数据库代码公开：药敏相关性分析

Gene = "MKI67"
library(limma)
#读取药物数据
data = read.table(drugFile,sep="\t",header=T,check.names=F)
data = as.matrix(data)
rownames(data) = data[,1]
drug = data[,2:ncol(data)]
dimnames=  list(rownames(drug),colnames(drug))
data = matrix(as.numeric(as.matrix(drug)),nrow=nrow(drug),dimnames=dimnames)
rm(drug)
#对药物数据补缺
data = impute.knn(data)
drug =  data$data
drug = avereps(drug)
rm(data)
#读取表达谱
data = read.table(expFile,sep="\t",header=T,check.names=F)
data = as.matrix(data)
rownames(data) = data[,1]
exp = data[,2:ncol(data)]
dimnames = list(rownames(exp),colnames(exp))
data = matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
exp = avereps(data)
rm(data)

samesample = intersect(colnames(exp),colnames(drug))
exp = exp[,samesample]
drug = drug[,samesample]

outTab = data.frame()
x = as.numeric(exp[Gene,])
#对药物循环与基因表达量执行spearman相关性分析
for(Drug in row.names(drug)){
  y = as.numeric(drug[Drug,])
  corT = cor.test(x,y,method="spearman")
  cor = corT$estimate
  pvalue = corT$p.value
  if(pvalue < 0.05){
    outVector = cbind(Gene,
                      Drug,
                      cor,
                      pvalue)
    outTab = rbind(outTab,outVector)
  }
}