Online Program Home
My Program

Abstract Details

Activity Number: 233 - ASA Biometrics Section JSM Travel Awards (I)
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #327237 Presentation
Title: Bayesian Latent Hierarchical Model for Transcriptomic Meta-Analysis to Detect Biomarkers with Clustered Meta-Patterns of Differential Expression Signals
Author(s): Zhiguang Huo* and Chi Song and George Tseng
Companies: University of Florida and Ohio State University and University of Pittsburgh
Keywords: transcriptomic differential analysis; meta-analysis ; Bayesian hierarchical model; Dirichlet process

Due to 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 statistical power to detect disease related biomarkers. 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 two examples including a microarray dataset from metabolism related knockout mice, and an RNA-seq dataset from HIV transgenic rats are used to demonstrate performance of the proposed method.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program