Microarray data analysis using rival penalized EM algorithm in normal mixture models

Microarray technology is a useful tool for monitoring the expressed levels of thousands of genes simultaneously. Recently, mixture modelling has been used to extract information from expressed genes. It utilizes two separate steps to estimate the number of classes and model parameters, respectively, which however may be time-consuming and fall into sub-optimal solutions. In this paper, we therefore apply an one-step approach, namely rival penalized expectation-maximization (RPEM) algorithm, to microarray data analysis. The RPEM algorithm is capable of estimating the parameters of the normal mixture model, meanwhile determining the number of classes automatically. The numerical results have shown the effectiveness of this technique on real microarray data analysis.