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Abstract:
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Due to rapid development of high-throughput experimental tech- niques and fast dropping prices, many transcriptomic datasets have been generated and accumulated in the public domain. Meta-analysis combining multiple transcriptomic studies can increase statistical power to detect disease related biomarkers. In this paper, we intro- duce a Bayesian latent hierarchical model to perform transcriptomic meta-analysis. This method is capable of detecting genes that are dif- ferentially expressed (DE) in only a subset of the combined studies, and the latent variables help quantify homogeneous and heteroge- neous differential expression signals across studies. A tight clustering algorithm is applied to detected biomarkers to capture differential meta-patterns that are informative to guide further biological inves- tigation. Simulations and three examples using a microarray dataset from metabolism related knockout mice, an RNA-seq dataset from HIV transgenic rats and cross-platforms prostate cancer datasets are used to demonstrate performance of the proposed method.
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