title: Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Statistics, Penn State University, University Park, PA 16802, USA.
authors.forename: Yihan
authors.initials: Y
authors.lastname: Li
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
mesh_terms: D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D013234:Stem Cells / Q000378:metabolism*