Meta-analysis approaches to combine multiple gene set enrichment studies

Wentao Lu, Xinlei Wang, Xiaowei Zhan, Adi F Gazdar

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.

Original languageEnglish (US)
Pages (from-to)659-672
Number of pages14
JournalStatistics in Medicine
Issue number4
StatePublished - Feb 20 2018


  • GSEA
  • MAPE
  • adjusted Kolmogorov-Smirnov statistic
  • between-study heterogeneity
  • fixed effect
  • generalized linear model
  • integrative GSEA
  • random effects

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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