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Abstract Details

Activity Number: 558
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304909
Title: Bayesian Meta-Analysis for Pathway Enrichment Analysis
Author(s): Miao Zang*+ and Sherry Wang and Andy Xiao and Min Chen
Companies: Southern Methodist University and Southern Methodist University and The University of Texas Southwestern Medical Center and The University of Texas Southwestern Medical Center at Dallas
Address: 5454 Amesbury Dr., Dallas, TX, 75206, United States
Keywords: Bayesian ; Pathway analysis ; Meta-analysis
Abstract:

Many pathway analysis (or gene set enrichment analysis) methods have been developed to identify enriched pathways under different biological conditions. Meta-analysis methods have also been developed to integrate information among multiple studies. Currently, most meta-analysis methods for combining genomic studies focus on biomarker detection or identification of differentially expressed genes. Few meta-analysis methods for pathway analysis are available. In this research, a Bayesian hierarchical model is proposed to identify enriched pathways from multiple gene expression datasets, and is compared to three meta-analysis methods introduced by Shen and Tseng (2010, Bioinformatics): MAPE_P, MAPE_G and MAPE_I. Through intensive simulation studies, we show the proposed method outperforms the others. Further, it can explicitly identify which pathways are up-regulated and which are down-regulated while the other methods cannot without appropriate modification. We apply the proposed approach to both simulated datasets and gene expression datasets in lung cancer to compare the performance.


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