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Compression algorithm for pre-simulated Monte Carlo p-value functions: Application to the ontological analysis of microarray studies

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Author

Summary, in English

Monte Carlo simulation is frequently employed to compute p-values for test statistics with unknown null distributions. However, the computations can be exceedingly time-consuming, and, in such cases, the use of pre-computed simulations can be considered to increase speed. This approach is attractive in principle, but complicated in practice because the size of the pre-computed data can be prohibitively large. We developed an algorithm for computing size-reduced representations of Monte Carlo p-value functions. We show that, in typical settings, this algorithm reduces the size of the pre-computed data by several orders of magnitude, while bounding provably the approximation error at an explicitly controllable level. The algorithm is data-independent, fully non-parametric, and easy to implement. We exemplify its practical utility by applying it to the threshold-free ontological analysis of microarray data. The presented algorithm simplifies the use of pre-computed Monte Carlo p-value functions in software, including specialized bioinformatics applications.

Publishing year

2008

Language

English

Pages

768-772

Publication/Series

Pattern Recognition Letters

Volume

29

Issue

6

Document type

Journal article

Publisher

Elsevier

Topic

  • Medical Genetics

Keywords

  • ontological analysis
  • microarrays
  • biomedical pattern recognition
  • bioinformatics
  • data compression

Status

Published

ISBN/ISSN/Other

  • ISSN: 0167-8655