Detection of Outliers in Gene Expression Data Using Expressed Robust-t Test

Detection of outliers in gene expression data has drawn a great deal of attention in recent years. Although a variety of outlier detection methods is available in the literature Tomlins et al. (2005) argued that they are not readily applicable to gene expression data. They developed the "cancer outlier profile analysis (COPA)" method to detect cancer genes and outliers. Following their way several methods are proposed in the literature for detecting outliers. Most of these methods are based on t -type tests which are basically nonrobust and hence fail to identify multiple outliers. In this paper we propose a robust version of the t -test that we call expressed robust t (ERT) test. The usefulness of the proposed methods is then investigated by Monte Carlo simulation and real cancer data.