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Activity Number: 322
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #318995
Title: Biomarker Detection and Categorization in RNA-Seq Meta-Analysis Using Bayesian Hierarchical Model
Author(s): Tianzhou Ma* and Faming Liang and George Tseng
Companies: University of Pittsburgh and University of Florida and University of Pittsburgh
Keywords: Bayesian hierarchical model ; Diff erential expression (DE) ; Meta-analysis ; Model-based clustering ; RNA sequencing (RNA-seq)

Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become affordable, increasing number of RNA-seq datasets are available in the public domain. A naive approach to combine multiple RNA-seq studies is to apply differential analysis tools such as edgeR and DESeq to each study and then combine their p-values by conventional meta-analysis methods. Such a two-stage approach loses statistical power, especially for genes with short length or low expression abundance. In this paper, we propose a full Bayesian hierarchical model (namely, BayesMetaSeq) for RNA-seq meta-analysis by modeling count data, integrating information across studies, and modeling homogeneous and heterogeneous differential signals across studies. Model-based clustering embedded in the Bayesian model provides categorization of detected biomarkers according to their differential expression patterns across studies. Simulation and an RNA-seq application on multi-brain-region HIV-1 transgenic rats demonstrate improved sensitivity, accuracy and biological findings of the proposed method.

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

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