R语言Pathway可视化

使用的是Y叔的包
主要是构建geneList数据结构

library(clusterProfiler)
library(enrichplot)
#需要将差异倍数logFC按从高到底排序，同时将gene name转化为NCBI的ID
data<-read.csv("~/Desktop/DEGs.csv",header = T)
geneList<-data$logFC
names(geneList)<-data$geneID
de<-as.character(data$geneID)
ego <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont="BP", readable=TRUE)
goplot(ego)
barplot(ego, showCategory=20)
dotplot(ego, showCategory=30)
ego2 <- simplify(ego)
cnetplot(ego2, foldChange=geneList)
cnetplot(ego2, foldChange=geneList, circular = TRUE, colorEdge = TRUE)
heatplot(ego2, foldChange=geneList)
upsetplot(ego)
emapplot(ego2)
kk <- gseKEGG(geneList, nPerm=1000)
ridgeplot(kk)

我的一个例子

rt<-human_dif
filename<-"human_dif"

human_fasting_fed_dif<-human_dif[order(human_dif$log2FoldChange,decreasing = T),]
new_names<-unlist(lapply(row.names(rt), FUN = function(x) {return(strsplit(x, split = ".", fixed=T)[[1]][1])}))
row.names(rt)<-new_names
gene_list<-select(org.Hs.eg.db, keys=as.character(new_names), columns=c("SYMBOL","ENTREZID"), keytype="ENSEMBL") 
gene_list<-gene_list[!duplicated(gene_list$ENSEMBL),]
row.names(gene_list)<-as.character(gene_list$ENSEMBL)
a<-intersect(row.names(rt),row.names(gene_list))
data<-cbind(rt[a,],gene_list[a,])
data<-na.omit(data)
geneList<-data[,8]
names(geneList)<-as.character(data$ENTREZID)
de<-as.character(data$ENTREZID)
ego <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont="BP", readable=TRUE)

goplot<-goplot(ego)
barplot<-barplot(ego, showCategory=20)
dotplot<-dotplot(ego, showCategory=30)
ego2 <- simplify(ego)
cnetplot<-cnetplot(ego2, foldChange=geneList)
cnetplot2<-cnetplot(ego2, foldChange=geneList, circular = TRUE, colorEdge = TRUE)
heatplot<-heatplot(ego2, foldChange=geneList)
upsetplot<-upsetplot(ego)
emapplot<-emapplot(ego2)
ggsave(plot=goplot,paste0(filename,"_goplot"),device = "pdf")
ggsave(plot=barplot,paste0(filename,"_barplot"),device = "pdf")
ggsave(plot=dotplot,paste0(filename,"_dotplot"),device = "pdf")
ggsave(plot=cnetplot,paste0(filename,"_cnetplot"),device = "pdf")
ggsave(plot=cnetplot2,paste0(filename,"_cnetplot2"),device = "pdf")
ggsave(plot=heatplot,paste0(filename,"_heatplot"),device = "pdf")
ggsave(plot=upsetplot,paste0(filename,"_upsetplot"),device = "pdf")
ggsave(plot=upsetplot,paste0(filename,"_emapplot"),device = "pdf")
kk <- gseKEGG(geneList, nPerm=1000)
ridgeplot<-ridgeplot(kk)
ggsave(plot=ridgeplot,paste0(filename,"_ridgeplot"),device = "pdf")