  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 expression 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 the 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.
