Package: escape 2.3.0
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:
escape_2.3.0.tar.gz
escape_2.3.0.zip(r-4.5)escape_2.3.0.zip(r-4.4)escape_2.3.0.zip(r-4.3)
escape_2.3.0.tgz(r-4.4-any)escape_2.3.0.tgz(r-4.3-any)
escape_2.3.0.tar.gz(r-4.5-noble)escape_2.3.0.tar.gz(r-4.4-noble)
escape_2.3.0.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')) |
- escape.gene.sets - Built-In Gene Sets for escape
On BioConductor:escape-2.1.5(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.
softwaresinglecellclassificationannotationgenesetenrichmentsequencinggenesignalingpathways
Last updated 24 days agofrom:78d6ceb67b. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win | NOTE | Oct 31 2024 |
| R-4.5-linux | NOTE | Oct 30 2024 |
| R-4.4-win | NOTE | Oct 31 2024 |
| R-4.4-mac | NOTE | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:densityEnrichmentescape.matrixgetGeneSetsgeyserEnrichmentheatmapEnrichmentpcaEnrichmentperformNormalizationperformPCAridgeEnrichmentrunEscapescatterEnrichmentsplitEnrichment
Dependencies:abindannotateAnnotationDbiaskpassassortheadAUCellbabelgenebase64encbeachmatBHBiobaseBiocFileCacheBiocGenericsBiocNeighborsBiocParallelBiocSingularBiostringsbitbit64blobbslibcachemclicodetoolscolorspacecpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArrayDelayedMatrixStatsdigestdistributionaldotCall64dplyrevaluatefansifarverfastmapfilelockfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggdistggplot2ggpointdensityggridgesglobalsgluegraphGSEABaseGSVAgtableHDF5ArrayhighrhtmltoolshtmlwidgetshttrIRangesirlbaisobandjquerylibjsonliteKEGGRESTkernlabknitrlabelinglambda.rlaterlatticelazyevallifecyclelistenvmagickmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemixtoolsmsigdbrmunsellnlmenumDerivopensslparallellypatchworkpillarpkgconfigplogrplotlyplyrpngprogressrpromisespurrrquadprogR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppEigenreshape2rhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRSQLitersvdS4ArraysS4VectorssassScaledMatrixscalessegmentedSeuratObjectSingleCellExperimentsnowspspamSparseArraysparseMatrixStatsSpatialExperimentstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttinytexUCellUCSC.utilsutf8vctrsviridisLitewithrxfunXMLxtableXVectoryamlzlibbioc
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 |
