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Activity Number: 183 - SPEED: Bayesian Methods Student Awards
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 11:15 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #325091
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: Department of Biostatistics, University of Pittsburgh and University of Florida and University of Pittsburgh
Keywords: Bayesian hierarchical model ; differential expression (DE) ; meta-analysis ; model-based clustering ; Ribonucleic acid sequencing (RNA-seq)
Abstract:

Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become mature and 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 to each study and then to combine the summary statistics by conventional meta-analysis methods. Such a two-stage approach loses statistical power, especially for genes with short length or low expression abundance. We propose a full Bayesian hierarchical model (namely, BayesMetaSeq) for RNA-seq meta-analysis by modeling count data, integrating information across studies, and modeling potentially heterogeneous differential signals across studies via latent variables. A Dirichlet process mixture (DPM) prior is further applied on the latent variables to provide categorization of detected biomarkers according to their differential expression patterns across studies. Simulations and a real application on HIV transgenic rats demonstrate improved sensitivity, accuracy and biological findings of the method.


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

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