Bayesian Fourier clustering of gene expression data

Clustering gene expression data are an important step in providing information to biologists. A Bayesian clustering procedure using Fourier series with a Dirichlet process prior for clusters was developed. As an optimal computational tool for this Bayesian approach, Gibbs sampling of a normal mixtur...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2017-09, Vol.46 (8), p.6475-6494
Main Authors: Kim, Jaehee, Kyung, Minjung
Format: Article
Language:English
Subjects:
Bayesian analysis
Biological effects
Clustering
Clusters
Computer simulation
Dirichlet problem
Dirichlet process
Fourier coefficient
Fourier series
Gene expression
Model-based cluster
Monte Carlo simulation
Normal mixture
Primary 62H30
Secondary 62G08
Software
Yeast
Yeast cell cycle
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Summary:Clustering gene expression data are an important step in providing information to biologists. A Bayesian clustering procedure using Fourier series with a Dirichlet process prior for clusters was developed. As an optimal computational tool for this Bayesian approach, Gibbs sampling of a normal mixture with a Dirichlet process was implemented to calculate the posterior probabilities when the number of clusters was unknown. Monte Carlo study results showed that the model was useful for suitable clustering. The proposed method was applied to the budding yeast Saccaromyces cerevisiae and provided biologically interpretable results.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2016.1206929