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cancerclass: An R Package for Development and Validation of Diagnostic Tests from High-Dimensional Molecular Data

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  • Jan, Budczies
  • Kosztyla, Daniel
  • von Törne, Christian
  • Stenzinger, Albrecht
  • Darb-Esfahani, Silvia
  • Dietel, Manfred
  • Denkert, Carsten

Abstract

Progress in molecular high-throughput techniques has led to the opportunity of a comprehensive monitoring of biomolecules in medical samples. In the era of personalized medicine, these data form the basis for the development of diagnostic, prognostic and predictive tests for cancer. Because of the high number of features that are measured simultaneously in a relatively low number of samples, supervised learning approaches are sensitive to overfitting and performance overestimation. Bioinformatic methods were developed to cope with these problems including control of accuracy and precision. However, there is demand for easy-to-use software that integrates methods for classifier construction, performance assessment and development of diagnostic tests. To contribute to filling of this gap, we developed a comprehensive R package for the development and validation of diagnostic tests from high-dimensional molecular data. An important focus of the package is a careful validation of the classification results. To this end, we implemented an extended version of the multiple random validation protocol, a validation method that was introduced before. The package includes methods for continuous prediction scores. This is important in a clinical setting, because scores can be converted to probabilities and help to distinguish between clear-cut and borderline classification results. The functionality of the package is illustrated by the analysis of two cancer microarray data sets.

Suggested Citation

  • Jan, Budczies & Kosztyla, Daniel & von Törne, Christian & Stenzinger, Albrecht & Darb-Esfahani, Silvia & Dietel, Manfred & Denkert, Carsten, 2014. "cancerclass: An R Package for Development and Validation of Diagnostic Tests from High-Dimensional Molecular Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i01).
  • Handle: RePEc:jss:jstsof:v:059:i01
    DOI: http://hdl.handle.net/10.18637/jss.v059.i01
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    1. Laura J. van 't Veer & Hongyue Dai & Marc J. van de Vijver & Yudong D. He & Augustinus A. M. Hart & Mao Mao & Hans L. Peterse & Karin van der Kooy & Matthew J. Marton & Anke T. Witteveen & George J. S, 2002. "Gene expression profiling predicts clinical outcome of breast cancer," Nature, Nature, vol. 415(6871), pages 530-536, January.
    2. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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