We explore the performance of the outlier-sum statistic (Tibshirani and Hastie, Biostatistics 2007 8:2--8), a proposed method for identifying genes for which only a subset of a group of samples or patients exhibits differential expression levels. Our discussion focuses on this method as an example of how inattention to standard statistical theory can lead to approaches that exhibit some serious drawbacks. In contrast to the results presented by those authors, when comparing this method to several variations of the $t$-test, we find that the proposed method offers little benefit even in the most idealized scenarios, and suffers from a number of limitations including difficulty of calibration, high false positive rates owing to its asymmetric treatment of groups, poor power or discriminatory ability under many alternatives, and poorly defined application to one-sample settings. Further issues in the Tibshirani and Hastie paper concern the presentation and accuracy of their simulation results; we were unable to reproduce their findings, and we discuss several undesirable and implausible aspects of their results.
Emerson, Sarah C. and Emerson, Scott S., "The Importance of Statistical Theory in Outlier Detection" (August 2011). UW Biostatistics Working Paper Series. Working Paper 381.