<p>Hello Biostar,</p>

<p>I'm performing some Gene Ontology enrichment analysis using R/Bioconductor and, as far as I understand, there are two packages that allow to do this: topGO and GOstats (I'm not interested in packages that compare multiple datasets in this scenario). </p>

<p>The main difference seems to be the statistics used: GOstats uses the hypergeometric distribution (basically the standard way to test for overrepresentation), while topGO allows the user to use a much wider and complete range of algorithms and statistical tests to check for enrichment.</p>

<p>With regards to graphic capabilities, topGO seems to be producing much better graphs than GOstats, but I have only looked at the packages' vignettes.</p>

<p>Has anybody here experience with both? Which one did you end up using? Why?</p>

<p>Also, on a related note, what would be the topGO algorithm/statistics test combination that emulates GOstats' hypergeometrical test? Would it be the classic/fisher combination (used as the most basic example in topGO's vignette)?</p>

<p>Thank you.</p>

To avoid the following <a href="http://xkcd.com/979/">situation</a>:

I tried both GOstats and topGO briefly before switching to topGO for all of my GO enrichment analysis in R. Its been a while but from what I remember it seemed more versatile, i.e. one can employ various default algorithm/test combinations (along with the capability of customized tests).

It seems reasonable that the classic/fisher would emulate GOstats hypergeometric test, though I have not tested this personally.

I found this was a <a href="http://www.bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO.pdf">good resource for working with topGO</a>.