[BioC] reasonable Illumina hyperG test

[michael watson (IAH-C)]

To me, it depends on where sig comes from.  

Did you select "sig" before or after you filtered for IQR?  If you did it before, then (to me) you have falsely reduced your universe;  however, if you did it after, everything seems ok.


-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch on behalf of Sebastien Gerega
Sent: Fri 05/09/2008 6:18 AM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] reasonable Illumina hyperG test
 
Hi,
I have been looking around at examples of the hyperGTest (in the 
GOstats, lumi, and other documentation) and feel like I have seen many 
slight variations on the methodology.
These variations are usually found in the way the non-specific filtering 
is performed. I haven't come across many examples of a hyperGTest for 
KEGG pathways and would like to ask whether my approach seems reasonable 
or whether I should make any changes.
Here is my code ("sig" is a vector of EntrezID):

uni = exprs(lumi.N.P)

#Remove those without PATH annotation
havePATH = sapply(mget(allFeatures, lumiHumanAllPATH),
function(x){
    if (length(x) == 1 && is.na(x))
    FALSE
    else TRUE
})
uni <- uni[names(which(havePATH == TRUE)),]

#Remove those with little variation accross samples
iqrCutoff = 0.5
uni.IQR = apply(uni, 1, IQR)
uni = uni[which((uni.IQR > iqrCutoff) == TRUE),]

#Keep probes w/largest IQR
uni = uni[findLargest(rownames(uni), uni.IQR[rownames(uni)], 
"lumiHumanAll"),]
uni = mget(rownames(uni), lumiHumanAllENTREZID)

params = new("KEGGHyperGParams", geneIds=sig, universeGeneIds = uni, 
annotation="lumiHumanAll", pvalueCutoff=0.05, testDirection="over")

hgOver = hyperGTest(params)


Does this code/approach seem reasonable? Should I correct for multiple 
testing after the hyperGTest?
Would it be fair to perform a test on gene ontologies in teh same way 
(obviously after having changed the param type and specifying an 
ontology branch)?

thanks,
Sebastien

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