Bayesian Fourier clustering of gene expression data. Issue 8 (14th September 2017)

• Record Type: Journal Article
• Authors: Kim, Jaehee; Kyung, Minjung
• Is Part Of: Communications in statistics. Volume 46:Issue 8(2017)
• Page Start: 6475
• Page End: 6494
• Publication Date: 2017-09-14
• Subjects: Dirichlet process -- Fourier coefficient -- Model-based cluster -- Normal mixture -- Yeast cell cycle; Primary 62H30 -- Secondary 62G08; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1206929
• Languages: English
• ISSNs: 0361-0918

Abstract

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.