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German Conference on Pattern Recognition

GCPR 2014: Pattern Recognition pp 81–91Cite as

Information Bottleneck for Pathway-Centric Gene Expression Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

Abstract

While DNA microarrays enable us to conveniently measure expression profiles in the scope of thousands of genes, the subsequent association studies typically suffer from a tremendous imbalance between number of variables (genes) and observations (subjects). Even more so, each gene is heavily perturbed by noise which prevents any meaningful analysis on the single-gene level [6]. Hence, the focus shifted to pathways as groups of functionally related genes [4], in the hope that aggregation potentiates the underlying signal. Technically, this leads to a problem of feature extraction which was previously tackled by principal component analysis [5]. We reformulate the task using an extension of the Meta-Gaussian Information Bottleneck method as a means to compress a gene set while preserving information about a relevance variable. This opens up new possibilities, enabling us to make use of clinical side information in order to uncover hidden characteristics in the data.

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Notes

  1. 1.

    \((A, \varSigma _\xi )\) and \((A^*, I_p)\) are equivalent under \(\mathcal {L}\) with a linear transformation between \(A\) and \(A^*\).

  2. 2.

    http://www.genome.jp/kegg/

  3. 3.

    http://www.broadinstitute.org/gsea/msigdb/

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland

    David Adametz, Mélanie Rey & Volker Roth

Authors
  1. David Adametz
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  2. Mélanie Rey
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  3. Volker Roth
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Corresponding author

Correspondence to David Adametz .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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© 2014 Springer International Publishing Switzerland

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Adametz, D., Rey, M., Roth, V. (2014). Information Bottleneck for Pathway-Centric Gene Expression Analysis. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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