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The effort to identify genes with periodic expression during the cell cyclefrom genome-wide microarray time series data has been ongoing for a decade.However, the lack of rigorous modeling of periodic expression as well as thelack of a comprehensive model for integrating information across genes andexperiments has impaired the effort for the accurate identification ofperiodically expressed genes. To address the problem, we introduce a Bayesianmodel to integrate multiple independent microarray data sets from three recentgenome-wide cell cycle studies on fission yeast. A hierarchical model was usedfor data integration. In order to facilitate an efficient Monte Carlo samplingfrom the joint posterior distribution, we develop a novel Metropolis--Hastingsgroup move. A surprising finding from our integrated analysis is that more than40% 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 detectionproblem.

Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle