Hi  Monther,

I have done some testing with EGSEA using microarray data (a couple of thousand analyses). And as far as I can tell it is working fine! A few remarks.

1. symbolsMap = y$genes[, c(1, 3)] needs to be changed to something like symbolsMap=row.names(featureData(eSet)@data). Apparently the vooma object does not include the "genes" slot (genes dataframe of gene annotation, only if counts was a DGEList object). 

2. It does not seem to be doing multi-threading very effectively (processor activity remains at more or less the same level). But I was using the "custom" gene sets option (one gene set at a time). So maybe the parallelization is done differently. But at least in my case it might be better to give the function just 1 thread, split the data into lists and to do the parallelization with the Biocparallel "bplapply".

3. The results object is very complicated. I quite like the "biobroom" Bioconductor package that does "tidy" data frames from limma results objects. I wrote similar routines for my analysis. Egsea results are at: gsa@results$custom$test.results[[c]] where the c is a contrast (need to lapply/do.call over all of the contrasts) and then do the same for the individual methods which are at: gsa@results$custom$base.results[[c]]$ora (for the ora method). 

4. The visualization routine takes enormous amounts of time to run and if you have e.g., 9 contrasts in your dataset which generates a huge number of combinations. But I suppose it is useful for smaller analyses.

5. I wonder if some of the methods (like GSVA and ROAST) should be run in the "absolute i.e., mixed" or the "directional" mode. But I suppose if one cares about that then it is possible to customize the "ensemble" method accordingly.

6. Some methods like the GSVA require at least 10 samples to be effective (don't know if applies to others).

All in all a very useful package! Both for automating the running of lots of methods at the same time and of course for the "ensemble" method.