That's because you (and many other people) actually want to filter on the
(posterior) probability that the log fold change is bigger than some
(prespecified) value that you believe is biologically meaningful. I have no
idea how to accomplish this with topGO, but if you can find a way to
compute that probability, then topGO can filter your list.
On Aug 7, 2013 7:47 AM, "Enrico Ferrero" <enricoferrero86@gmail.com> wrote:

> 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
>
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