GSEAmining

DOI: 10.18129/B9.bioc.GSEAmining    

This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see GSEAmining.

Make Biological Sense of Gene Set Enrichment Analysis Outputs

Bioconductor version: 3.15

Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study.

Author: Oriol Arqués [aut, cre]

Maintainer: Oriol Arqués <oriol.arques at gmail.com>

Installation

To install this package, start R (version "4.2") and enter:

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

BiocManager::install("GSEAmining")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GSEAmining")

 

HTML	R Script	GSEAmining
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	Clustering, GeneSetEnrichment, Software, Visualization
Version	1.6.0
In Bioconductor since	BioC 3.12 (R-4.0) (2 years)
License	GPL-3 | file LICENSE
Depends	R (>= 4.0)
Imports	dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods
LinkingTo
Suggests	knitr, rmarkdown, BiocStyle, clusterProfiler, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
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Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	GSEAmining_1.6.0.tar.gz
Windows Binary	GSEAmining_1.6.0.zip
macOS Binary (x86_64)	GSEAmining_1.6.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/GSEAmining
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GSEAmining
Package Short Url	https://bioconductor.org/packages/GSEAmining/
Package Downloads Report	Download Stats