GSEA preranked different results opposing phenotypes I am running an analysis of two phenotypes: mutant and control.
I have a statistic used to rank the different genes, and the ranking is exactly the opposite between mutant and control. Moreover, there are no duplicates in the ranking (which excludes that genes are ranked alphabetically). I uploaded the test file here.
My expectation is that running GSEA using mutant as positive phenotype, should give me the same results (but in opposite direction) than running GSEA using control as a positive phenotype - since there are only two phenotypes, and the ranking statistic is computed mutant vs control.
However, when I run Gene Set Enrichment Analysis using mutant as positive phenotype, the results I get are not the same with respect to running GSEA using mutant as negative phenotype.
I initially thought that it might be related to the choice of the random seed, however, I get the same results even setting the same random seed in all runs.
The following is an example of an enriched result of the positive phenotype:


The same results with the negative phenotype:


The ranking is exactly the same, however, the leading edge is different in the two cases. It's probably due to the fact that the ranking sum goes in one direction (increasing the enrichment score) in both phenotypes. This is probably the cause of different scores in the opposite phenotypes, however, which is the result I should use in this case?
What do you mean by having the same results but opposite direction?
What GSEA does is that it goes through the gene list from the top to bottom, whenever it encounters a gene that belongs to a gene set, it adds a positive score to that gene set, and if not, it penalizes the score. That is what you see in the green line in the results.
Now imagine that you reverse the order of your input gene list, it is obvious the results are going to be different if the program still goes through the list from top to bottom.
I think as a general rule, whenever you change the order of input gene list, results are going to be different.