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.
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Summarize and interpret a list of significant gene sets from an enrichment analysis using network clustering, text-mining, and gene/PPI visualization.
library(msigdb)
library(GSEABase)
library(vissE)
library(igraph)
library(ggplot2)
library(patchwork)
# 1. READ USER INPUT FILES (Simulated here)
msigdb_hs = getMsigdb()
msigdb_hs = subsetCollection(msigdb_hs, c('h', 'c2', 'c5'))
set.seed(360)
geneset_res = sample(sapply(msigdb_hs, setName), 2500)
geneset_gsc = msigdb_hs[geneset_res]
# 2. COMPUTE GENE-SET OVERLAP & NETWORK
gs_ovlap = computeMsigOverlap(geneset_gsc, thresh = 0.25)
gs_ovnet = computeMsigNetwork(gs_ovlap, msigdb_hs)
# 3. IDENTIFY CLUSTERS
geneset_stats = rnorm(2500)
names(geneset_stats) = geneset_res
grps = findMsigClusters(gs_ovnet, genesetStat = geneset_stats, alg = cluster_walktrap, minSize = 5)
# 4. CHARACTERIZE CLUSTERS (TEXT-MINING)
p1 = plotMsigWordcloud(msigdb_hs, grps[1:6], type = 'Name')
# 5. VISUALIZE GENE-LEVEL STATISTICS
genes = unique(unlist(geneIds(geneset_gsc)))
gene_stats = rnorm(length(genes))
names(gene_stats) = genes
p3 = plotGeneStats(gene_stats, msigdb_hs, grps[1:6]) + geom_hline(yintercept = 0, colour = 2, lty = 2)
# 6. VISUALIZE PROTEIN-PROTEIN INTERACTIONS
ppi = getIMEX('hs', inferred = TRUE)
p4 = plotMsigPPI(ppi, msigdb_hs, grps[1:6], geneStat = gene_stats, threshStatistic = 0.2, threshConfidence = 0.2)
# 7. COMBINE VISUALIZATIONS
p2 = plotMsigNetwork(gs_ovnet, markGroups = grps[1:6], genesetStat = geneset_stats)
p1 + p2 + p3 + p4 + plot_layout(2, 2)
Input: A GeneSetCollection object containing significant gene sets. Output: A combined paneled plot summarizing the gene-set clusters, text-mining word clouds, gene statistics, and protein-protein interactions.
limma::fry, singscore, or GSEA) using network-based clustering.plotGeneStats) and protein-protein interactions (using plotMsigPPI) within identified gene-set clusters.limma or singscore first.GeneSetCollection object; vissE requires these to compute overlaps.GeneSetCollection object (e.g., from GSEABase or msigdb) representing the significant gene sets.computeMsigOverlap.cluster_walktrap): Graph clustering algorithm from igraph used in findMsigClusters.findMsigClusters.plotMsigWordcloud.plotMsigPPI.plotMsigPPI.subsetCollection to reduce noise.set.seed before plotting networks (plotMsigNetwork) or PPIs to ensure reproducible layouts.patchwork (+ and plot_layout) for collective interpretation.GeneSetCollection.thresh parameter in computeMsigOverlap to filter out weak overlaps.enrichplot for alternative visualization of enrichment results.clusterProfiler for functional profiling and gene set enrichment analysis.No reviews yet. Tell others what you thought.
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