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. <tim.triche@gmail.com>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 <smyth@wehi.edu.au> 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 <gbayon@gmail.com> >>> 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<https: stat.e="" thz.ch="" mailman="" listinfo="" bioconductor=""> >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor<http: news.gmane.org="" gmane="" .science.biology.informatics.conductor=""> >> > > > > -- > *A model is a lie that helps you see the truth.* > * > * > Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> > > -- *A model is a lie that helps you see the truth.* * * Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> [[alternative HTML version deleted]]

Hi Tim. Sorry for the late reply. (OFFTOPIC: my third child decided to be born :) the day after I asked the question in the list, so I have been on paternal leave, and really had no time to answer the emails) The arcsin proposal is very interesting. I'll give a try too, although, as I have answered to Dr. Smyth, I do not exactly know if the curve is really important as I thought it was the first time. I am currently re-working on that pipeline, because I have to remember the exact point where I was twenty days before, and that is sometimes hard :) Thank you very much for your hints Regards, Gus --------------------------- Enviado con Sparrow (http://www.sparrowmailapp.com/?sig) El viernes 3 de agosto de 2012 a las 04:16, Tim Triche, Jr. escribi?: > 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 <smyth at="" wehi.edu.au="" (mailto:smyth="" at="" wehi.edu.au)=""> 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 <gbayon at="" gmail.com="" (mailto:gbayon="" at="" gmail.com)=""> > > > To: bioconductor at r-project.org (mailto:bioconductor at 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 at r-project.org (mailto:Bioconductor at 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 (http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf)

Hi Wolfgang, First of all, I apologize for the late reply. As I have answered in a previous mail, there have been major reasons that have kept me away from the e-mail. --------------------------- Enviado con Sparrow (http://www.sparrowmailapp.com/?sig) El s?bado 4 de agosto de 2012 a las 00:32, Wolfgang Huber escribi?: > Dear Gustavo > > the two issues: > - whether filtering of probes by overall variance is admissible and > helpful for your analysis > - whether the variance depends on the mean > are unrelated. If I understand your question correctly (and I am not > sure I do), then you should filter on the overall variance of the M > values, and need not worry about the mean-variance relationship. I was thinking about that, when I noticed that the curve showing that relationship really had nearly no influence on a filtering of that kind. I.e., if I want to get rid of those probes whose variance is low, those are quite homogenous in the graph behavior. Well, I should have to re-think this, as I currently have to re-create the pipeline. > > Can you check the paper on this topic ("Independent filtering increases > detection power for high-throughput experiments", > http://www.pnas.org/content/107/21/9546.long) and get back if it is > still unclear? > I'll give it a read. Thank you very much for the link. > > Best wishes > Wolfgang Thank you, as always, for your interesting hints and references. Regards, Gus > > > Aug/3/12 1:19 AM, Gordon K Smyth scripsit:: > > 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 <gbayon at="" gmail.com="" (mailto:gbayon="" at="" gmail.com)=""> > > > To: bioconductor at r-project.org (mailto:bioconductor at 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 inte...{{dropped:21}} > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org (mailto:Bioconductor at r-project.org) > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Dear Brent, No Amean <- rowMeans(M) wouldn't have the desired effect. Amean should reflect average intensity, so it would be necessary to compute Amean from the original intensities used to compute the M-values or beta values. Note that I don't have any first hand experience with methylation arrays, so this is just to suggest something that could be tried. Best wishes Gordon --------------------------------------------- Professor Gordon K Smyth, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia. http://www.statsci.org/smyth On Fri, 24 Aug 2012, Brent Pedersen wrote: > On Thu, Aug 2, 2012 at 5:19 PM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: >> Use eBayes with trend=TRUE later in the pipeline, then variance >> stabilization may not be needed. >> >> Gordon > > Is that recommendation only for beta values? > > when using M-values as a matrix, fit$Amean is not set so this gives an > error when using eBayes with trend=TRUE. > > Or should one just manually set fit$Amean = rowMeans(M) ? > > thanks, > -Brent > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}