Modeling dependent gene expression

Donatello Telesca, Peter Müller, Giovanni Parmigiani, Ralph S. Freedman

Research output: Contribution to journalArticlepeer-review

Abstract

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 valuate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.

Original languageEnglish (US)
Pages (from-to)542-560
Number of pages19
JournalAnnals of Applied Statistics
Volume6
Issue number2
DOIs
StatePublished - Jun 2012
Externally publishedYes

Keywords

  • Conditional independence
  • Microarray data
  • Probability of expression
  • Probit models
  • Reciprocal graphs
  • Reversible jumps MCMC

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

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