2018年SCI论文--整合GEO数据挖掘完整复现 五 ：RobustRankAggreg(RRA)整合四个GSE数据集的差异基因，筛选共同差异基因

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四个GSE数据集差异表达基因（按logFC值排序）并为一个list，正序倒序各一个list

setwd("./2.RobustRankAggreg_analysis")
padj=0.05
logFC=1

files=c("GSE7476_limmaTab.txt","GSE13507_limmaTab.txt","GSE37815_limmaTab.txt","GSE65635_limmaTab.txt")
upList=list()
downList=list()
allFCList=list()
for(i in 1:length(files)){
    inputFile=files[i]
    rt=read.table(inputFile,header=T,sep = '\t',quote = '') # 注意文件读取
    header=unlist(strsplit(inputFile,"_"))
    downList[[header[1]]]=as.vector(rt[,1])
    upList[[header[1]]]=rev(as.vector(rt[,1]))
    fcCol=rt[,1:2]
    colnames(fcCol)=c("Gene",header[[1]])
    allFCList[[header[1]]]=fcCol
}

所有差异基因在四个GSE数据集中logFC矩阵

mergeLe=function(x,y){
  merge(x,y,by="Gene",all=T)}
newTab=Reduce(mergeLe,allFCList)
rownames(newTab)=newTab[,1]
newTab=newTab[,2:ncol(newTab)]
newTab[is.na(newTab)]=0

筛选共同上调基因

library(RobustRankAggreg)
upMatrix = rankMatrix(upList)
upAR = aggregateRanks(rmat=upMatrix)
colnames(upAR)=c("Name","Pvalue")
upAdj=p.adjust(upAR$Pvalue,method="bonferroni")
upXls=cbind(upAR,adjPvalue=upAdj)
upFC=newTab[as.vector(upXls[,1]),]
upXls=cbind(upXls,logFC=rowMeans(upFC))
write.table(upXls,file="up.xls",sep="\t",quote=F,row.names=F)
upSig=upXls[(upXls$adjPvalue<padj & upXls$logFC>logFC),]
write.table(upSig,file="upSig.xls",sep="\t",quote=F,row.names=F)

筛选共同下调基因

downMatrix = rankMatrix(downList)
downAR = aggregateRanks(rmat=downMatrix)
colnames(downAR)=c("Name","Pvalue")
downAdj=p.adjust(downAR$Pvalue,method="bonferroni")
downXls=cbind(downAR,adjPvalue=downAdj)
downFC=newTab[as.vector(downXls[,1]),]
downXls=cbind(downXls,logFC=rowMeans(downFC))
write.table(downXls,file="down.xls",sep="\t",quote=F,row.names=F)
downSig=downXls[(downXls$adjPvalue<padj & downXls$logFC< -logFC),]
write.table(downSig,file="downSig.xls",sep="\t",quote=F,row.names=F)

合并共同上下调基因

allSig = rbind(upSig,downSig)
colnames(allSig)
allSig = allSig[,c("Name","logFC")]
write.table(allSig,file = 'allSign.xls',sep = '\t',quote = F)

logFC.tiff

hminput=newTab[c(as.vector(upSig[1:20,1]),as.vector(downSig[1:20,1])),]
library(pheatmap)
tiff(file="logFC.tiff",width = 15,height = 20,units ="cm",compression="lzw",bg="white",res=400)
pheatmap(hminput,display_numbers = TRUE,
         fontsize_row=10,
         fontsize_col=12,
         color = colorRampPalette(c("green", "white", "red"))(50),
         cluster_cols = FALSE,cluster_rows = FALSE, )
dev.off()