Computation of significance scores of unweighted Gene Set Enrichment Analyses

@article{citeulike:1538192, abstract = {BACKGROUND:Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values.RESULTS:We present a novel dynamic programming algorithm for calculating exact significance values of unweighted Gene Set Enrichment Analyses. Our algorithm avoids typical problems of nonparametric permutation tests, as varying findings in different runs caused by the random sampling procedure. Another advantage of the presented dynamic programming algorithm is its runtime and memory efficiency. To test our algorithm, we applied it not only to simulated data sets, but additionally evaluated expression profiles of squamous cell lung cancer tissue and autologous unaffected tissue.}, author = {Keller, Andreas and Backes, Christina and Lenhof, Hans P. }, citeulike-article-id = {1538192}, doi = {10.1186/1471-2105-8-290}, journal = {BMC Bioinformatics}, keywords = {gene\_sets}, number = {1}, posted-at = {2008-05-16 15:48:02}, priority = {2}, title = {Computation of significance scores of unweighted Gene Set Enrichment Analyses}, url = {http://dx.doi.org/10.1186/1471-2105-8-290}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1538192 L3 - citeulike-article-id:1538192 TI - Computation of significance scores of unweighted Gene Set Enrichment Analyses JF - BMC Bioinformatics VL - 8 IS - 1 N2 - BACKGROUND:Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values.RESULTS:We present a novel dynamic programming algorithm for calculating exact significance values of unweighted Gene Set Enrichment Analyses. Our algorithm avoids typical problems of nonparametric permutation tests, as varying findings in different runs caused by the random sampling procedure. Another advantage of the presented dynamic programming algorithm is its runtime and memory efficiency. To test our algorithm, we applied it not only to simulated data sets, but additionally evaluated expression profiles of squamous cell lung cancer tissue and autologous unaffected tissue. KW - gene_sets AU - Keller, Andreas AU - Backes, Christina AU - Lenhof, Hans PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-290 ER -