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Bioinformatics Advance Access published online on April 7, 2005
Bioinformatics, 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@oupjournals.org
Received November 2, 2004
Revised April 1, 2005
Accepted April 2, 2005

Article

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

Lodewyk F. A. Wessels 1*, Marcel J. T. Reinders 2, Augustinus A. M. Hart 3, Cor J. Veenman 2, Hongyue Dai 4, Yudong D. He 4, and Laura J. van't Veer 5

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

* To whom correspondence should be addressed.
Lodewyk F. A. Wessels, E-mail: l.f.a.wessels{at}ewi.tudelft.nl


  Abstract

Motivation: Microarray gene expression data is 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 degree 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.


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