Time-course experiments are commonly conducted to capture temporal changes. It is generally of interest to detect if any changes happen over time, which we define as a detection problem. If there is a change, it is informative to know when the change is, which we define as an identification problem. It is often desired to control Type I error rate at a nominal level while applying a testing procedure to detect or identify these changes. Quite a few analytic methods have been proposed. Most existing methods aim to solve either the detection problem or, more recently, the identification problem. Here, we propose to solve these two problems using a unified multiple-testing framework built upon an empirical Bayes change-point model. Our model provides a flexible framework that can account for sophisticated temporal gene expression patterns. We show that our testing procedure is valid and asymptotically optimal in the sense of rejecting the maximum number of null hypotheses, while the Bayesian false discovery rate (FDR) can be controlled at a prede-fined nominal level. Simulation studies and application to real transcriptome time-course data illustrate that our proposed model is a flexible and powerful method to capture various temporal patterns in analysis of time-course data.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
- Change-point model
- Empirical Bayes
- Time series