%0 Journal Article
%T Improved Statistical Testing of Two-class Microarrays with a Robust Statistical Approach
%A Hee-Seok Oh
%A Dongik Jang
%A Seungyoon Oh
%A heebal Kim
%J Interdisciplinary Bio Central
%D 2010
%I IBC
%X The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student¡¯s t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups . The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.
%K Microarray
%K t-test
%K empirical Bayes
%K Pseudo data
%U http://www.ibc7.org/article/journal_v.php?sid=197