Hi everybody,
I am currently working on a DNA methylation microarray analysis project. I have 20 samples measured on a Illumina 450k. After some initial preprocessing and non-specific filtering, I lowered its dimensionality down to 47k probes. Using minfi, I adjust a linear regression model to each probe taking the sample age as the only continuous predictor and trying to estimate the methylation level (in the form of M-values, logit transformations of the beta values). P-values are then adjusted using FDR, and I keep the significant probes as the final subset of differentially methylated probes.
Now, we want to divide these probes in several groups, according to their variability trend. This is, we want to be able to detect if, for a given probe, the methylation values are convergent or divergent with respect to age. At first I was thinking about using the White test to see if the squared residuals behave as stated before, or something equivalent for heteroskedasticity testing. But then I thought that if the squared residuals behave in a non-normal way, it could be due to several other factors, such as outliers or influence points. Am I right untrusting this approximation or the White test could fit in this context?
A fellow told me another possible way would be to use Mixed Models with a variance function. That way I could model not only the change in methylation level but also the change in variabilty. If I choose this way, then I should define some age groups and partition the samples among them, shouldn't I? Is this a better approximation in this case than the basic linear regression?
Any help or hint will be much appreciated.
Regards, Gus
