full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based...

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Bibliographic Details
Published in:Genetics and molecular research 2008-01, Vol.7 (3), p.883-897
Main Authors: Lauretto, M.S, Pereira, C.A.B, Stern, J.M
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian theory
Cluster Analysis
Computer Simulation
gene clutering
Gene Expression Profiling - methods
Gene Expression Profiling - statistics & numerical data
gene expression regulation
Models, Genetic
multigene family
multivariate analysis
principal component analysis
Saccharomyces cerevisiae
Sequence Analysis, DNA
statistical models
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Summary:Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.
ISSN:1676-5680
1676-5680
DOI:10.4238/vol7-3X-Meeting06