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Bioinformatics 2009 25(12):i161-i168; doi:10.1093/bioinformatics/btp211
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fewer permutations, more accurate P-values

Theo A. Knijnenburg 1,*, Lodewyk F. A. Wessels 2, Marcel J. T. Reinders 3 and Ilya Shmulevich 1

1Institute for Systems Biology, Seattle, WA, USA, 2Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam and 3Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands

*To whom correspondence should be addressed.


  Abstract

Motivation: Permutation tests have become a standard tool to assess the statistical significance of an event under investigation. The statistical significance, as expressed in a P-value, is calculated as the fraction of permutation values that are at least as extreme as the original statistic, which was derived from non-permuted data. This empirical method directly couples both the minimal obtainable P-value and the resolution of the P-value to the number of permutations. Thereby, it imposes upon itself the need for a very large number of permutations when small P-values are to be accurately estimated. This is computationally expensive and often infeasible.

Results: A method of computing P-values based on tail approximation is presented. The tail of the distribution of permutation values is approximated by a generalized Pareto distribution. A good fit and thus accurate P-value estimates can be obtained with a drastically reduced number of permutations when compared with the standard empirical way of computing P-values.

Availability: The Matlab code can be obtained from the corresponding author on request.

Contact: tknijnenburg{at}systemsbiology.org

Supplementary information:Supplementary data are available at Bioinformatics online.


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