A Bayesian model for cross-study differential gene expression.

  title={A Bayesian model for cross-study differential gene expression.},
  author={Robert B. Scharpf and H{\aa}kon Tjelmeland and Giovanni Parmigiani and Andrew B. Nobel},
  journal={Journal of the American Statistical Association},
  volume={104 488},
In this paper we define a hierarchical Bayesian model for microarray expression data collected from several studies and use it to identify genes that show differential expression between two conditions. Key features include shrinkage across both genes and studies, and flexible modeling that allows for interactions between platforms and the estimated effect, as well as concordant and discordant differential expression across studies. We evaluated the performance of our model in a comprehensive… CONTINUE READING


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Statistical Methods for Meta-analysis

  • L. V. Hedges, I. Olkin
  • 1985
Highly Influential
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