Abstract #301043

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JSM 2003 Abstract #301043
Activity Number: 471
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301043
Title: A Mixture Regression Model for Identifying Genes with Different Time-course Expression Profiles
Author(s): Fangxin Hong*+ and Hongzhe Li
Companies: University of California, Davis and University of California, Davis
Address: 3800 Solano Park Circle, Davis, CA, 95616,
Keywords: time-course gene expression ; mixture model ; Monte Carlo EM
Abstract:

Time-course studies are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. Microarray technology allows simultaneous measurements of genome-wide expression levels over time, which make it possible to study temporal difference in gene expression profiles among different groups. Current approaches identify differentially expressed genes at each time point, without incorporating time information or time-varying factor, and provide no measurement on overall statistical significance. Here, we introduce a general mixture regression model approach to model gene expression trajectories over time. With flexible choice of polynomials, spline bases or some other existing curve models, the method is able to model various temporal patterns so that it can address the overall significance on differentially expressed genes between groups. The Monte Carlo EM algorithm is used for parameter estimation. We illustrate the methods by both simulated and real dataset of gene expression time-course expression profiles in rat heart and liver.

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