Bayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data
Article first published online: 16 JUN 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 142–150, March 2011
How to Cite
Yu, F., Chen, M.-H., Kuo, L., Huang, P. and Yang, W. (2011), Bayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data. Biometrics, 67: 142–150. doi: 10.1111/j.1541-0420.2010.01447.x
- Issue published online: 14 MAR 2011
- Article first published online: 16 JUN 2010
- Received May 2009. Revised March 2010. Accepted March 2010.
- Dirichlet distribution;
- Gene expression;
- Mixture distributions;
- Multinomial distribution;
- Shrinkage estimators
Summary Expressed sequence tag (EST) sequencing is a one-pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method.