I want to identify differentially methylated probes between two groups. After preprocessed raw data, 666805 probes remained. Differential methylation analysis was performed on the remain probes using limma. Age and gender information were used as covariates. Probes with BH adjusted P value<0.05 were considered as differentially methylated probes(DMPs).
As a result, 134841 probes were identified as DMPs. It reveals nearly 1/5 probes was differentially methylated between our two groups. It is kind of strange because samples from both groups were healthy people. They just grouped by their living environment.
So, we asked one technician to perform differential methylation analysis on the same 666805 probes. By the way, they use IMA to do it. Then, only 8388 probes observed as DMPs in his analysis.
It is so strange that the numbers of two results is extremely different. I am newcomer to this research field, and i did not figure out the errors in my code. So i paste the code for DMP detection here and Hope someone can help me.
library(limma) targets<-read.table("Samplesheet_basicinfo",header=TRUE, stringsAsFactors=FALSE) betaval<-read.delim("betaval_preprocessed.txt",header=TRUE,stringsAsFactors=FALSE) mval<-log2(betaval/(1-betaval)) var<-model.matrix(~AgeY + as.factor(Gender)+as.factor(Group),data=targets) colnames(var)<-c("control","age","gender","group") fit<-lmFit(mval,var) fit2<-eBayes(fit) probe<-topTable(fit2,adjust="BH",coef=4,num=Inf) sig.probe<-probe[which(probe$adj.P.Val<=0.05),] write.table(sig.probe,file="Result.DMP.txt",sep="\t",quote=TRUE)