[R-meta] Crusial question of possible implementation of methodologies/approaches in R for combining fishers exact test p values

[Στάθης Βλαχάβας]

Dear "R-sig-meta-analysis-group",

i would like to ask you a more general question about the possibility of
integrating/aggregating  p-values resulting from functional enrichment
analysis in R.

Briefly, in my research group i have developed an algorithm/pipeline of
ranking some functional enrichment analysis results, conserning pertubagen
experiments, based on various similarity metrics. Recently, i have also
added the Fishers exact test, and this is where perhaps is the crusial part
of my question:

as, the input of the user is a DEG list, separated in up and down genes,
then, for each "experiment" from a drug repository database, a separate
Fisher's exact test is performed: for the up genes versus the up genes from
the drug base, as also one for the down input genes and the relative down
from the base.

*Overall, from each experiment/gene-set tested, i have 2 Fisher's exact
test p-values*
*(with function: fisher.test(....alternative="greater")*

Thus, in your opinion:

Is my approach feasible to combine these 2 p-values, into a final
aggregated one? and if so, which of the methodologies should be more
appropriate_ ?

(*I also have to mention, that in the drug base repository, even for each
experiment are 2 "separate" gene-sets, up & down, these are resulted from
the same samples, etc. Only the gene list from the user is changing.*)

Any opinion or comment would be essential !!

Best Regards,

Efstathios-Iason Vlachavas

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