I have a question in relation to limma and how to extract the random intercepts results after running a duplicated correlation. To summarize, I have DNA methylation data from a repeated intervention that was inter-spaced by one year of wash out. What I would like to see is if DNA methylation markers change similarly even after one year of wash out when the same intervention is applied again. My code is as follows:
design=model.matrix(~intervention+Age+Batch,data=pheno) #time as continuous library(limma) corfit <- duplicateCorrelation(DMFS,design,block=pheno$ID)#The function duplicateCorrelation estimates the inter-subject correlation across all probes #Then, we need to input this inter-correlation into the linear model to take into account the paired design fit = lmFit(DMFS, design,maxit=1000,block=pheno$ID,correlation=corfit$consensus) #I have chosen the option "robust" which will downplay the influence of outliers. This takes significantly more time for your computer to run but is more robust and is always my preference. #get info on eBayes in the vignette of the limma package fit2 <- eBayes(fit)
Where the outcome is delta DNA methylation (Delta M-values), intervention is 1 or 2. Then n the duplicateCorrelation I have blocked by participants ID (random intercept). However I can seem to find a way to extract results from this random intercept. I would like to see if any CpGs (probes) change similarly in both interventions, at the individual level. Thus I need estimate, SD and p-values for the random intercept. Can anyone help please?
Hi, you should have mentioned that you have also posted on Bioconductor( https://support.bioconductor.org/p/134340/ ). Thank you.