title: Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model.
abstract: In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building MMC 303, 420 Delaware St SE, Minneapolis, MN 55455, USA.
authors.forename: Sang Mee
authors.initials: SM
authors.lastname: Lee
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building MMC 303, 420 Delaware St SE, Minneapolis, MN 55455, USA.
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
authors.affiliation: Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: John H
authors.initials: JH
authors.lastname: Kersey
keywords: EM; Finite mixture model; Gene set enrichment analysis