Read Houseman's paper and use the sorted cells from Juha Kere's lab to calibrate. If you have expression data for your samples you can further decrease the variance of your estimates. Lymphoid bias in younger subjects gradually transitions to a myeloid bias in the elderly. Or not-so-gradually depending on what they've been exposed to. This itself correlates with an overall decrease in methylation but with focal hypermethylation and deterioration of the local correlation structure in older (especially mitotically older) populations. Anyways. This is of a piece with sensitive tests for differential variability. --t On May 3, 2013, at 8:09 AM, Simone <enomis.bioc at="" gmail.com=""> wrote: > Dear Kasper, > > thank you very much for your hint. Indeed, we are (in some cases) > working on whole blood measurements, and we are aware of this problem, > but in those cases I fear we don't have any possibility to solve it. > Furthermore, it is very difficult to get datasets with large sample > sizes where cell sorting was performed, at least by now (and also > because you'd need plenty of blood to be able to purify for certain > cell types, I think). > > Although this is slightly off-topic: As you write about "correct for > cell type composition", do you know of a good way to do so? Or asking > differently, do you know any good paper estimating the subpopulation > changes during the whole aging process (in blood)? I saw plenty of > papers dealing with pediatrics, but there seems not to be much > available spanning the whole range of ages in humans (although the > biggest and fastest changes however seem to occur during the first > years of life). > > Simone > > On Fri, May 3, 2013 at 3:58 PM, Kasper Daniel Hansen > <kasperdanielhansen at="" gmail.com=""> wrote: >> For aging and methylation be very aware of the need to correct for cell type >> composition, if the measurements are on blood. >> >> Kasper >> >> >> On Fri, May 3, 2013 at 9:20 AM, Simone <enomis.bioc at="" gmail.com=""> wrote: >>> >>> Hello! >>> >>> First of all, I'm very sorry I could not reply earlier, and thank you very >>> much for answering my question on how to analyze differences in the >>> variability of methylation and expression values with aging. >>> >>> >>>> From: Pekka Kohonen >>> >>>> I also find this an interesting question. I don't have a solution >>>> handy but I would say that using linear models seems preferable >>>> because you can discount in the model other sources of variation. For >>>> instance batch effects, other confounding variables/covariates >>>> (smoking?, bmi?) and so on that are not age-related. >>> >>> Yes, this is also what we were thinking about. But I am not sure about how >>> to model variability (gene-wise) in such an approach. Whenever I would >>> like >>> to add any variability measure for a gene in a simple linear model, I >>> would >>> have to build groups of ages previously (if not, I don't know where >>> increased or decreased variability with increasing age should come from, >>> but maybe I am missing something, my experience with building such models >>> is very limited so far), but this (building of groups) is exactly what was >>> suggested to avoid. >>> >>> >>>> From: Tim Triche, Jr. >>> >>>> From an article on mixture models for simultaneously detecting >>> differences >>>> in the mean and variance, by Haim Bar and Jim Booth, which is by far the >>>> best I've read. >>> >>> Thank you very much for mentioning this very interesting paper. I don't >>> know why I haven't found it before, I should have, because it deals >>> (almost) exactly with the problem I want to solve. And it sounds really >>> good! The only thing is, that for the method the author describes in his >>> paper, I would also need to build age groups to be able to then compare >>> the >>> differences between the two groups. But as I already wrote above, I'm not >>> sure if it would really be easily possible to work without age groups >>> anyway. >>> >>> As I was very interested in the approach of the paper and could not find >>> the corresponding code neither an R package, I contacted Haim Bar via >>> e-mail. He told me that he could provide the code and that he's currently >>> generalizing the model to handle more groups and also covariates >>> (including >>> continuous variables), which is what I was looking for. So probably this >>> will be the way to go for me. >>> >>> However, I think I'll also have to come back to discuss the issue with >>> those who told me not to use age groups for my analysis, to get things >>> clearer. >>> >>> Thank you for your help. >>> >>> Best, >>> Simone >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >>