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Multidimensional scaling with discrimination coefficients for supervised visualization of high-dimensional data

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Abstract

Visualization techniques for high-dimensional data sets play a pivotal role in exploratory analysis in a wide range of disciplines. A particularly challenging problem represents gene expression data based on microarray technology where the number of features (genes) typically exceeds 20,000, whereas the number of samples is frequently below 200. We investigated class-specific discrimination coefficients for each feature and each pair of classes for an effective nonlinear mapping to lower-dimensional space. We applied the technique to three microarray data sets and compared the projections to two-dimensional space with the results from a conventional multidimensional scaling method, a score plot resulting from principal component analysis, and projections from linear discriminant analysis. In the experiments, we observed that the discrimination coefficients allowed for an improved visualization of high-dimensional genomic data.

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Acknowledgments

This work was supported in part by the Japan Society for the Promotion of Science. We thank H. Kitano for his support and the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Systems Biology Research Group, School of Biomedical Sciences, University of Ulster, Coleraine, UK

    Daniel Berrar

  2. Department of Cancer Systems Biology, Cancer Institute, Japan Foundation for Cancer Research, Tokyo, Japan

    Daniel Berrar

  3. University of Applied Sciences, Weihenstephan, Germany

    Georg Ohmayer

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  1. Daniel Berrar
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  2. Georg Ohmayer
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Correspondence to Daniel Berrar.

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Berrar, D., Ohmayer, G. Multidimensional scaling with discrimination coefficients for supervised visualization of high-dimensional data. Neural Comput & Applic 20, 1211–1218 (2011). https://doi.org/10.1007/s00521-010-0478-1

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  • DOI: https://doi.org/10.1007/s00521-010-0478-1

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