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CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

M Slawski1, M Daumer1 and A-L Boulesteix12*

Author Affiliations

1 Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr. 1, D-81677 Munich, Germany

2 Department of Statistics, University of Munich, Ludwigstr. 33, D-80539 Munich, Germany

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BMC Bioinformatics 2008, 9:439  doi:10.1186/1471-2105-9-439

Published: 16 October 2008



For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called "p n" setting where the number of predictors p by far exceeds the number of observations n, hence the term "ill-posed-problem". Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers.


In this article, we introduce a new Bioconductor package called CMA (standing for "Classification for MicroArrays") for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches.


CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at webcite.