulPlot 1.1.2
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")
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.
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:
library("chevreulPlot")
# Load the data
data("small_example_dataset")
sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] chevreulPlot_1.1.2 chevreulProcess_1.1.2
#> [3] scater_1.37.0 ggplot2_3.5.2
#> [5] scuttle_1.19.0 SingleCellExperiment_1.31.0
#> [7] SummarizedExperiment_1.39.0 Biobase_2.69.0
#> [9] GenomicRanges_1.61.0 GenomeInfoDb_1.45.4
#> [11] IRanges_2.43.0 S4Vectors_0.47.0
#> [13] BiocGenerics_0.55.0 generics_0.1.4
#> [15] MatrixGenerics_1.21.0 matrixStats_1.5.0
#> [17] BiocStyle_2.37.0
#>
#> 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_1.61.3
#> [7] farver_2.1.2 rmarkdown_2.29
#> [9] GlobalOptions_0.1.2 fs_1.6.6
#> [11] BiocIO_1.19.0 vctrs_0.6.5
#> [13] memoise_2.0.1 Rsamtools_2.25.0
#> [15] DelayedMatrixStats_1.31.0 RCurl_1.98-1.17
#> [17] forcats_1.0.0 htmltools_0.5.8.1
#> [19] S4Arrays_1.9.1 curl_6.2.3
#> [21] BiocNeighbors_2.3.1 SparseArray_1.9.0
#> [23] sass_0.4.10 bslib_0.9.0
#> [25] htmlwidgets_1.6.4 plotly_4.10.4
#> [27] cachem_1.1.0 ResidualMatrix_1.19.0
#> [29] GenomicAlignments_1.45.0 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] digest_0.6.37 colorspace_2.1-1
#> [41] patchwork_1.3.0 AnnotationDbi_1.71.0
#> [43] dqrng_0.4.1 irlba_2.3.5.1
#> [45] RSQLite_2.4.0 beachmat_2.25.1
#> [47] httr_1.4.7 abind_1.4-8
#> [49] compiler_4.5.0 doParallel_1.0.17
#> [51] bit64_4.6.0-1 withr_3.0.2
#> [53] BiocParallel_1.43.3 viridis_0.6.5
#> [55] DBI_1.2.3 DelayedArray_0.35.1
#> [57] rjson_0.2.23 bluster_1.19.0
#> [59] tools_4.5.0 vipor_0.4.7
#> [61] beeswarm_0.4.0 glue_1.8.0
#> [63] restfulr_0.0.15 batchelor_1.25.0
#> [65] grid_4.5.0 cluster_2.1.8.1
#> [67] megadepth_1.19.0 gtable_0.3.6
#> [69] tzdb_0.5.0 tidyr_1.3.1
#> [71] ensembldb_2.33.0 data.table_1.17.4
#> [73] hms_1.1.3 metapod_1.17.0
#> [75] BiocSingular_1.25.0 ScaledMatrix_1.17.0
#> [77] XVector_0.49.0 foreach_1.5.2
#> [79] stringr_1.5.1 ggrepel_0.9.6
#> [81] pillar_1.10.2 limma_3.65.1
#> [83] circlize_0.4.16 dplyr_1.1.4
#> [85] lattice_0.22-7 rtracklayer_1.69.0
#> [87] bit_4.6.0 tidyselect_1.2.1
#> [89] ComplexHeatmap_2.25.0 locfit_1.5-9.12
#> [91] Biostrings_2.77.1 knitr_1.50
#> [93] gridExtra_2.3 bookdown_0.43
#> [95] ProtGenerics_1.41.0 edgeR_4.7.2
#> [97] cmdfun_1.0.2 xfun_0.52
#> [99] statmod_1.5.0 stringi_1.8.7
#> [101] UCSC.utils_1.5.0 EnsDb.Hsapiens.v86_2.99.0
#> [103] lazyeval_0.2.2 yaml_2.3.10
#> [105] evaluate_1.0.3 codetools_0.2-20
#> [107] tibble_3.2.1 wiggleplotr_1.33.0
#> [109] BiocManager_1.30.25 cli_3.6.5
#> [111] jquerylib_0.1.4 dichromat_2.0-0.1
#> [113] Rcpp_1.0.14 png_0.1-8
#> [115] XML_3.99-0.18 parallel_4.5.0
#> [117] readr_2.1.5 blob_1.2.4
#> [119] AnnotationFilter_1.33.0 scran_1.37.0
#> [121] sparseMatrixStats_1.21.0 bitops_1.0-9
#> [123] viridisLite_0.4.2 scales_1.4.0
#> [125] purrr_1.0.4 crayon_1.5.3
#> [127] GetoptLong_1.0.5 rlang_1.1.6
#> [129] KEGGREST_1.49.0