fgsea做GSEA
library(fgsea)1.导入测试数据,fgesa的examplePathways，exampleRanks测试数据分别是通路的list和经过fold change排序的基因。
data(examplePathways)
data(exampleRanks)
2.运行 fgsea
The resulting table contains enrichment scores and p-values:
fgseaRes <- fgsea(pathways = examplePathways,
stats = exampleRanks,
minSize=15,
maxSize=500,
nperm=10000)
head(fgseaRes[order(pval), ])
3.计算padj<0.01的个数
sum(fgseaRes[, padj < 0.01])4.画特定的通路
plotEnrichment(examplePathways[["5991130_Programmed_Cell_Death"]],
exampleRanks) + labs(title="Programmed Cell Death")
5.选择上调下调的top10的pathtway
topPathwaysUp <- fgseaRes[ES > 0][head(order(pval), n=10), pathway]
topPathwaysDown <- fgseaRes[ES < 0][head(order(pval), n=10), pathway]
topPathways <- c(topPathwaysUp, rev(topPathwaysDown))
plotGseaTable(examplePathways[topPathways], exampleRanks, fgseaRes,
gseaParam = 0.5)
From the plot above one can see that there are very similar pathways in the table (for example 5991502_Mitotic_Metaphase_and_Anaphase and 5991600_Mitotic_Anaphase). To select only independent pathways one can use collapsePathways function:
collapsedPathways <- collapsePathways(fgseaRes[order(pval)][padj < 0.01],
examplePathways, exampleRanks)
mainPathways <- fgseaRes[pathway %in% collapsedPathways$mainPathways][
order(-NES), pathway]
plotGseaTable(examplePathways[mainPathways], exampleRanks, fgseaRes,
gseaParam = 0.5)
最后保存结果。
library(data.table)
fwrite(fgseaRes, file="fgseaRes.txt", sep="\t", sep2=c("", " ", ""))本文参与 腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。
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