[BioC] [topGO] More flexible way to select 'interesting' genes

[Enrico Ferrero]

Hi,

Adrian, I'm using your topGO package and really appreciate how
powerful and customizable it is with respect to the choice of
algorithms and statistical tests.

There's just one limitation that I don't really understand, hopefully
you or somebody else in the list can shed some light on this.

I usually select 'interesting' genes from gene expression experiments
based on two different parameters: their p-value and their
log(fold-change).

As far as I understand, if I want to run a GO enrichment analysis in
topGO using a statistical test that uses ranked gene lists such as KS
(Kolmogorov–Smirnov test, also used by GSEA), I can only filter on one
paramater (tipically the p-value).

This is a consequence of the way the topGOdata objects are built, e.g.:

myData <- new("topGOdata", description="myData", ontology="BP",
allGenes=myAllGenes, geneSel=geneSelFunc, nodeSize=5, annot=annFUN.db,
affyLib="hgu133plus2.db")

where:

- myAllGenes is a named vector of all p-values for each probe on the
array, named after their probeID and

- geneSelFunc is a function to select the interesting ones, such as:

geneSelFunc <- function (score) {
    return(score <= 0.05)
}

I'm basically looking for a more flexible way to perform the selection
of my interesting probes: for example I'd like to only select probes
that have a p-value<=0.05 and a |log(fold-change)| >= 1.

Is there any way to do this?

Thank you.
Best,


-- 
Enrico Ferrero
PhD Student
Department of Genetics
Cambridge Systems Biology Centre
University of Cambridge