Applications of Bayesian statistical methods in microarray data analysis.
Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.
Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Dongyan
D
Yang
Stanislav O
SO
Zakharkin
Grier P
GP
Page
Jacob P L
JP
Brand
Jode W
JW
Edwards
Alfred A
AA
Bartolucci
David B
DB
Allison
D001499:Bayes Theorem; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis