An Empirical Bayes' Approach to Joint Analysis of Multiple Microarray Gene Expression Studies
Article first published online: 22 APR 2011
© 2011, The International Biometric Society
Volume 67, Issue 4, pages 1617–1626, December 2011
How to Cite
Ruan, L. and Yuan, M. (2011), An Empirical Bayes' Approach to Joint Analysis of Multiple Microarray Gene Expression Studies. Biometrics, 67: 1617–1626. doi: 10.1111/j.1541-0420.2011.01602.x
- Issue published online: 14 DEC 2011
- Article first published online: 22 APR 2011
- Received June 2010. Revised January 2011. Accepted February 2011.
- Empirical Bayes';
- Gene expression;
- Joint analysis;
- Mixture model
Summary With the prevalence of gene expression studies and the relatively low reproducibility caused by insufficient sample sizes, it is natural to consider joint analysis that could combine data from different experiments effectively to achieve improved accuracy. We present in this article a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms by fitting each data with different set of parameters, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes' fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.