[BioC] Why NONE of any contrast is significant when overall F-test is significant? using "global" method in decideTests() from limma package
guiyuanlei at gmail.com
Thu Feb 28 17:26:37 CET 2008
Thank you, Jim.
I also tried "separate" and "nestedF". For "separate" method, there
are also genes (around 8% of 397 significantly differential genes by
F-test) got five '0's. As for "nestedF", every gene got at least one
In the post https://stat.ethz.ch/pipermail/bioconductor/2007-April/016698.html
'method="separate" and method="nestedF" do quite different things.
"separate" controls the FDR on a per-contrast basis only. It does not
control the FDR globally across all contrasts. '
Why "separate" method also generated five "0"s (5 contrasts) for
significantly differential genes by F-test?
On Thu, Feb 28, 2008 at 3:54 PM, James W. MacDonald
<jmacdon at med.umich.edu> wrote:
> Hi Guiyan,
> Guiyuan Lei wrote:
> > Dear all,
> > I am using limma package to identify differential expression. I have 5
> > contrasts, I used F-statistic to measure significant differential
> > expression, the F-test p-value is adjusted by "fdr" method . I used
> > the command
> > p.adjust(eb$F.p.value, method="fdr") < 0.05
> > where eb is the object from eBayes(). I got 397 significant
> > differential expression.
> > Then for those 397 significantly differential expressed genes, I want
> > to look at each contrast to check which contrast is significant. I
> > used
> > decideTests(eb, method="global")
> > to classify each t-statistics as up, down or not significant.
> > I found that among genes which are significantly expressed by F-test
> > p-value, that is, the above 397 genes, some have got five '0's from
> > decideTests, which means those genes are not significant for any of
> > its contrast while its F-test is significant. As I understand, if
> > F-test is significant, should at least one of its contrast (t-test) is
> > significant. I doubt that the "global" method used in decideTests() is
> > not properly used for this case? But why? Can any one explain this to
> > me? Many thanks! I attach the code and results as following.
> Yes. From ?decideTests:
> 'method="global"' will treat the entire matrix of t-statistics as
> a single vector of unrelated tests.
> So when you did the F-test and adjusted for multiplicity, you adjusted
> for n tests. Then when you did the t-tests, you did the same, but
> adjusting for 5n tests. Increasing the number of simultaneous tests
> five-fold was enough to cause your multiplicity-adjusted p-values to all
> become insignificant at an alpha of 0.05.
> You might try method="separate" or "nestedF".
> > library(limma)
> > levels= c('h','h','h','hh','hh','mh','mh','s','s','s','y','y','y','y')
> > dimnames(eset.matrix)[]= levels
> > TS <- factor(levels, levels= c('h','hh','mh','s','y'))
> > design <- model.matrix(~0+TS)
> > colnames(design) <- levels(TS)
> > fit <- lmFit(eset.gcrma, design)
> > #Construct the contrasts
> > mc <- makeContrasts('s-y','h-y','mh-y','hh-h','mh-hh',levels=design)
> > fit2 <- contrasts.fit(fit, mc)
> > eb <- eBayes(fit2)
> > #The adjustment methods using "fdr"
> > modFpvalue <- eb$F.p.value
> > selectedgenesindx <- p.adjust(eb$F.p.value, method="fdr") < 0.05
> > Sig<-modFpvalue[selectedgenesindx]
> > nsiggenes<-length(Sig) #number of significantly differential expression
> > #decideTests using "global" method
> > results1 <- decideTests(eb, method="global")
> > #Order the F-statistic
> > modF <- eb$F
> > modFordered<-order(modF, decreasing = TRUE)
> > #Get the up or down or not significant for nsiggenes significant
> > (according adjusted p-value of F-test) differential expression
> > updown<-results1[modFordered[1:nsiggenes],]
> > Examples for genes which are significant expression by F-test
> > F-test results from decideTests()
> > eb$F.p.value contrast1 contrast2 contrast3 contrast4 contrast4
> > 5.6E-05 0 0 0 0 0
> > (this one is not significant for any of its contrast, why five '0's?)
> > 0.00036 0 -1 0 0 0
> > Best regards,
> > Guiyuan
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/bioconductor
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> James W. MacDonald, M.S.
> Affymetrix and cDNA Microarray Core
> University of Michigan Cancer Center
> 1500 E. Medical Center Drive
> 7410 CCGC
> Ann Arbor MI 48109
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