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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

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Volume 3, Issue 1


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A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

Markus Ruschhaupt / Wolfgang Huber / Annemarie Poustka / Ulrich Mansmann
Published Online: 2004-12-19 | DOI: https://doi.org/10.2202/1544-6115.1078

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

This article offers supplementary material which is provided at the end of the article.

Keywords: compendium; machine learning; classification; microarray; cancer

About the article

Published Online: 2004-12-19

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 3, Issue 1, Pages 1–24, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1078.

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