[BioC] LIMMA decideTests result zero from contrast matrix
Ekta_Jain at jubilantbiosys.com
Wed May 9 10:31:47 CEST 2012
Thank you very much. It appears much clear now. I agree, I wouldn't want to apply the filter to unadjusted P values since it just shows that the results obtained are very insignificant. I did not understand in detail how the decideTests() works and was looking for some info regarding the same in case someone else had a similar issue. The venn Diagrams are not important if the data is bad, it is not going to lead anywhere. It was very intriguing when summary(decideTests) gave zeros for all contrasts and now I know exactly why.
Thank you kindly,
From: bioconductor-bounces at r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Alex Gutteridge
Sent: 09 May 2012 13:50
To: bioconductor at r-project.org
Subject: Re: [BioC] LIMMA decideTests result zero from contrast matrix
On 09.05.2012 04:21, Ekta Jain wrote:
> Dear Jim,
> I did change things around when you pointed out for the first time.
> All I have been doing is
>> numGenes <- rownames(eset)
>> topTable(fit2, coef=1, adjust="BH", sort.by="B", number=numGenes)
>> results<-decideTests(fit2, method ="global", lfc =0)
> As you mention in your email that "by default decideTests() uses a BH
> adjusted p-value to filter genes" so am i not applying the same
> adjustment for both the toptable() and decideTests() here?
> This is what I am clueless about since I still get zero genes. I
> cannot seem to figure out how to not let decideTests use a BH adjust
> for p-value. For the sake of detail, the code worked fine for all my
> cell lines treated with CPI since the p-values were not as bad as the
> ones for treatment with DMSO.
> Thank you,
I think it is the difference in the default P value cutoffs for
topTable and decideTests that is confusing the issue. From the
Look at the default p.value cutoff - 0.05. Earlier in the thread you
said your adjusted P values were ~0.9, hence *nothing* will come through
the filter. The default for topTable is 1:
topTable(fit, coef=NULL, number=10, genelist=fit$genes,
sort.by="B", resort.by=NULL, p.value=1, lfc=0,
If you want to apply the filter to unadjusted P values the docs say
adjust.method: character string specifying p-value adjustment method.
Possible values are ‘"none"’, ‘"BH"’, ‘"fdr"’ (equivalent to
‘"BH"’), ‘"BY"’ and ‘"holm"’. See ‘p.adjust’ for details.
Though I'm not sure why you would want to do this.
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