Open Access
June 2012 Modeling dependent gene expression
Donatello Telesca, Peter Müller, Giovanni Parmigiani, Ralph S. Freedman
Ann. Appl. Stat. 6(2): 542-560 (June 2012). DOI: 10.1214/11-AOAS525


In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.


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Donatello Telesca. Peter Müller. Giovanni Parmigiani. Ralph S. Freedman. "Modeling dependent gene expression." Ann. Appl. Stat. 6 (2) 542 - 560, June 2012.


Published: June 2012
First available in Project Euclid: 11 June 2012

zbMATH: 1243.62038
MathSciNet: MR2976482
Digital Object Identifier: 10.1214/11-AOAS525

Keywords: Conditional independence , microarray data , probability of expression , probit models , reciprocal graphs , reversible jumps MCMC

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 2 • June 2012
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