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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。