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

TL;DR

This paper introduces a Bayesian hierarchical model with a novel sampling method to accurately identify periodically expressed genes in fission yeast, revealing a much higher proportion of such genes than previously reported.

Contribution

The paper presents a new Bayesian hierarchical model and a novel Metropolis–Hastings group move for integrating multiple microarray datasets to detect periodic gene expression.

Findings

Over 40% of genes are periodically expressed in fission yeast.

The method significantly improves detection accuracy over previous estimates.

Results suggest a need to reconsider the prevalence of periodic genes in cell cycle studies.

Abstract

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.