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Due to the rapid development of high-throughput experimental techniques andfast-dropping prices, many transcriptomic datasets have been generated andaccumulated in the public domain. Meta-analysis combining multipletranscriptomic studies can increase the statistical power to detectdisease-related biomarkers. In this paper, we introduce a Bayesian latenthierarchical model to perform transcriptomic meta-analysis. This method iscapable of detecting genes that are differentially expressed (DE) in only asubset of the combined studies, and the latent variables help quantifyhomogeneous and heterogeneous differential expression signals across studies. Atight clustering algorithm is applied to detected biomarkers to capturedifferential meta-patterns that are informative to guide further biologicalinvestigation. Simulations and three examples, including a microarray datasetfrom metabolism-related knockout mice, an RNA-seq dataset from HIV transgenicrats, and cross-platform datasets from human breast cancer, are used todemonstrate the performance of the proposed method.

annals of applied statistics/the annals of applied statistics

Bayesian latent hierarchical model for transcriptomic meta-analysis to  detect biomarkers with clustered meta-patterns of differential expression  signals