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Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions

Among the first microarray experiments were those measuring expression over time, and time course experiments remain common. Most methods to analyze time course data attempt to group genes sharing similar temporal profiles within a single biological condition. However, with time course data in multiple conditions, a main goal is to identify differential expression patterns over time. An intuitive approach to this problem would be to apply at each time point any of the many methods for identifying differentially expressed genes across biological conditions and then somehow combine the results of the repeated marginal analyses. But considering each time point in isolation is inefficient, because it does not use the information contained in the dependence structure of the time course data. This problem is exacerbated in microarray studies, where low sensitivity is a problematic feature of many methods. Furthermore, a gene's expression pattern over time might not be identified by simply combining results from repeated marginal analyses. We propose a hidden Markov modeling approach developed to efficiently identify differentially expressed genes in time course microarray experiments 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, compared with a marginal analysis at each time point. This increase is also 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.