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Bioinformatics Advance Access originally published online on April 7, 2005
Bioinformatics 2005 21(19):3755-3762; doi:10.1093/bioinformatics/bti429
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

A protocol for building and evaluating predictors of disease state based on microarray data

Lodewyk F. A. Wessels 1,2,*, Marcel J. T. Reinders 1, Augustinus A. M. Hart 3, Cor J. Veenman 1, Hongyue Dai 4, Yudong D. He 4 and Laura J. van't Veer 2

1Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands
2Department of Pathology, The Netherlands Cancer Institute Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
3Department of Radiotherapy, The Netherlands Cancer Institute Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
4Rosetta Inpharmatics LLC (a wholly owned subsidiary of Merck & Co., Inc.) 401 Terry Avenue N. Seattle, Washington 98109, USA

*To whom correspondence should be addressed at Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O.Box 5031, 2600 GA Delft, The Netherlands. Tel.: +31 15 2785114; fax: +31 15 2781843.

Motivation: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no ‘standard’ computational approach has emerged which enables robust outcome prediction.

Results: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Contact: l.f.a.wessels{at}ewi.tudelft.nl

Supplementary information: http://ict.ewi.tudelft.nl/index.php?option=com_pub&task=view&id=1983

Received on November 2, 2004; revised on April 2, 2005; accepted on April 2, 2005

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