Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

Sang Mee Lee, Baolin Wu, John H. Kersey

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)38-54
Number of pages17
JournalStatistics in Biosciences
Issue number1
StatePublished - May 2014

Bibliographical note

Funding Information:
Acknowledgements This research was supported in part by a Biomedical Informatics and Computational Biology research grant from the University of Minnesota-Rochester, and National Institute of Health grant CA134848 and GM083345. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. We would like to thank the associate editor and two anonymous referees for their constructive comments, which have dramatically improved the presentation of the paper.


  • EM
  • Finite mixture model
  • Gene set enrichment analysis


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