Hi,

I am analyzing methylation data, I have two files with beta values one for disease samples and another for control samples.

I would like to convert both disease and control beta values in to M values and calculate Fold change of disease/control using M values

I checked many articles and found that in some studies only beta values were used and delta B was calculated to assess DMR. 

I tried both ways in the following example and found that different ways could give different assessemnt for a probe or position but i got different results

disease=.73

cont=.57

M_disease=1.434937

M_control=0.4066253

FC=M_disease/M_control=3.528893 which is differentially methylated in case threshold is 2 fold

delta B=.73-.57=.0.16 #NOT differentially methylated in case threshold=.2

Any one has an experience which has more robust output, using either delta B or  FC for instance or using p-value of statistical tests as implemented in many R packages.

Any help is appreciated

Hi Asma,

In any test of differential methylation, you want to consider the variation in each group, not just the mean difference (delta). I'm assuming here that you have multiple samples in each of the "disease" and "control" conditions. There are well-founded methods for performing differential methylation testing in, for example, the minfi Bioconductor package. You may find reading the included User's Guide helpful. 

As to the choice of M-values or Beta-values, there is some literature discussing the pros and cons of each approach. The paper "Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis" by Pan Du et al. provides a good overview of these tradeoffs. I think the argument can be reasonably summarised by "beta-values are more interpretable but M-values may offer better statistical properties".