Network-based empirical bayes methods for linear models with applications to genomic data

Caiyan Li, Zhi Wei, Hongzhe Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Empirical Bayes methods are widely used in the analysis of microarray gene expression data in order to identify the differentially expressed genes or genes that are associated with other general phenotypes. Available methods often assume that genes are independent. However, genes are expected to function interactively and to form molecular modules to affect the phenotypes. In order to account for regulatory dependency among genes, we propose in this paper a network-based empirical Bayes method for analyzing genomic data in the framework of linear models, where the dependency of genes is modeled by a discrete Markov random field defined on a predefined biological network. This method provides a statistical framework for integrating the known biological network information into the analysis of genomic data. We present an iterated conditional mode algorithm for parameter estimation and for estimating the posterior probabilities using Gibbs sampling. We demonstrate the application of the proposed methods using simulations and analysis of a human brain aging microarray gene expression data set.

Original languageEnglish (US)
Pages (from-to)209-222
Number of pages14
JournalJournal of Biopharmaceutical Statistics
Volume20
Issue number2
DOIs
StatePublished - Mar 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

Keywords

  • Gibbs sampling
  • Markov random field
  • Molecular modules

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