NDLI: Network-Based Empirical Bayes Methods for Linear Models with Applications to Genomic Data

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Network-Based Empirical Bayes Methods for Linear Models with Applications to Genomic Data

Content Provider	Scilit
Author	Li, Caiyan Wei, Zhi Li, Hongzhe
Copyright Year	2010
Description	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.
Related Links	http://europepmc.org/articles/pmc3422552?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3422552/pdf
Ending Page	222
Page Count	14
Starting Page	209
ISSN	10543406
e-ISSN	15205711
DOI	10.1080/10543400903572712
Journal	Journal of Biopharmaceutical Statistics
Issue Number	2
Volume Number	20
Language	English
Publisher	Informa UK Limited
Publisher Date	2010-03-19
Access Restriction	Open
Subject Keyword	Statistics and Probability Gibbs Sampling Markov Random Field Molecular Modules
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Pharmacology Pharmacology (medical)

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