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
| Maintainer | Dharmesh D. Bhuva <bhuva.d@wehi.edu.au> |
| Author | Dharmesh D. Bhuva [aut, cre] (ORCID: <https://orcid.org/0000-0002-6398-9157>), Ahmed Mohamed [ctb] |
| License | GPL-3 |
| URL | https://davislaboratory.github.io/vissE |
| Bug Reports | https://github.com/DavisLaboratory/vissE/issues |
| Downloads rank | 305 |
| Source branch | RELEASE_3_23 |
| biocViews | GeneExpression, 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