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Network-Based Analysis of Multivariate Gene Expression Data

  • Wei Zhi
  • Jane Minturn
  • Eric Rappaport
  • Garrett Brodeur
  • Hongzhe LiEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 972)

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 modified 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 identified genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.

Key words

Markov random field Empirical Bayes KEGG pathways 

Notes

Acknowledgments

This research was supported by NIH grants R01-CA127334 and P01-CA097323. We thank Mr. Edmund Weisberg, MS at Penn CCEB for editorial assistance.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Wei Zhi
    • 1
  • Jane Minturn
    • 2
  • Eric Rappaport
    • 3
  • Garrett Brodeur
    • 2
  • Hongzhe Li
    • 4
    Email author
  1. 1.Department of Biostatistics and EpidemiologyNew Jersey Institute of TechnologyNewarkUSA
  2. 2.Department of PediatricsChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  3. 3.Children’s Hospital of PhiladelphiaPhiladelphiaUSA
  4. 4.Department of Biostatistics and EpidemiologyUniversity of Pennsylvania School of MedicinePhiladelphiaUSA

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