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