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Hidden Markov Random Field Models for Network-Based Analysis of Genomic Data

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

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Abstract

Graphs and networks are common ways of depicting biological information. In biology, many different biological processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This kind of a priori use of graphs is a useful supplement to the standard numerical data such as microarray gene expression data and single nucleotide polymorphisms (SNPs) data. How to incorporate such a prior network information into analysis of numerical data raises interesting statistical problems. Representing the genetic networks as undirected graphs, we have developed several approaches for identifying differentially expressed genes and genes or SNPs associated with diseases in a unified framework of hidden Markov random field (HMRF) models. Different from the traditional empirical Bayes approaches for analysis of gene expression data, the HMRF-based models account for the prior dependency among the genes on the network and therefore effectively utilize the prior network information in identifying the subnetworks of genes that are perturbed by experimental conditions. In this paper, we briefly review the basic setup of the HMRF models and the emission probability functions for some problems often encountered in analysis of microarray gene expression and SNPs data. We also present some interesting areas that require further research.

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Acknowledgements

This research was supported in part by NIH grants ES009911 and CA127334.

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Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, USA

    Hongzhe Li

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  1. Hongzhe Li
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Correspondence to Hongzhe Li .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Li, H. (2011). Hidden Markov Random Field Models for Network-Based Analysis of Genomic Data. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_17

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