JSM 2011 Online Program

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Abstract Details

Activity Number: 418
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #302294
Title: An Empirical Bayesian Model for Identifying Differentially Co-Expressed Genes
Author(s): John Alexander Dawson*+ and Christina Kendziorski
Companies: University of Wisconsin at Madison and University of Wisconsin at Madison
Address: Department of Statistics, Madison, WI, 53706,
Keywords: Empirical Bayes ; Differential correlation ; Gene Expression ; Microarray ; Meta-analysis
Abstract:

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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