Hi everybody. I am working with Illumina 450k methylation data. I am currently cleaning a data set, getting rid of XY probes, etc? and I would like to do a non-specific filtering and preserve only 20% of the probes, those with the higher variability (as seen in Chapter 7 of the Bioconductor Case Studies book). In the book, they create a meanSdPlot() and proceed as the variance is not dependent on the mean (to a significant degree). Trying to follow that procedure, I have converted my beta values to M-values, and then called meanSdPlot(). It shows, for my data, that there is a relationship between mean and variance, i.e. the line with the median is not horizontal. Of course, if I create a meanSdPlot with the beta values, the effect is greater, due to their heteroscedasticity. Question: Is it correct to use a variance stabilization transformation (as the one in justvsn) on the M-values in order to discard low- variance probes? Any hint will be much appreciated. Regards, Gus --------------------------- Enviado con Sparrow (http://www.sparrowmailapp.com/?sig)