The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 4, Number 2 (2010), 988-1013.
Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle
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.
Ann. Appl. Stat., Volume 4, Number 2 (2010), 988-1013.
First available in Project Euclid: 3 August 2010
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Fan, Xiaodan; Pyne, Saumyadipta; Liu, Jun S. Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle. Ann. Appl. Stat. 4 (2010), no. 2, 988--1013. doi:10.1214/09-AOAS300. https://projecteuclid.org/euclid.aoas/1280842149
- Supplementary material: Various supporting materials. In this supplement we provide model fitting diagnoses, hierarchical clustering results, the effect of data size on the statistical power, supporting evidences for newly found genes, and figures referred to in this paper.