hi Ido, Nevermind, I was able to view the images on my cellphone. These results look concordant to me, the p-values are very close to each other and we have a bit more shrinkage of log fold changes due to some recent improvements on the beta prior variance. Note that the dispersion is simulated from a line disp = 0.1 + 4 * base-mean, so of course the shrinkage towards the line (which the parametric curve has accurately captured) makes sense. best, Mike On Thu, Jul 17, 2014 at 11:35 AM, Michael Love <michaelisaiahlove at="" gmail.com=""> wrote: > hi Ido, > > I can't see these images for some reason. Maybe you can forward them > as attachments to me? > > You jumped from the October 2013 release to the current devel version, > and hence got a lot of improved estimates at once. Due to an > improvement in dispersion estimation from 1.2 to 1.4, the maximum > likelihood estimates in your dataset are now falling closer to the > line (variance of log dispersions of 0.711 instead of 0.977), and > hence the model "trusts" the fitted line more. > > In the development branch (so from 1.4 to 1.5), we have made more > robust the estimate of the variance of the prior on log fold changes. > Note that the development branch is just that: for development. So if > you want more stability, you should use the release branch (v1.4). > > best, > > Mike > > > On Thu, Jul 17, 2014 at 11:20 AM, Ido Tamir <tamir at="" imp.ac.at=""> wrote: >> Hi, >> >> I tried to switch from DESeq2_1.2.10 (R3.0) to DESeq2_1.5.26 (R3.1) >> >> but it looks like it 1.5.26 is much more aggressive in the shrinking of the variance estimation. >> The rlog normalized fold changes are also very different. >> >> The dataset was generated with >> >> dds <- makeExampleDESeqDataSet(n = 30000, m = 6, betaSD = 1.5) >> >> saved and worked on in 2 different R/DESeq2 versions. >> >> a) >> Now the obvious question is: is newer truthier? >> >> b) >> Is there a parameter to get similar estimates with the new version as in the old version. >> Some estimates are more robust now I read in the news e.g. Cooks distance, beta prior variance. >> But I don?t understand the large changes this entails for some estimates. >> >> >> thank you very much, >> ido >> >> dispersion estimate plot 3.0 >> http://postimg.org/image/t3z7i27zz/ >> >> dispersion estimate plot 3.1 >> http://postimg.org/image/v9tickbgf >> >> log fc 3.1 vs 3.0 >> http://postimg.org/image/mp06le1a7 >> >> mean 3.1 vs 3.0 illustrating identical input data (< 100) >> http://postimg.org/image/hno76a4fz >> >> mean 3.1 vs 3.0 illustrating identical input data (all) >> http://postimg.org/image/wv46qmwan >> >> pvalue 3.1 vs 3.0 >> http://postimg.org/image/7rn46mynz >> >> 3.0: >> attr(,"coefficients") >> asymptDisp extraPois >> 0.1147892 5.3433952 >> attr(,"fitType") >> [1] "parametric" >> attr(,"varLogDispEsts") >> [1] 0.9778035 >> attr(,"expVarLogDisp") >> [1] 0.6449341 >> attr(,"dispPriorVar") >> [1] 0.3328694 >> >> 3.1: >> attr(,"coefficients") >> asymptDisp extraPois >> 0.1117856 5.8477354 >> attr(,"fitType") >> [1] "parametric" >> attr(,"varLogDispEsts") >> [1] 0.7119179 >> attr(,"expVarLogDisp") >> [1] 0.6449341 >> attr(,"dispPriorVar") >> [1] 0.25 >> >> _______________________________________________ >> 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