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