Compression algorithm for pre-simulated Monte Carlo p-value functions: Application to the ontological analysis of microarray studies
(2008) In Pattern Recognition Letters 29(6). p.768-772- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1206210
- author
- Nilsson, Björn LU
- organization
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ontological analysis, microarrays, biomedical pattern recognition, bioinformatics, data compression
- in
- Pattern Recognition Letters
- volume
- 29
- issue
- 6
- pages
- 768 - 772
- publisher
- Elsevier
- external identifiers
-
- wos:000255129600007
- scopus:39949083941
- ISSN
- 0167-8655
- DOI
- 10.1016/j.patrec.2007.12.007
- language
- English
- LU publication?
- yes
- id
- dcfabf40-1eac-461e-91c9-a4d138d8a459 (old id 1206210)
- date added to LUP
- 2016-04-01 14:22:12
- date last changed
- 2022-01-28 00:18:36
@article{dcfabf40-1eac-461e-91c9-a4d138d8a459,
abstract = {{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.}},
author = {{Nilsson, Björn}},
issn = {{0167-8655}},
keywords = {{ontological analysis; microarrays; biomedical pattern recognition; bioinformatics; data compression}},
language = {{eng}},
number = {{6}},
pages = {{768--772}},
publisher = {{Elsevier}},
series = {{Pattern Recognition Letters}},
title = {{Compression algorithm for pre-simulated Monte Carlo p-value functions: Application to the ontological analysis of microarray studies}},
url = {{http://dx.doi.org/10.1016/j.patrec.2007.12.007}},
doi = {{10.1016/j.patrec.2007.12.007}},
volume = {{29}},
year = {{2008}},
}