Package: escape 2.1.0

Nick Borcherding

escape: Easy single cell analysis platform for enrichment

A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.

Authors:Nick Borcherding [aut, cre], Jared Andrews [aut]

escape_2.1.0.tar.gz
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escape_2.1.0.tgz(r-4.4-any)escape_2.1.0.tgz(r-4.3-any)
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escape_2.1.0.tgz(r-4.4-emscripten)
escape.pdf |escape.html✨
escape/json (API)
NEWS

# Install 'escape' in R:

install.packages('escape', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

escape.gene.sets - Built-In Gene Sets for escape

On BioConductor:escape-2.1.0(bioc 3.20)escape-2.0.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

12 exports 1.24 score 163 dependencies

Last updated 2 months agofrom:dbea780eff

Exports:densityEnrichment escape.matrix getGeneSets geyserEnrichment heatmapEnrichment pcaEnrichment performNormalization performPCA ridgeEnrichment runEscape scatterEnrichment splitEnrichment

Dependencies:abind annotate AnnotationDbi askpass AUCell babelgene base64enc beachmat BH Biobase BiocFileCache BiocGenerics BiocNeighbors BiocParallel BiocSingular Biostrings bit bit64 blob bslib cachem cli codetools colorspace cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray DelayedMatrixStats digest distributional dotCall64 dplyr evaluate fansi farver fastmap filelock fontawesome formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggdist ggplot2 ggpointdensity ggridges globals glue graph GSEABase GSVA gtable HDF5Array highr htmltools htmlwidgets httr IRanges irlba isoband jquerylib jsonlite KEGGREST kernlab knitr labeling lambda.r later lattice lazyeval lifecycle listenv magick magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime mixtools msigdbr munsell nlme numDeriv openssl parallelly patchwork pillar pkgconfig plogr plotly plyr png progressr promises purrr quadprog R.methodsS3 R.oo R.utils R6 rappdirs RColorBrewer Rcpp RcppEigen RcppHNSW reshape2 rhdf5 rhdf5filters Rhdf5lib rjson rlang rmarkdown RSQLite rsvd S4Arrays S4Vectors sass ScaledMatrix scales segmented SeuratObject SingleCellExperiment snow sp spam SparseArray sparseMatrixStats SpatialExperiment stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect tinytex UCell UCSC.utils utf8 vctrs viridisLite withr xfun XML xtable XVector yaml zlibbioc

Using escape to perform gene set enrichment analyses on single-cell RNA-seq data

Nick Borcherding, Jared Andrews

Rendered fromvignette.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2024-03-04
Started: 2020-06-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Visualize the mean density ranking of genes across gene set	densityEnrichment
Built-In Gene Sets for escape	escape.gene.sets
Calculate gene set enrichment scores	escape.matrix
Get a collection of gene sets to perform enrichment on	getGeneSets
Generate a ridge plot to examine enrichment distributions	geyserEnrichment
Generate a heatmap to visualize enrichment values	heatmapEnrichment
Visualize the PCA of enrichment values	pcaEnrichment
Perform Normalization on Enrichment Data	performNormalization
Perform Principal Component Analysis on Enrichment Data	performPCA
Visualize enrichment results with a ridge plot	ridgeEnrichment
Enrichment calculation for single-cell workflows	runEscape
Generate a density-based scatter plot	scatterEnrichment
Visualize enrichment results with a split violin plot	splitEnrichment