On Tue, 12 Mar 2013, Zhuzhu Zhang wrote: > Dear Gordon, > > Thank you very much for your reply. That was greatly helpful. > > For prion.df, would you recommend that the user use the default value in the > current version, or choose it differently for different datasets since it > varies as the data size changes? Use the default value. As I tried to explain, the whole point of using prior.df instead of prior.n is that the optimal value for prior.df does not depend on data size. > If the latter, would the same principle of > choosing prior.n be applied? prior.n is a function of prior.df. See ?getPriorN. Gordon > Thanks, > Zhuzhu > > > > > On 3/8/13 2:06 AM, Gordon K Smyth wrote: >> Dear Zhuzhu, >> >> The edgeR User's guide gives case studies of how we intend edgeR to be >> used. You will see that we expect users to generally use the default >> parameters. Although there are a great many parameters that can be >> varied in principle in the edgeR functions, we not expect them to be >> changed in most analyses. >> >> One exception was the prior.n argument to estimateTagwiseDisp. It was >> never our intention that a fixed value for prior.n would be used for >> datasets of different sizes, so previous versions of the User's Guide >> used to explain how to set this parameter. Several versions ago, we >> eliminated the prior.n argument and replaced it with prior.df so that >> the default behavior was what we intended. >> >> Over time we have reduced the default value for prior.df somewhat. >> Larger values of prior.df give more priority to genes with large fold >> changes between groups. Smaller values of prior.df give more priority >> to genes with small dispersion values, i.e., to genes that are >> consistent between replicates. >> >> You ask about exactTest(). To learn more about the different rejection >> regions, you should start by reading the help page for exactTest(), but >> I doubt that this is causing you a problem. The theory behind the >> original exact negative binomial test of Robinson and Smyth (2008) >> presupposed that the conditional distibution of the counts given the >> genewise total was unimodal (like a binomial distribution). This is >> true for typical dispersion values, but is not true for very large >> dispersion values. Hence the original exactTest can give totally >> innappropriate results when the dispersion is very, very large. The new >> rejection region is similar to the original when the dispersion is >> smallish, but gives sensible results in any situation. Hence it should >> be used. The old rejection region is preserved as an option only for >> backward compatability. >> >> Best wishes >> Gordon >> >> >>> Date: Wed, 06 Mar 2013 19:22:06 -0500 >>> From: Zhuzhu Zhang <zhuzhuz at="" email.unc.edu=""> >>> To: bioconductor at r-project.org >>> Subject: [BioC] edgeR: new defaults of estimateTagwiseDisp and >>> exactTest >>> >>> Dear All, >>> >>> I'm running edgeR 3.0.8 and notice that the results are considerably >>> different than those from older versions. I realized that it was >>> likely due to the different prior.df I used in Function >>> estimateTagwiseDisp, thanks to an earlier discussion on the list- >>> https://stat.ethz.ch/pipermail/bioconductor/2012-December/049644.html >>> >>> I have a few more questions: >>> >>> 1. How to choose a proper prior.df (or prior.n)? >>> >>> 2. How is the new default method of exactText different from the old >>> default (rejection.region = "smallp")? How does it improve the >>> performance? >>> >>> 3. In general, what parameters should I tune for different datasets, >>> when using function estimateTagwiseDisp and exactTest? I used the >>> defaults but realized that they may not be most appropriate. >>> >>> Thank you for your time and attention. Any suggestions and comments >>> would be extremely helpful and appreciated. >>> >>> Thanks, >>> Zhuzhu ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}