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vissE

Visualising Set Enrichment Analysis Results

Bioconductor version: 3.23 · Package version: 1.20.0

This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("vissE")

Details

MaintainerDharmesh D. Bhuva <bhuva.d@wehi.edu.au>
AuthorDharmesh D. Bhuva [aut, cre] (ORCID: <https://orcid.org/0000-0002-6398-9157>), Ahmed Mohamed [ctb]
LicenseGPL-3
URLhttps://davislaboratory.github.io/vissE
Bug Reportshttps://github.com/DavisLaboratory/vissE/issues
Downloads rank305
Source branchRELEASE_3_23
biocViewsGeneExpression, GeneSetEnrichment, Network, NetworkEnrichment, Software

Documentation

Download

Dependencies

Depends: R (>= 4.1)

Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph

Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr

Reverse dependencies

Suggests Me (1): msigdb