TRANSCRIPTOME 2002: From Functional Genomics to Systems Biology
March 10-13, 2002
Seattle, Washington, USA


A Regression-based Method to Identify Differentially Expressed Genes in Microarray Time Course Studies and its Application in an Inducible Huntington’s Disease Transgenic Model

Xie L. Xu, James M. Olson, Lue Ping Zhao, Fred Hutchinson Cancer Research Center, Seattle, WA

Time course studies with microarray technologies provide enormous potential for exploring underlying mechanisms of biological phenomena in many areas of biomedical research, but the large amount of gene expression data generated by such studies also present great challenges to data analysis. Here we introduce a regression-based statistical modeling approach that identifies differentially expressed genes in microarray time course studies. To illustrate this method, we applied it to data generated from an inducible Huntington’s disease transgenic model. The regression method accounts for the induction process, incorporates relevant experimental information, and includes parameters that specifically address the research  interest: the temporal differences in gene expression profiles between the mutant and control mice over the time course, in addition to heterogeneities that commonly exist in microarray data. Least squares and estimating equation techniques were used to estimate parameters and variances, and inferences were made based on efficient and robust Z statistics under a set of well-defined assumptions. A permutation test was also used to estimate the number of false positives, providing an alternative measurement of statistical significance useful for investigators to make decisions on follow-up studies.

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