Package: GSEAmining
Type: Package
Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs
Version: 1.23.0
Authors@R: person("Oriol", "Arqués", email = "oriol.arques@gmail.com",
                  role = c("aut", "cre")) 
Description: 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.
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud,
        stringr, gridExtra, rlang, grDevices, graphics, stats, methods
Depends: R (>= 4.0)
Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat, tm
VignetteBuilder: knitr
biocViews: GeneSetEnrichment, Clustering, Visualization
Config/pak/sysreqs: libicu-dev libjpeg-dev libpng-dev libxml2-dev
        libssl-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:53:08 UTC
RemoteUrl: https://github.com/bioc/GSEAmining
RemoteRef: HEAD
RemoteSha: 7b07dc9e9e9d3c558be2348b7fe6a2e541e94f1a
NeedsCompilation: no
Packaged: 2026-06-02 08:35:11 UTC; root
Author: Oriol Arqués [aut, cre]
Maintainer: Oriol Arqués <oriol.arques@gmail.com>
Built: R 4.6.0; ; 2026-06-02 08:37:05 UTC; windows
