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Article
Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data
Bioinformatics (2012)
  • Yihan Li
  • Debashis Ghosh, university of colorado denver
Abstract

MOTIVATION:There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically.

RESULTS: In this article, we propose 'assumption weighting', which exploits a weighted hypothesis testing framework proposed by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets.

Publication Date
2012
Citation Information
Yihan Li and Debashis Ghosh. "Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data" Bioinformatics (2012)
Available at: http://works.bepress.com/debashis_ghosh/70/