nb. I should have written: "the variance of the M-value variance as a function of the mean is more U-shaped towards the extremes, versus the n shape for betas" My apologies. --t On Thu, Aug 2, 2012 at 7:16 PM, Tim Triche, Jr. wrote: > The mean-variance plot should be far "more" horizontal with M-values than > beta-values; have you plotted it against total intensity? You end up going > down the rabbit hole eventually due to copy number variation, but plotting > m-value variance against the mean, the line of best fit is nearly flat > across the range of values. The variance is more U-shaped (as opposed to > the "n" shape with beta values). > > You could try an arcsin transform > > asin(sqrt(beta))) > > if your primary goal is to stabilize the variance, though Dr. Smyth's > suggestion will probably be better for sensitivity in the end. > > Just a thought. There are many ways to transform a proportion and they > all have relative strengths and weaknesses in practice. > > > > On Thu, Aug 2, 2012 at 4:19 PM, Gordon K Smyth wrote: > >> Use eBayes with trend=TRUE later in the pipeline, then variance >> stabilization may not be needed. >> >> Gordon >> >> Date: Wed, 1 Aug 2012 15:20:56 +0200 >>> From: Gustavo Fern?ndez Bay?n >>> To: bioconductor@r-project.org >>> Subject: [BioC] Variance stabilization of m-values >>> >>> 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 >>> >> >> ______________________________**______________________________** >> __________ >> The information in this email is confidential and intend...{{dropped:4}} >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor >> > > > > -- > *A model is a lie that helps you see the truth.* > * > * > Howard Skipper > > -- *A model is a lie that helps you see the truth.* * * Howard Skipper [[alternative HTML version deleted]]