ROCS: Receiver Operating Characteristic Surface for Class-Skewed High-Throughput Data

• Type of Material: Article
• Authors: Tianwei Yu, Emory University
• Language: English
• Date: 2012-07-06
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2012 Tianwei Yu
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0040598
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 7
• Issue: 7
• Start Page: e40598
• End Page: e40598
• Grant/Funding Information: This research was partially supported by National Institutes of Health grants 5P01ES016731, 5U19AI057266 and 1U19AI090023.
• Research Categories: Health Sciences, Public Health

Abstract

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the curve (AUC) insufficient for the purpose of judging classifier performance. Here we define an ROC surface (ROCS) using true positive rate (TPR), false positive rate (FPR), and true discovery rate (TDR). The ROC surface, together with the associated quantities, volume under the surface (VUS) and FDR-controlled area under the ROC curve (FCAUC), provide a useful approach for gauging classifier performance on class-skewed high-throughput data. The implementation as an R package is available at http://userwww.service.emory.edu/~tyu8/R​OCS/.