chevreulPlot 0.99.11
chevreulPlotR 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.
chevreulPlotThe 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
library(chevreuldata)
chevreul_sce <- human_gene_transcript_sce()
chevreul_sce
#> class: SingleCellExperiment
#> dim: 9740 883
#> metadata(2): markers experiment
#> assays(3): counts logcounts scaledata
#> rownames(9740): 5-8S-rRNA A2M-AS1 ... HHIP-AS1 AC117490.2
#> rowData names(0):
#> colnames(883): ds20181001-0001 ds20181001-0002 ... ds20181001-1039
#> ds20181001-1040
#> colData names(49): orig.ident nCount_gene ... nFeature_transcript ident
#> reducedDimNames(2): PCA UMAP
#> mainExpName: gene
#> altExpNames(1): transcriptsessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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] chevreuldata_0.99.16 ExperimentHub_2.15.0
#> [3] AnnotationHub_3.15.0 BiocFileCache_2.15.0
#> [5] dbplyr_2.5.0 chevreulPlot_0.99.11
#> [7] chevreulProcess_0.99.19 scater_1.35.0
#> [9] ggplot2_3.5.1 scuttle_1.17.0
#> [11] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
#> [13] Biobase_2.67.0 GenomicRanges_1.59.1
#> [15] GenomeInfoDb_1.43.1 IRanges_2.41.1
#> [17] S4Vectors_0.45.2 BiocGenerics_0.53.3
#> [19] generics_0.1.3 MatrixGenerics_1.19.0
#> [21] matrixStats_1.4.1 BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] batchelor_1.23.0 BiocIO_1.17.0
#> [3] bitops_1.0-9 filelock_1.0.3
#> [5] tibble_3.2.1 EnsDb.Mmusculus.v79_2.99.0
#> [7] XML_3.99-0.17 lifecycle_1.0.4
#> [9] edgeR_4.5.0 doParallel_1.0.17
#> [11] lattice_0.22-6 ensembldb_2.31.0
#> [13] magrittr_2.0.3 plotly_4.10.4
#> [15] limma_3.63.2 sass_0.4.9
#> [17] rmarkdown_2.29 jquerylib_0.1.4
#> [19] yaml_2.3.10 metapod_1.15.0
#> [21] DBI_1.2.3 RColorBrewer_1.1-3
#> [23] ResidualMatrix_1.17.0 abind_1.4-8
#> [25] zlibbioc_1.53.0 purrr_1.0.2
#> [27] AnnotationFilter_1.31.0 RCurl_1.98-1.16
#> [29] rappdirs_0.3.3 circlize_0.4.16
#> [31] GenomeInfoDbData_1.2.13 ggrepel_0.9.6
#> [33] irlba_2.3.5.1 megadepth_1.17.0
#> [35] cmdfun_1.0.2 dqrng_0.4.1
#> [37] DelayedMatrixStats_1.29.0 codetools_0.2-20
#> [39] DelayedArray_0.33.2 tidyselect_1.2.1
#> [41] shape_1.4.6.1 UCSC.utils_1.3.0
#> [43] farver_2.1.2 wiggleplotr_1.31.0
#> [45] ScaledMatrix_1.15.0 viridis_0.6.5
#> [47] GenomicAlignments_1.43.0 jsonlite_1.8.9
#> [49] GetoptLong_1.0.5 BiocNeighbors_2.1.0
#> [51] iterators_1.0.14 foreach_1.5.2
#> [53] tools_4.5.0 Rcpp_1.0.13-1
#> [55] glue_1.8.0 gridExtra_2.3
#> [57] SparseArray_1.7.2 xfun_0.49
#> [59] dplyr_1.1.4 withr_3.0.2
#> [61] BiocManager_1.30.25 fastmap_1.2.0
#> [63] bluster_1.17.0 fansi_1.0.6
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#> [73] data.table_1.16.2 rtracklayer_1.67.0
#> [75] htmlwidgets_1.6.4 httr_1.4.7
#> [77] S4Arrays_1.7.1 pkgconfig_2.0.3
#> [79] gtable_0.3.6 blob_1.2.4
#> [81] ComplexHeatmap_2.23.0 XVector_0.47.0
#> [83] htmltools_0.5.8.1 bookdown_0.41
#> [85] ProtGenerics_1.39.0 clue_0.3-66
#> [87] scales_1.3.0 png_0.1-8
#> [89] scran_1.35.0 knitr_1.49
#> [91] tzdb_0.4.0 rjson_0.2.23
#> [93] curl_6.0.1 cachem_1.1.0
#> [95] GlobalOptions_0.1.2 stringr_1.5.1
#> [97] BiocVersion_3.21.1 parallel_4.5.0
#> [99] vipor_0.4.7 AnnotationDbi_1.69.0
#> [101] restfulr_0.0.15 pillar_1.9.0
#> [103] grid_4.5.0 vctrs_0.6.5
#> [105] BiocSingular_1.23.0 EnsDb.Hsapiens.v86_2.99.0
#> [107] beachmat_2.23.1 cluster_2.1.6
#> [109] beeswarm_0.4.0 evaluate_1.0.1
#> [111] readr_2.1.5 GenomicFeatures_1.59.1
#> [113] cli_3.6.3 locfit_1.5-9.10
#> [115] compiler_4.5.0 Rsamtools_2.23.0
#> [117] rlang_1.1.4 crayon_1.5.3
#> [119] forcats_1.0.0 fs_1.6.5
#> [121] ggbeeswarm_0.7.2 stringi_1.8.4
#> [123] viridisLite_0.4.2 BiocParallel_1.41.0
#> [125] munsell_0.5.1 Biostrings_2.75.1
#> [127] lazyeval_0.2.2 Matrix_1.7-1
#> [129] hms_1.1.3 patchwork_1.3.0
#> [131] sparseMatrixStats_1.19.0 bit64_4.5.2
#> [133] KEGGREST_1.47.0 statmod_1.5.0
#> [135] igraph_2.1.1 memoise_2.0.1
#> [137] bslib_0.8.0 bit_4.5.0