Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle. (English) Zbl 1194.62020

Fan, Xiaodan; Pyne, Saumyadipta; Liu, Jun S.

Ann. Appl. Stat. 4, No. 2, 988-1013 (2010).

Summary: The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expressions as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address this problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis-Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10–15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.

MSC:

62F15	Bayesian inference
62P10	Applications of statistics to biology and medical sciences; meta analysis
92C40	Biochemistry, molecular biology
65C05	Monte Carlo methods
92C37	Cell biology

Keywords:

cell cycle; periodically expressed genes; microarray time series; meta-analysis; fission yeast; Schizosaccharomyces; Markov chain Monte Carlo

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