Correct p-value in GSA (Gene set enrichment) permutation tests? [Scanned.]
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@dirk-repsilber-3024
Last seen 9.6 years ago
Dear BioC Bioinformaticians, I am using the package GSA for testing gene set enrichment in gene expression data. GSA uses a permutation test for calculating p-values of enrichment. Such p-values are usually defined as p=(#(T* >= T)) / #B where T is the test statistics originally observed, #B the number of permutations and T* the test statistics observed for the permutated datasets. However, function GSA implemented p=(#(T* > T)) / #B (as is also defined in the belonging article) see article here: http://www-stat.stanford.edu/~tibs/ftp/GSA.pdf As a consequence, even for really insufficient small designs (say comparison of two independent groups, both of size 2) the resulting p-values contain a lot of cases with p=0. In my experience this is often the case for about half of the pathways under consideration. For larger designs this difference might not be that crucial, but for really small designs, I think that, this p-value calculation delivers far too overoptimistic results (too many "significant" pathways). Is there a motivation for this unusual p-value calculation or should the lines in the GSA function (original:) pvalues.hi[i] = sum(r.star[i, ] > r.obs[i])/nperms pvalues.lo[i] = sum(r.star[i, ] < r.obs[i])/nperms read instead: pvalues.hi[i] = sum(r.star[i, ] >= r.obs[i])/nperms pvalues.lo[i] = sum(r.star[i, ] <= r.obs[i])/nperms Would be grateful for any comments or clarifications!! sincerely Dirk. -- _____________________________________________________ Dr. Dirk Repsilber Biomathematics / Bioinformatics group Genetics and Biometry Research Institute for the Biology of Farm Animals FBN Wilhelm-Stahl-Allee 2 D-18196 Dummerstorf Tel: +49 38208 68 916 Fax: +49 38208 68 902 www.fbn-dummerstorf.de/de/Forschung/FBs/fb2/repsilber
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