I am in the process of performing a meta-analysis on multiple MA studies run on several platforms and several species. Although I realize there are probably other ways to approach the data, my collaborators have chosen to use Cohen's D-statistic to summarize their data. I would like to use GSEA to look for over-represented groups or pathways in the data, but what I will end up with is essentially a one-class experiment. In other words, I would like to identify groups whose summary D-statistic (I recognize from the recent literature that there is more than one summary method available) is different from zero. From what I have read, the GSEA method is usually used to evaluate 2 class data, the GSA method of Tibshurani can be used to evaluate multi-class data, but can either method be easily adapted to evaluate one class data? It would seem theoretically reasonable, but I don't know if I would have to modify one of the packages or if one already provides for this. And, while I'm at it, I will also want to look for individual genes whose average D-statistic is different from zero. I am advocating using a t-test with n=number of experiments with corresponding degrees of freedom, whereas others are advocating using a z-test because the D-statistics are summarizing many underlying individual statistics. Help with these questions would be deeply appreciated. Mark -- Mark W. Kimpel MD Neuroinformatics Department of Psychiatry Indiana University School of Medicine