  Due to the rapid development of high-throughput experimental techniques and
fast-dropping prices, many transcriptomic datasets have been generated and
accumulated in the public domain. Meta-analysis combining multiple
transcriptomic studies can increase the statistical power to detect
disease-related biomarkers. In this paper, we introduce a Bayesian latent
hierarchical model to perform transcriptomic meta-analysis. This method is
capable of detecting genes that are differentially expressed (DE) in only a
subset of the combined studies, and the latent variables help quantify
homogeneous and heterogeneous 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
investigation. Simulations and three examples, including a microarray dataset
from metabolism-related knockout mice, an RNA-seq dataset from HIV transgenic
rats, and cross-platform datasets from human breast cancer, are used to
demonstrate the performance of the proposed method.
