Permutation Methods for Filtering Genes on Microarray Repeated Measurement Data

Repeated measurement design has lots of advantages on the investigation of underlying genetic pathway. Recently decade, microarray technology also has great aid of improvements in biology relative fields. Because the cost of microarray is still high, most of microarray experiments with repeated measurement design are only several biology replicates. Many repeated measurement analysis tools are based on asymptotic theory, the small samples performance of these methods are often unsuitable to microarray repeated measurement data including the popular generalized estimating equations (GEE) method for analysis of correlated data. We suggest by using GEE combining with permutation methods to solve the problem. The simulation results show that model-based variance estimator with univariate permutation GEE to analyze repeated measurement microarray data performs well on the controlling of nominal type I error with maintaining relative high power. If the sample sizes are extremely small, e.g., less than 5, we propose to use model-based variance estimator with multivariate permutation methods to control the number of false positive with maintaining relative high detective ability.