Comparative study of gene set enrichment methods Export

@article{citeulike:5705840, abstract = {BACKGROUND:The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.RESULTS:The simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap.CONCLUSION:The three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods.}, author = {Abatangelo, Luca and Maglietta, Rosalia and Distaso, Angela and D'Addabbo, Annarita and Creanza, Teresa and Mukherjee, Sayan and Ancona, Nicola}, citeulike-article-id = {5705840}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-275}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19725948}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19725948}, doi = {10.1186/1471-2105-10-275}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {comparison, enrichment, gba, overrepresentation, review}, number = {1}, pages = {275+}, posted-at = {2009-09-03 09:39:57}, priority = {5}, title = {Comparative study of gene set enrichment methods}, url = {http://dx.doi.org/10.1186/1471-2105-10-275}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:5705840 L3 - citeulike-article-id:5705840 N2 - BACKGROUND:The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.RESULTS:The simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap.CONCLUSION:The three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Comparative study of gene set enrichment methods VL - 10 SP - 275 KW - comparison KW - enrichment KW - gba KW - overrepresentation KW - review AU - Abatangelo, Luca AU - Maglietta, Rosalia AU - Distaso, Angela AU - D'Addabbo, Annarita AU - Creanza, Teresa AU - Mukherjee, Sayan AU - Ancona, Nicola PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-275 ER -