A New Statistical Test to Identify
Differentially Expressed Genes from Microarray Data

Abstract:  Microarray experiments contribute significantly to the progress in disease treatment by enabling a precise and early diagnosis. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The statistical methods, currently available in literature to analyze microarray data are not up to the mark, mainly due to the lack of understanding of the distribution of microarray data. We present a new test to identify differentially expressed genes using microarray data. The proposed test is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods. When applied to microarray data, it is found that the proposed test identifies more differentially expressed genes than its competitors. The asymptotic distribution of the proposed test statistic and the p-value function is presented.