title: Fewer permutations, more accurate P-values.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The Matlab code can be obtained from the corresponding author on request.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org
authors.forename: Theo A
authors.initials: TA
authors.lastname: Knijnenburg
authors.forename: Lodewyk F A
authors.initials: LF
authors.lastname: Wessels
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D015233:Models, Statistical