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"))
}
