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.

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 presents 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.