Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions

Abstract

We develop an approach to analyze time-course microarray data which are obtained from a single sample at multiple time points and to identify which genes are cell-cycle regulated. Since some genes have similar gene expression patterns, to reduce the amount of hypothesis testing, we first perform a clustering analysis to group genes into classes with similar cell-cycle patterns, including a class with no cell-cycle phenomena at all. Then we build a statistical model and an inference function assuming that genes within a cluster share the same mean model. A varying coefficient nonparametric approach is employed to be more flexible to fit the time-course data. In order to incorporate the correlation of longitudinal measurements, the quadratic inference function method is applied to obtain more efficient estimators and more powerful tests. Furthermore, this method allows us to perform chi-squared tests to determine whether certain genes are cell-cycle regulated. A data example on cell-cycle microarray data as well as simulations are illustrated.