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Originally published as Genetics Published Articles Ahead of Print on September 9, 2008.
Genetics, Vol. 180, 821-834, October 2008, Copyright © 2008
doi:10.1534/genetics.108.093690
A Computational Approach to the Functional Clustering of Periodic Gene-Expression Profiles
Bong-Rae Kim*,1,
Li Zhang
,1,
Arthur Berg*,
Jianqing Fan
and
Rongling Wu*,2
* Department of Statistics, University of Florida, Gainesville, Florida 32611, Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio 44195 and Department of Operation Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544
2 Corresponding author: Department of Statistics, University of Florida, Gainesville, FL 32611.
E-mail: rwu{at}stat.ufl.edu
DNA microarray analysis has emerged as a leading technology to enhance our understanding of gene regulation and function in cellular mechanism controls on a genomic scale. This technology has advanced to unravel the genetic machinery of biological rhythms by collecting massive gene-expression data in a time course. Here, we present a statistical model for clustering periodic patterns of gene expression in terms of different transcriptional profiles. The model incorporates biologically meaningful Fourier series approximations of gene periodic expression into a mixture-model-based likelihood function, thus producing results that are likely to be closer to biological relevance, as compared to those from existing models. Also because the structures of the time-dependent means and covariance matrix are modeled, the new approach displays increased statistical power and precision of parameter estimation. The approach was used to reanalyze a real example with 800 periodically expressed transcriptional genes in yeast, leading to the identification of 13 distinct patterns of gene-expression cycles. The model proposed can be useful for characterizing the complex biological effects of gene expression and generate testable hypotheses about the workings of developmental systems in a more precise quantitative way.