On Sun, Aug 19, 2012 at 2:21 PM, Tim Triche, Jr. <tim.triche at="" gmail.com=""> wrote: > (a bit late, but better late than never) > > Dr. Coombes is right, of course, and I ought to have mentioned this > earlier. It is a thorny issue, especially with epidemiological studies. > Cancer, less so. > > For differences that are real, usually they are significant on the beta > value scale *and* the logistic scale. Changes that are only significant on > the logit scale are often artifacts. The logistic scale is much more > sensitive to technical artifacts, even if you damp the extremes by using a > huge offset. There are intermediate transformations (probit, > arcsin(sqrt(x)), etc.) which are intermediate between proportional and > logistic, but they haven't found much traction. > > There are a couple of things that do help and can be used to filter an > unbiased fashion. I'm hoping to get them submitted in the very near > future. I do not know if they will be as useful for CHARM data as they > seem to be for Illumina arrays, eRRBS, and WGSBS sequence data, but I guess > we shall find out. > > regarding TCGA data: > > TCGA methylation data is background corrected and dye bias equalized (for > the 450k samples, at least, and as batches are updated, 27k as well) but no > batch correction is done for the level 3 data. In the case of multi-batch > tumors it is a good idea to run ComBat or (if you must) SVA to compensate. Sorry to hijack the thread, but, what is the reason to prefer ComBat over SVA? > It's run through normalizeMethyLumiSet(methylumi.bgcorr(the.data)) for > level 2 and 3 data, and the IDAT files are provided as level 1, so anyone > who wants to reproduce things from scratch with a different preprocessing > strategy is welcome to do so. Methylumi and minfi happen to have the same > IDAT parsing code these days, and I would not be surprised if they > eventually merged. I wouldn't bother using anything else, especially for > large volumes of samples. > > switching from 0.1% methylated to 99.9% methylated is probably a real > effect. Switching from 1% to 3% across the board is probably technical > artifacts. I'm guessing this to be true only for tumor/normal comparisons or "pure" samples. What about peripheral blood where one may be measuring a signal from a variety of cell types or tissues? > You will see this all the time on 450k data that hasn't been > (ahem) properly background corrected, and to a significant degree in 27k > data as well. It's not limited to TCGA; I've seen plenty of data from > other centers that benefited significantly from being re-processed > sensibly. In any event, I pushed for, and got, changes to policy so that > Illumina methylation data for TCGA is provided as raw IDAT files, and all > of the code used for processing is available either from the Bioconductor > package repository or from GitHub (in the case of the packaging pipeline > itself). It's not perfect but at least it is 100% transparent. > > one last thing: > > The Illumina annotations have been updated to a FeatureDb (one for hg19, > and another available soon for hg18) which I would like to propose as the > standard for these arrays, as they cover both 27k and 450k features (along > with some information that very few people seem to know about each). I > think it's as fast as what minfi uses and as comprehensive as the .db0 > packages, while less confusing than either. So anyone who wants to, please > try them. > > > > On Fri, Aug 17, 2012 at 1:05 PM, Kevin R. Coombes <kevin.r.coombes at="" gmail.com="">> wrote: > >> I understand all the statistical reasons for converting from methylation >> "beta values" to something logistic, and am frequently tempted to do this >> myself. >> >> But I think in the context of methylation that this advice should come >> with a warning: changes in levels near 0 and 1 may have a lot of leverage >> on the final results. For example, we have done analyses on some of the >> TCGA data where we find "statistically significant differences in >> methylation between normal and tumor" where the mean beta values are 0.03 >> and 0.08. I find it hard to believe that this level of change in >> methylation has any kind of biological meaning. In fact, I'm not even >> convinced that we can accurately measure this amount of change using the >> technology that TCGA is using (although I might well believe that such a >> change could result from batch effects, whether in the assay or in the data >> processing). >> >> I don't have any magic solution to fix this issue; it is intrinsic in the >> shape of the logistic curve. One might want to explore shrinking the beta >> values toward 0.5 (i.e., away from 0 and 1), but I can't offer any concrete >> advice on how well this might work in practice. >> >> Best, >> Kevin >> >> >> On 8/17/2012 12:36 PM, Tim Triche, Jr. wrote: >> >> The reason to switch from a proportion (%, beta-value, whichever; anything >> measuring M / (M+U) where M and U are surrogates for methylated and >> unmethylated cytosines) to a fold-change (logit(proportion.methylated) or >> log2(M/U)) is that the latter is far more amenable to linear models, and >> roughly parallels the expected behavior in terms of expression changes on a >> log2 or log-fold-change scale. >> >> Furthermore, the range for logit(M/U) is -Infinity to +Infinity, which is >> appropriate when you are modeling something as having Gaussian error. >> Something with a range of 0 to 1 is neither homoskedastic (which is to >> say, such a 0-1 measurement will have a variance that depends on the mean) >> nor unbounded (this turns out to be an issue when computing maximum >> likelihood estimates, for example, as values close to the boundary will >> cause problems). >> >> In any event, logit(% methylation) is equivalent to log(M/U) which is where >> I veered off course this morning. My brain seems to have been a bit slow. >> >> >> On Fri, Aug 17, 2012 at 9:26 AM, zeynep ?zkeserli <zeynep.ozkeserli at="" gmail.com=""> wrote: >> >> >> Dear Tim, >> >> Thank you for your answer. But to my understanding, if I could get this >> answer by undoing the logit function (I tought you were doing this), we >> should use inverse logit function. Which is exp(x)/(1+exp(x)) >> >> And in my case it gives: >> >> >> exp(-0.30427)/(1+exp(-0.30427)) >> >> [1] 0.424514 >> >> Ok, this seems reasonable. And it makes sense how you put this into words. >> But if we could use this one as a methylation measure, why would the >> creators make things more complicated and convert the value to a logit >> value? So, again, to my understanding, I shall learn how to interpret the >> diff thing. >> >> Thank you again, >> >> Best :) >> >> Zeynep >> >> On Fri, Aug 17, 2012 at 6:29 PM, Tim Triche, Jr. <tim.triche at="" gmail.com=""> <tim.triche at="" gmail.com="">wrote: >> >> >> Perhaps "on average this region has an >> >> R> 1 - exp(-0.347) >> [1] 0.2931947 >> >> approximately 29.3% relative decrease in cytosine methylation after >> treatment?" >> >> >> >> On Fri, Aug 17, 2012 at 1:56 AM, zeynep ?zkeserli <zeynep.ozkeserli at="" gmail.com=""> wrote: >> >> >> Dear All, Dear Dr. Aryee and Dr. Carvalho, >> >> I have a question on interpreting the results of dmrFinder function. >> >> We have performed a CHARM analysis on the data we got from NimbleGen >> Promoter Medip Arrays. The data is obtained from each patient before and >> after treatment. And after performing CHARM analysis, we got some >> differentially methylated regions (DMRs). >> >> As the samples are before and after treatment results of the same >> patient, >> the samples are treated as paired samples. >> >> My question is about interpretation of the results: >> >> After running this: >> >> dmr1_2 <- dmrFinder(rawData, p = p, groups = grp,compare = c("to", "ts"), >> cutoff=0.995,paired=TRUE,pairs=pairs) >> >> to: before treatment >> ts: after treatment >> >> - For example I have found a DMR like this (I summerized the result for >> my >> question): >> >> chr 8, diff= -0.30427 and maxdiff=0.47935 >> >> As the diff value is calculated like this: average l (logit(percentage) >> methylation if l=NULL) difference within the DMR if paired=TRUE >> >> Is it true to say that: "The region has 0.30427 times the risk of being >> methylated in samples of after treatment compared to samples of before >> treatment." >> >> I know that it does not look meaningful to use the word "risk" when >> talking >> about something like that but I can not find a better way to say it >> truely. Is it possible to express it like a "0.30427 fold difference in >> methylation"? And also am I interpreting the "-" sign truely? >> >> Thank you for your help in advance, >> >> Best Regards, >> >> Zeynep >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing listBioconductor at r-project.orghttps://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives:http://news.gmane.org/gmane.science.biology.inf ormatics.conductor >> >> -- >> *A model is a lie that helps you see the truth.* >> * >> * >> Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> <http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> >> >> >> >> _______________________________________________ >> Bioconductor mailing listBioconductor at r-project.orghttps://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=""> > > [[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