找出cluster的差异基因并进行GO和KEGG分析

单细胞测序数据经Seurat包tsne降维聚类后，得到cluster，如何找出cluster的marker并进行GO、KEGG分析

需要R包：Seurat、clusterProfiler、ggplot2

library（Seurat）

library(dplyr)

library(clusterProfiler)

library(ggplot2)

for( j in 0:12)

{

cluster.markers <- FindMarkers(object = RA.integrated, ident.1 =j, logfc.threshold = 0.25, test.use = "bimod", only.pos = TRUE)

cluster<- row.names.data.frame(cluster.markers)

cluster=bitr(cluster,fromType = "SYMBOL",toType = c("ENTREZID"),OrgDb = "org.Hs.eg.db")

cluster.go<-enrichGO(gene=cluster[,"ENTREZID"],keyType = "ENTREZID",OrgDb=org.Hs.eg.db,ont = "ALL",pAdjustMethod = "BH",pvalueCutoff = 0.01,qvalueCutoff = 0.05,readable = TRUE)

assign(paste0("cluster",j,".go"),cluster.go)

pdf(file = paste0("cluster",j,"go.pdf"),,width=20,height=10)

barplot(cluster.go,showCategory=50)

dev.off()

cluster.kegg<-enrichKEGG(gene = cluster[,"ENTREZID"],organism = 'hsa', pvalueCutoff = 0.05,pAdjustMethod = 'BH', minGSSize = 10,maxGSSize = 500,qvalueCutoff = 0.2,use_internal_data = FALSE)

assign(paste0("cluster",j,".kegg"),cluster.kegg)

pdf(file = paste0("cluster",j,"kegg.pdf"),,width=20,height=10)

dotplot(cluster.kegg,showCategory=50)

dev.off()

write.csv(x=cluster.markers,file=paste0("cluster",j,".csv"))

}