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Bayesian Hierarchical Modeling for Time Course Microarray Experiments
Author(s) -
Chi YuehYun,
Ibrahim Joseph G.,
Bissahoyo Anika,
Threadgill David W.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00689.x
Subject(s) - bayesian probability , microarray analysis techniques , bayesian hierarchical modeling , microarray , expression (computer science) , computational biology , computer science , gene expression , gene expression profiling , selection (genetic algorithm) , identification (biology) , hierarchical database model , bayesian inference , gene , dna microarray , data mining , biology , artificial intelligence , genetics , programming language , botany
Summary Time course microarray experiments designed to characterize the dynamic regulation of gene expression in biological systems are becoming increasingly important. One critical issue that arises when examining time course microarray data is the identification of genes that show different temporal expression patterns among biological conditions. Here we propose a Bayesian hierarchical model to incorporate important experimental factors and to account for correlated gene expression measurements over time and over different genes. A new gene selection algorithm is also presented with the model to simultaneously identify genes that show changes in expression among biological conditions, in response to time and other experimental factors of interest. The algorithm performs well in terms of the false positive and false negative rates in simulation studies. The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane‐induced gene expression profiles with colorectal cancer susceptibility.