Question: limma: decideTests
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gravatar for Gordon Smyth
11.9 years ago by
Gordon Smyth39k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth39k wrote:
Dear Al, No research papers (to my knowledge) have been written yet on multiple testing for the hierarchical linear model, so I can't refer you to any literature. Use "separate" if you want to get the same results as topTable(). The great advantage of this method is that you'll get the equivalent results regardless of which set of contrasts you test together. This is fine for each contrast but does not do any multiple testing adjustment between contrasts. Another disadvantage is that the raw p-value cutoff for any adjusted p-value threshold can be very different for different contrasts. Use this method if you have only a few contrasts and want to use the simplest method. "global" is the simplest and obvious choice if you want to do multiple testing across all the probes and all contrasts simultaneously. The raw p-value cutoff is consistent across all contrasts. However you have to be careful that you do not include spurious contrasts in your test set because they will affect the results of the others. There is no theorem which says that "BH" will hold correctly for combinations of negatively correlated contrasts. However simulations suggest that it is pretty safe. I suggest you avoid "hierarchical" as it is still experimental and its properties are not yet well understood with "BH". Most multiple testing methods tend to underestimate the number of probes which are simultaneously significant for two or more contrasts. In my experience, "nestedF" gives good results when you want to focus on probes which respond to several different contrasts at once. However this method is virtually undocumentated as I have not found time to write the method up properly. It also provides formal fdr control at the probe level only, not at the contrast level. Do not use it if either of these things bothers you. Hope this helps Gordon > Date: Wed, 19 Dec 2007 16:17:47 -0000 > From: "Al Ivens" <alicat at="" sanger.ac.uk=""> > Subject: [BioC] limma: decideTests > To: "'bioc'" <bioconductor at="" stat.math.ethz.ch=""> > Message-ID: <006501c8425a$b5051c40$881c13ac at internal.sanger.ac.uk> > > Hi all, > > I am doing some analyses of Affy arrays using limma. Following the > manual, I generated the fit2 object using the default eBayes settings, > and topTable with (almost) default settings. > >> dim(eset) > Features Samples > 22625 15 >> fit <- lmFit(eset,designMATRIX) >> fit2 <- eBayes(contrasts.fit(fit,contrast.matrix)) >> TT <- topTable(fit2,adjust.method="BH",number=length(fit2$genes)) > > I am now using decideTests, and have read the help that goes with it. > However, I still can't decide (no pun intended) on what is the best > method ("separate", "global", "hierarchical", "nestedF") to apply with > what adjust.method ("none", "BH", "fdr", "BY", "holm"). I have so far > left adjust.method as "BH", and varied the method. I can chose one > (global) that gives me most of the genes I "want", but I'd rather chose > the method more objectively. I have 5 contrasts, so am guessing from > the email archives that nestedF is probably not appropriate for so few > contrasts? > > Are there any general guidelines as to which is the best method to use > when? > > Thanks in anticipation, > > a
probe affy limma • 1.0k views
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