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Abstract Details
Activity Number:
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418
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #302294 |
Title:
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An Empirical Bayesian Model for Identifying Differentially Co-Expressed Genes
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Author(s):
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John Alexander Dawson*+ and Christina Kendziorski
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Companies:
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University of Wisconsin at Madison and University of Wisconsin at Madison
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Address:
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Department of Statistics, Madison, WI, 53706,
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Keywords:
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Empirical Bayes ;
Differential correlation ;
Gene Expression ;
Microarray ;
Meta-analysis
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Abstract:
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A common goal of high-throughput genomic experiments is to identify genes that vary across biological conditions. Most often this is accomplished by identifying differentially expressed genes and many effective methods have been developed for this task. Although useful, these approaches do not accommodate other types of differential regulation, such as differential co-expression (DC). Investigations of DC genes are hampered by large search-space cardinality and outliers and as a result, existing DC approaches are often underpowered, prone to false discoveries, and/or computationally intractable for even moderately sized datasets. To address this, an empirical Bayesian approach is developed for identifying DC gene pairs within a single study or across multiple studies. The approach provides a false discovery rate (FDR) controlled list of significant DC gene pairs without sacrificing power. Computational complexity is eased by a modification of the EM algorithm and procedural heuristics. Simulations suggest that the approach outperforms existing methods; and case study results demonstrate utility of the approach in practice.
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