On Gene Ranking Using Replicated Microarray Time Course Data
Article first published online: 5 JUN 2008
© 2008, The International Biometric Society
Volume 65, Issue 1, pages 40–51, March 2009
How to Cite
Chuan Tai, Y. and Speed, T. P. (2009), On Gene Ranking Using Replicated Microarray Time Course Data. Biometrics, 65: 40–51. doi: 10.1111/j.1541-0420.2008.01057.x
- Issue published online: 17 MAR 2009
- Article first published online: 5 JUN 2008
- Received August 2007. Revised February 2008. Accepted March 2008.
- Empirical Bayes;
- Gene ranking;
- Microarray time course
Summary Consider the ranking of genes using data from replicated microarray time course experiments, where there are multiple biological conditions, and the genes of interest are those whose temporal profiles differ across conditions. We derive a multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multisample model assumes that time course replicates are independent and identically distributed multivariate normal vectors. On the other hand, we construct a cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes' setting which guarantee closed form solutions for the posterior odds. The simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches perform satisfactorily.