Assessing DMRs between treatment groups: subtracting control group DMRs to account for "baseline" methylation

Question

[Ellen O]

I am currently awaiting Illumina methylationEPIC (850k) data and in the mean time figuring out how to assess DMRs with demo data. I am new to these analyses but I am making use of the Maksimovic et al. cross-package workflow (https://f1000research.com/articles/5-1281/v2), which I have found very useful thus far.

I have three sample groups: healthy controls, poor treatment responders, and good treatment responders. I was wondering if it is possible to "subtract" the differential methylation seen within the controls before comparing the two treatment groups, so that I can account for a baseline/ "normal" level of variable methylation. Would this be possible in limma, minfi or any other packages? Or would it make more sense to just compare all three groups to each other?

Thanks in advance for any assistance!

Answer 1

[andrew.j.skelton73]

Others may be able to correct me if I'm wrong... but if you're making a contrast matrix in Limma, you could just do something along the lines of:

(healthy_control - poor_response) - (healthy_control - good_response)

[Ellen O]

Thanks Andrew, I will try it out!

Answer 2

[hongbo919]

I suggest using the DMR identification software based on entropy such QDMR (for pre-defined regions) or SMART (for de novo identification of DMRs from BS-Seq data). As there are multiple samples in the same group, the DMC function of SMART should work for your project.