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Bioinformatics Advance Access published online on October 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti724
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 4, 2005
Revised September 19, 2005
Accepted October 16, 2005

Article

Regularized ROC method for disease classification and biomarker selection with microarray data

Shuangge Ma 1 and Jian Huang 2*

1 Department of Biostatistics, University of Washington
2 Department of Statistics and Actuarial Science, and Program in Public Health Genetics, University of Iowa

* To whom correspondence should be addressed.
Jian Huang, E-mail: jian{at}stat.uiowa.edu


  Abstract

Motivation: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease classification. Thus there is a need for developing statistical methods that can efficiently use such high-throughput genomic data, select biomarkers with discriminant power and construct classification rules. The ROC (receiving operator characteristic) technique has been widely used in disease classification with low dimensional biomarkers because (1) it does not assume a parametric form of the class probability as required for example in the logistic regression method; (2) it accommodates case-control designs; and (3) it allows treating false positives and false negatives differently. However, due to computational difficulties, the ROC based classification has not been used with microarray data. Moreover, the standard ROC technique does not incorporate built-in biomarker selection.

Results: We propose a novel method for biomarker selection and classification using the ROC technique for microarray data. The proposed method uses a sigmoid approximation to the area under the ROC curve as the objective function for classification and the threshold gradient descent regularization method for estimation and biomarker selection. Tuning parameter selection based on the V-fold cross validation and predictive performance evaluation are also investigated. The proposed approach is demonstrated with a simulation study, the Colon data and the Estrogen data. The proposed approach yields parsimonious models with excellent classification performance.

Availability: R code is available upon request.


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