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General Departmental Seminar Series

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

Ming Yuan, PhD Candidate, Dept. of Statistics, UW-Madison

Friday, March 12, 2004, 12-1 p.m.

132 WARF Building, 610 Walnut St.

ABSTRACT

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. I will present a Hidden Markov modeling approach designed specifically to address this question. Simulation studies show a substantial increase in sensitivity without an increase in the false discovery rate when compared to a marginal analysis at each time point. Results from three case studies will be discussed.

This is joint work with Prof. Christina Kendziorski.

 

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