CSAX: Characterizing Systematic Anomalies in eXpression Data

Abstract

Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine learning problem. However, due to the dimensionality and variability of expression data, existing methods based on feature space analysis or individual anomalously-expressed genes are insufficient. We present a novel approach, CSAX, that identifies pathways in an individual sample in which the normal expression relationships are disrupted. To evaluate our approach, we have compiled and released a compendium of public microarray data sets, reformulated to create a testbed for anomaly detection. We demonstrate the accuracy of CSAX on the data sets in our compendium, compare it to other leading anomaly-detection methods, and show that CSAX aids both in identifying anomalies and in explaining their underlying biology. We note the potential for the use of such methods in identifying subclasses of disease. We also describe an approach to characterizing the difficulty of specific expression anomaly detection tasks and discuss how one can estimate the feasibility of a specific task. Our approach provides an important step towards identification of individual disease patterns in the era of personalized medicine.