Nonparametric modeling approach for discovering differentially expressed genes in replicated microarray experiments

Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels of thousands of genes simultaneously. In microarray data analysis, the comparison of gene-expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large data sets. In particular, Dudoit et al. (2002) developed methods using t-statistics with p-values calculated through permutations, and with the Westfall and Young step-down approach to correct for multiple testing. Thomas et al. (2001) developed a regression modelling approach.Following the idea of Efron et al. (2000) and Tusher et al. (2001), Pan (2002) proposed mixture modelling approach that relaxes many strong assumptions on the null distributions of the test statistics. In this paper, we replace the based Normal mixture density estimators proposed by Pan et al. (2002), with less restrictive nonparametric ones.