Detecting Differentially-expressed Time Course Gene Expression Profiles

• Activity Number: 354
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Xueli Liu*+ and Rongling Wu and George Casella
• Companies: University of Florida and University of Florida and University of Florida
• Keywords: Non-parametric Bootstrap ; Principal Component Analysis ; Conditional Expectation ; Time Course Gene Expression ; p-value ; Flase Discovery Rate

Abstract

Among the large amounts of high-throughput biological data, time course gene expression profiles can reveal important dynamic features of cell activities. Yet, not so much effort has been contributed to address the key question of detecting differentially-expressed time course gene expression profiles. One reason may be that the experimental designs for the time course gene expression data are not consistent across subjects (e.g., varying sampling rates and the total number of time points sampled for each subject are often small). We present a statistical method for detecting significance of differential time course gene expression data, which can be applied when there are not so many time points for each subject or when the time grid is not regular. The idea of our method is to integrate a newly-developed principal analysis through conditional expectation method and a nonparametric bootstrap into a hypothesis test framework. In doing so, each gene will be assigned a p-value pertaining to whether the gene is differentially expressed. We applied the method to a rat study with the aim to identify those genes that are significantly differentially expressed with respect to time.