[BioC] Can gage not use large gene sets for pathway analysis?

[Luo Weijun]

Hi Richard,
It is normal that you don’t get any significant calls in a pathway analysis (with multiple pathways/tests) because none of the p-values (or q-values) is small enough. Very likely, there is not enough testing power with your data given its sample size, noise level and experiment quality. The adjusted p-values (or q-values) would be different when the total number of tests/pathways changes.
With your current dataset, you may do 2 things:
-Loosen the selection criteria (q-value cutoff), the option cutoff = 0.1 in sigGeneSet function and  q.cutoff = 0.1in gagePipe function can be set to a bigger value, say 0.2 etc.
-change the gene set size filter to include more pathways that are actually tested, the argument in gage function is set.size = c(10, 500). You can set it to be set.size = c(10, 2000) or even set.size = c(10, Inf).
HTH

--------------------------------------------
On Wed, 12/4/13, Richard <Richard@> wrote:

 Subject: Can gage not use large gene sets for pathway analysis?
 To: bioconductor at r-project.org, mao at life.uiuc.edu

 Date: Wednesday, December 4, 2013, 10:28 AM


 Hi Weijun Luo,
 Using your two packages of gage and pathview, I have
 finished pathway analysis of signal transduction and
 identified two disturbed pathways from 40 pathways. But I
 met problem with metabolism pathway analysis, because I
 could not find any disturbed pathway with the metabolism
 gene sets (82 pathways) from Kegg. More weird, If I used
 whole gene sets for honeybees from kegg including the
 met/sig gene sets 122 pathways, the two disturbed signal
 pathways could not be find too. Some guys told me that this
 resulted from more gene sets, which increase p. values of
 each gene set. Is it true, and how can I solve this
 problem?
 Thanks a lot!!
 Richard

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