Identification of Outliers in Gene Expression Data

Identification of outliers is a big challenge in big data although it has drawn a great deal of attention in recent years. Among all big data problems, the detection of outliers in gene expression data warrants extra attention because of its inherent complexity. Although a variety of outlier detection methods are available in the literature, Tomlins et al. (Tomlins et al. Science 310:644–648, 2005) argued that traditional analytical methods, for example, a two-sample t-statistic, which search for common activation of genes across a class of cancer samples, will fail to detect cancer genes, which show differential expression in a subset of cancer samples or cancer outliers. They developed the cancer outlier profile analysis (COPA) method to detect cancer genes and outliers. Inspired by the COPA statistic, some authors have proposed other methods for detecting cancer-related genes with cancer outlier profiles in the framework of multiple testing (Tibshirani and Hastie Tibshirani and Hastie Biostatistics 8:2–8, 2007; Wu Wu Biostatistics 8:566–575, 2007; Lian Lian Biostatistics 9:411–418, 2008; Wang and Rekaya Wang and Rekaya Biomarker Insights 5:69–78, 2010). Such cancer outlier analyses are affected by many problems especially if there is an outlier in the dataset then classical measures of location and scale are seriously affected. So the test statistic using these parameters might not be appropriate to detect outliers. In this study, we try to robustify one existing method. We propose a new technique called expressed robust t-statistic (ERT) for the identification of outliers. The usefulness of the proposed methods is then investigated through a Monte Carlo simulation.