Network-based analysis of multivariate gene expression data

Wei Zhi, Jane Minturn, Eric Rappaport, Garrett Brodeur, Hongzhe Li

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes, where the expression levels of a give gene are expected to be dependent. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time; these genes can also point to the pathways that are perturbed during a given biological process. Several empirical Bayes approaches have been developed for identifying the differentially expressed genes in order to account for the parallel structure of the data and to borrow information across all the genes. However, these methods assume that the genes are independent. In this paper, we introduce an alternative empirical Bayes approach for analysis of multivariate gene expression data by assuming a discrete Markov random field (MRF) prior, where the dependency of the differential expression patterns of genes on the networks are modeled by a Markov random field. Simulation studies indicated that the method is quite effective in identifying genes and the modi fied subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway information, with similar observed false discovery rates. We applied the proposed methods for analysis of a microarray time course gene expression study of TrkA- and TrkB-transfected neuroblastoma cell lines and identi fied genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.

Original languageEnglish (US)
Title of host publicationStatistical Methods for Microarray Data Analysis
Subtitle of host publicationMethods and Protocols
PublisherHumana Press Inc.
Pages121-139
Number of pages19
ISBN (Print)9781603273367
DOIs
StatePublished - 2013

Publication series

NameMethods in Molecular Biology
Volume972
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

Keywords

  • Empirical Bayes
  • KEGG pathways
  • Markov random field

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