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A Nonparametric Likelihood Ratio Test to Identify Differentially Expressed Genes from Microarray Data
by
Sankar Bokka
305 Hume Hall, Department of Mathematics, University of Mississippi, MS 38677
Coauthors: Sunil K. Mathur
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. In this paper, we present a nonparametric likelihood ratio (NPLR) test to identify differentially expressed genes using microarray data. The NPLR test is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the NPLR test is more powerful than some of the commonly used methods, such as two-sample t-test and Mann-Whitney U-test. When applied to microarray data, it is found that the NPLR test identifies more differentially expressed genes than its competitors. The asymptotic distribution of the NPLR test statistic, and the p-value function is presented.
Date received: October 11, 2005
Copyright © 2005 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # carm-46.