Hi,

I was trying to use DMRcate to find differentially methylated regions in illumina 450k methylation data. However, I need to account for covariates (e.g. age). I usually use the 'lm' function to find t-statistics and p-values associated with each CpG. However, with the DMRcate package, I can't see how I can incorporate the co-variates in the analysis.

Did I miss something?

thanks!

Hi Brian,

The DMRcate experimental setup mirrors that of limma, so all you have to do is pass your design matrix (with covariates), contrast matrix (optional) and coefficient of interest to cpg.annotate(), which contains the calls to lmFit() and contrasts.fit() internally. So if you have a binary contrast "mytype" with types "1" and "2" and your covariates "mycovariate1", "mycovariate2" and so on, some boilerplate would be:

type <- factor(mytype)
covariate1 <- as.numeric(mycovariate1)
covariate2 <- as.numeric(mycovariate2)
#etc

design <- model.matrix(~0 + type + covariate1 + covariate2) #etc as needed
cont.mat <- makeContrasts(c=type1-type2, levels=design)
groupanno <- cpg.annotate(myMs, analysis.type="differential", design=design, contrasts=T, cont.matrix=cont.mat, coef="c")

Best of luck,

Tim