Hidden Markov Models for Microarray Time-course Data in Multiple Biological Conditions

• Activity Number: 426
• Type: Invited
• Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
• Sponsor: JASA, Applications and Case Studies
• Author(s): Ming Yuan and Christina Kendziorski*+
• Companies: Georgia Institute of Technology and University of Wisconsin, Madison
• Keywords: Hidden Markov model ; microarray ; gene expression ; time course

Abstract

Most statistical methods to analyze microarray time course data attempt to group genes sharing similar temporal profiles within a single biological condition. With time-course data in multiple conditions, a main goal is to identify differential expression patterns over time. A simple approach would be to consider each time point in isolation and combine results from repeated marginal analyses. However, doing so does not utilize the dependence structure. This can be a serious drawback, particularly for microarray studies where low sensitivity is observed for many methods. We propose a Hidden Markov modeling approach developed to efficiently identify differentially expressed genes and classify genes based on their temporal expression patterns. Simulation studies demonstrate a substantial increase in sensitivity, with little increase in the false discovery rate, when compared to a marginal analysis at each time point. This increase also is observed in data from a case study of the effects of aging on stress response in heart tissue, where a significantly larger number of genes are identified using the proposed approach.