Perform multiple survival analysis with loop in R - r

Question

I am recently working on survival analysis with R. I have two data frames, geneDf for gene expression, survDf for the follow-up. As the following samples:
#Data frame:geneID
geneID=c("EGFR","Her2","E2F1","PTEN")
patient1=c(12,23,56,23)
patient2=c(23,34,11,6)
patient3=c(56,44,32,45)
patient4=c(23,64,45,23)
geneDf=data.frame(patient1,patient2,patient3,patient4,geneID)
> geneDf
patient1 patient2 patient3 patient4 geneID
1 12 23 56 23 EGFR
2 23 34 44 64 Her2
3 56 11 32 45 E2F1
4 23 6 45 23 PTEN
#Data frame:survDf
ID=c("patient1","patient2","patient3","patient4")
time=c(23,7,34,56)
status=c(1,0,1,1)
survDf=data.frame(ID,time,status)
#
> survDf
ID time status
1 patient1 23 1
2 patient1 7 0
3 patient1 34 1
4 patient1 56 1
I extract the expression data of specific gene from geneDf, and use the median of its expression as cut off value to perform survival analysis by “survival”package, and gain the p value by survdiff. In the following codes I use "EGFR" gene as an example.
#extract expression of a certain gene
targetGene<-subset(geneDf,grepl("EGFR",geneDf$geneID))
targetGene$geneID<-NULL
#Transpose the table and adjust its format
targetGene<-t(targetGene[,1:ncol(targetGene)])
targetGene<-data.frame(as.factor(rownames(targetGene)),targetGene)
colnames(targetGene)<-c("ID","Expression")
rownames(targetGene)<-NULL
targetGene$Expression1<-targetGene$Expression
targetGene$Expression1[ targetGene$Expression<median( targetGene$Expression)]<-1
targetGene$Expression1[ targetGene$Expression>=median( targetGene$Expression)]<-2
#Survival analysis
library(survival)
##Add survival object
survDf$SurvObj<-with(survDf, Surv(time,status==1))
## Kaplan-Meier estimator for stage
km<-survfit(SurvObj~targetGene$Expression1, data=survDf, conf.type = "log-log")
sdf<-survdiff(Surv(time, status) ~targetGene$Expression1, data=survDf)
#gain p value
p.val <-1-pchisq(sdf$chisq, length(sdf$n) - 1)
> p.val
[1] 0.1572992
I can do this through different genes one by one. But the question is: There are more than 10,000 gene need to be analyzed. I want gain all the p-values of them and put them to a new data frame. Do I need use loop or apply?

Answer 1

This is an ugly scritp but working.
In the Data10, in the first column you need to have the time, in the second one the status and in the next any treatments that you want.(patients as rownames)
loopsurff<-function(Data10){combos<-
rbind.data.frame(rep(1,ncol(Data10)- 2),
rep(2,ncol(Data10)-2),rep(3:(ncol(Data10)-2),1))
combos<-as.matrix(sapply(combos, as.numeric));library(plyr);
library(survival)
vv<-adply(combos, 2, function(x) {
fit <-survdiff(Surv(Data10[,1], Data10[,2]) ~ Data10[, x[3]],data=Data10)
p<-1 - pchisq(fit$chisq, 1)
out <- data.frame("var1"=colnames(Data10)[x[3]],"p.value" =
as.numeric(sprintf("%.3f", p)))
return(out)
})
}`
You will get a data frame with the column names of yourdata[,3:ncol(yourdata)] and the p value to each one.