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Permutation Filtering: A Novel Concept for Significance Analysis of Large-Scale Genomic Data

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Research in Computational Molecular Biology (RECOMB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3909))

Abstract

Permutation of class labels is a common approach to build null distributions for significance analyis of microarray data. It is assumed to produce random score distributions, which are not affected by biological differences between samples. We argue that this assumption is questionable and show that basic requirements for null distributions are not met.

We propose a novel approach to the significance analysis of microarray data, called permutation filtering. We show that it leads to a more accurate screening, and to more precise estimates of false discovery rates. The method is implemented in the Bioconductor package twilight available on http://www.bioconductor.org.

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Author information

Authors and Affiliations

  1. Max Planck Institute for Molecular Genetics, Computational Diagnostics Group, Ihnestrasse 63-73, D-14195, Berlin, Germany

    Stefanie Scheid & Rainer Spang

Authors
  1. Stefanie Scheid
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  2. Rainer Spang
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

  3. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

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© 2006 Springer-Verlag Berlin Heidelberg

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Scheid, S., Spang, R. (2006). Permutation Filtering: A Novel Concept for Significance Analysis of Large-Scale Genomic Data. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_29

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  • DOI: https://doi.org/10.1007/11732990_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

  • Online ISBN: 978-3-540-33296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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