MicroMultitest: Ranking Differentially-Expressed Genes in Microarray Data

The important purpose of the microarray gene expression data analysis is to identify significantly differentially-expressed genes between two groups of samples which are in two different experimental states. In this work, we propose to use several statistical test methods for a given microarray data set and cross-refer the results of different statistical test methods. The accuracy of different statistical methods is estimated by Receiver Operation Characteristic (ROC) technique. A new software tool, MicroMultitest, was developed. A number of statistical testing methods (such as t-test, adapted SAM method, p-value adjustments), as well as the ROC analysis technique were implemented in this software. Using the MicroMultitest one has the ability to evaluate the performance of different statistical testing methods by applying each to the same given microarray data set, optimize the cutoff values and permutation times for these statistical testing methods, and select relative reliable differentially-expressed gene set.