GSEA基因基富集分析
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###GSEA富集分析中，不需要提取差异基因，只需要将所有基因的表达情况按照一定顺序排列（一般按log2FD）之后根据对照组和实验组中所有基因在红色（蓝色）富集，从而得出对照组或者实验组所富集到的通路。因此，GSEA分析可以很有效的避免基因过滤从而导致一些基因被筛除掉。
library(DO.db)
require(DOSE)
library(clusterProfiler)
library(AnnotationHub)
library(readr)
library(pheatmap)
library(tidyverse)
library(DESeq2)
library(ggplot2)
library(export)
library(enrichplot)
library(Rgraphviz)
library(org.Hs.eg.db) #all_entrez.csv的第一列是entrezid,第二列是FoldChange的值。
DEG <- read.csv(file.choose(),sep = "\t",header = TRUE)
fix(DEG)
DEG1 <- DEG$X #第一列向量化
DEG.gene_symbol = as.character(DEG1)#选择基于列表并且将其向量化#进行基因名称转换匹配
DEG.entrize_id <- mapIds(x= org.Hs.eg.db,
keys = DEG.gene_symbol,
keytype = "SYMBOL",
column = "ENTREZID")
luffy <- data.frame(DEG.entrize_id)
fix(luffy)
luffy$lgfc = " "
luffy$lgfc = DEG$log2FoldChange
write.csv(DEG.entrize_id,file = "GESA-GO_CC.csv")#去除未匹配到的NA值
DEG.enter_id <- na.omit(DEG.entrize_id)#gse需要单独做数据格式
geneList <- luffy[,2]
names(geneList)=as.factor(luffy[,1])
geneList <- sort(geneList,decreasing = TRUE)
fix(geneList)#gseGO进行GSEA分析
###gseBP <- gseGO(geneList=geneList,ont="BP",OrgDb=maize,keyType = 'ENTREZID',nPerm = 50000,minGSSize = 100,maxGSSize = 6000,pvalueCutoff = 0.05,verbose = FALSE)############# GSEA CC 模式 start
ego3 <- gseGO(geneList = geneList,OrgDb = org.Hs.eg.db,ont = "BP",nPerm = 1000,minGSSize = 100,maxGSSize = 1000,pvalueCutoff = 0.05,verbose = FALSE)
write.csv(ego3,file = "GESA-GO_CC.csv")#显示前4组信息
gseaplot2(ego3,geneSetID = 1:4, ES_geom = "dot",pvalue_table = TRUE)############# GSEA BP 模式 start
ego2 <- gseGO(geneList = geneList,OrgDb = org.Hs.eg.db,ont = "CC",pvalueCutoff = 0.05,verbose = FALSE)
write.csv(ego2,file = "GESA-GO_BP.csv")
#ridgeline plot for expression distribution of GSEA result
ridgeplot(ego2)
out_img(filename = "ridgeplot_BP",pic_width = 12,pic_height = 12)#只显示值最高的一组的信息
gseaplot2(ego2,geneSetID = 1:4, ES_geom = "dot",pvalue_table = TRUE)############# GSEA MF 模式 start
ego4 <- gseGO(geneList = geneList,OrgDb = org.Hs.eg.db,ont = "MF",pvalueCutoff = 0.05,verbose = FALSE)
write.csv(ego4,file = "GESA-GO_MF.csv")
#ridgeline plot for expression distribution of GSEA result
ridgeplot(ego4)
out_img(filename = "ridgeplot_MF",pic_width = 12,pic_height = 12)#显示前4组信息
gseaplot2(ego4,geneSetID = 1:4, ES_geom = "dot",pvalue_table = TRUE)全部评论 (0)
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