MOTIVATION: Several statistical tests are available to detect the enrichment of differential expression in gene sets. Such tests were originally proposed for analyzing gene sets associated with biological processes. The objective evaluation of tests on real measurements has not been possible as it is difficult to decide a priori, which processes will be affected in given experiments. RESULTS: We present a first large study to rigorously assess and compare the performance of gene set enrichment tests on real expression measurements. Gene sets are defined based on the targets of given regulators such as transcription factors (TFs) and microRNAs (miRNAs). In contrast to processes, TFs and miRNAs are amenable to direct perturbations, e.g. regulator over-expression or deletion. We assess the ability of 14 different statistical tests to predict the perturbations from expression measurements in Escherichia coli, Saccharomyces cerevisiae and human. We also analyze how performance depends on the quality and comprehensiveness of the regulator targets via a permutation approach. We find that ANOVA and Wilcoxons test consistently perform better than for instance Kolmogorov-Smirnov and hypergeometric tests. For scenarios where the optimal test is not known, we suggest to combine all evaluated tests into an unweighted consensus, which also performs well in our assessment. Our results provide a guide for the selection of existing tests as well as a basis for the development and assessment of novel tests.
MOTIVATION: Several statistical tests are available to detect the enrichment of differential expression in gene sets. Such tests were originally proposed for analyzing gene sets associated with biological processes. The objective evaluation of tests on real measurements has not been possible as it is difficult to decide a priori, which processes will be affected in given experiments. RESULTS: We present a first large study to rigorously assess and compare the performance of gene set enrichment tests on real expression measurements. Gene sets are defined based on the targets of given regulators such as transcription factors (TFs) and microRNAs (miRNAs). In contrast to processes, TFs and miRNAs are amenable to direct perturbations, e.g. regulator over-expression or deletion. We assess the ability of 14 different statistical tests to predict the perturbations from expression measurements in Escherichia coli, Saccharomyces cerevisiae and human. We also analyze how performance depends on the quality and comprehensiveness of the regulator targets via a permutation approach. We find that ANOVA and Wilcoxons test consistently perform better than for instance Kolmogorov-Smirnov and hypergeometric tests. For scenarios where the optimal test is not known, we suggest to combine all evaluated tests into an unweighted consensus, which also performs well in our assessment. Our results provide a guide for the selection of existing tests as well as a basis for the development and assessment of novel tests.
Authors: Kourosh Zarringhalam; Ahmed Enayetallah; Alex Gutteridge; Ben Sidders; Daniel Ziemek Journal: Bioinformatics Date: 2013-09-26 Impact factor: 6.937
Authors: Ludwig Geistlinger; Gergely Csaba; Mara Santarelli; Marcel Ramos; Lucas Schiffer; Nitesh Turaga; Charity Law; Sean Davis; Vincent Carey; Martin Morgan; Ralf Zimmer; Levi Waldron Journal: Brief Bioinform Date: 2021-01-18 Impact factor: 11.622
Authors: Haroon Naeem; Nicholas C Wong; Zac Chatterton; Matthew K H Hong; John S Pedersen; Niall M Corcoran; Christopher M Hovens; Geoff Macintyre Journal: BMC Genomics Date: 2014-01-22 Impact factor: 3.969
Authors: J O Cleaver; D You; D R Michaud; F A Guzmán Pruneda; M M Leiva Juarez; J Zhang; P M Weill; R Adachi; L Gong; S J Moghaddam; M E Poynter; M J Tuvim; S E Evans Journal: Mucosal Immunol Date: 2013-05-01 Impact factor: 7.313