Bayesian Fourier clustering of gene expression data |
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Authors: | Jaehee Kim |
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Institution: | Department of Statistics, Duksung Women's University, Seoul, South Korea |
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Abstract: | 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. |
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Keywords: | Dirichlet process Fourier coefficient Model-based cluster Normal mixture Yeast cell cycle |
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