批量基因生存分析 简洁版本_r语言批量做基因的生存分析

input


codes

load(file ="G:/r/duqiang_IPF/GSE70866—true—_BAL_IPF_donors_RNA-seq/expr17077.RData")
head(expr.17077clean)
head(meta.17077)[,1:4]
dim(expr.17077clean)
dim(meta.17077)

colnames(meta.17077)=c('time','event','sex','diagnosis')

phe=transform(meta.17077,time=as.numeric(time)) %>% transform(event=as.numeric(event))
head(phe)


exprSet=expr.17077clean %>% transform(as.numeric()) %>% as.matrix()
head(exprSet)[,1:5]



#生存分析
library(survival)
library(survminer)
# 利用ggsurvplot快速绘制漂亮的生存曲线图 根据性别
sfit <- survfit(data=phe,Surv(time, event)~sex)
sfit
head(summary(sfit))
ggsurvplot(sfit, conf.int=F, pval=TRUE)
ggsurvplot(sfit,palette = c("#E7B800", "#2E9FDF"),
           risk.table =TRUE,pval =TRUE,
           conf.int =TRUE,xlab ="Time in months", 
           ggtheme =theme_light(), 
           ncensor.plot = TRUE)


#挑选感兴趣的基因做差异分析
phe$CBX4=ifelse(exprSet['CBX4',]>median(exprSet['CBX4',]),'high','low')
head(phe)
table(phe$CBX4)
ggsurvplot(survfit(Surv(time, event)~CBX4, data=phe), conf.int=F, pval=TRUE)
ggsurvplot(survfit(Surv(time, event)~phe[,'CBX4'], data=phe), conf.int=F, pval=TRUE)



#挑选感兴趣的基因批量做差异分析
gene_interested=readClipboard()
head(gene_interested)
library(stringr)
gene_interested=str_split(pattern = ",",gene_interested)[[1]]
gene_interested=gene_interested[-which(gene_interested=="RAB40A")]
gene_interested

getwd()
dir.create("G:/r/duqiang_IPF/GSE70866—true—_BAL_IPF_donors_RNA-seq/survival_for_genes-siena")
setwd("G:/r/duqiang_IPF/GSE70866—true—_BAL_IPF_donors_RNA-seq/survival_for_genes-siena")
#mySurv=with(phe,Surv(time, event))


for (eachgene in gene_interested) {
  phe$group=ifelse(exprSet[eachgene,]>median(exprSet[eachgene,]),'high','low')
  p=ggsurvplot(survfit(Surv(time, event)~group, 
                       data=phe), conf.int=F, pval=TRUE)
  pdf(paste0(eachgene, "_surv.pdf"),width = 5, height = 5)
  print(p, newpage = FALSE)
  dev.off()
}


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results结果展示