[BioC] Extract microarray data for genes identified by GO analysis

James W. MacDonald jmacdon at uw.edu
Tue Feb 19 23:08:59 CET 2013


Hi Mark,

On 2/19/2013 4:41 PM, Mark Ebbert wrote:
> Hi,
>
> I am using GOstats to identify molecular functions that are over represented. I'm getting conflicting results between a method from an example I found in the lumi vignette and using probeSetSummary. Specifically, to get the list of significant categories, I was using the following code:
>
> mfOver1<- hyperGTest(params.mf1)
> mf.gGhyp.pv1<- pvalues(mfOver1)
> mf.sigGO.ID.pv1<- names(mf.gGhyp.pv1[mf.gGhyp.pv1<  0.05])
> mf.sigGO.Term.pv1<- getGOTerm(mf.sigGO.ID.pv1)[["MF"]]
> length(mf.sigGO.Term.pv1)
>
> The number of resulting GO terms based on this code is 105. If I use probeSetSummary, however, I only get 92 significant GO terms. Here is the code I'm using:
>
> ps1<-probeSetSummary(mfOver1,0.05,sigProbesets=probeList)
> length(ps1)
>
>
>
> My understanding is that both methods should select only those categories with a p-value<  0.05, but I have no doubt misunderstood something.

Or you made a mistake somewhere. If I run

example("probeSetSummary")

To get some faked up results; hyp, a HyperGTestResult object, and ps, 
the output from probeSetSummary. If I then do what you intended to do 
(noting that the example uses the default p-value of 0.01):

 > ps <- probeSetSummary(hyp, pvalue = 0.05)
 > length(ps)
[1] 700
 > sum(pvalues(hyp) < 0.05)
[1] 700
 > all.equal(names(pvalues(hyp)[pvalues(hyp) < 0.05]), names(ps))
[1] TRUE

Seems the same to me.

Best,

Jim

>
> Thanks for your help!
>
> Mark
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
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Seattle WA 98105-6099



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