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

TL;DR

This paper introduces a Bayesian hierarchical model for transcriptomic meta-analysis that detects differentially expressed genes with clustered meta-patterns across studies, enhancing biomarker discovery.

Contribution

The proposed method uniquely models heterogeneous differential expression signals and clusters biomarkers to reveal informative meta-patterns, advancing transcriptomic meta-analysis techniques.

Findings

Effective detection of DE genes in subsets of studies

Captures homogeneous and heterogeneous expression signals

Identifies biologically meaningful meta-patterns

Abstract

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.