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

• Published in: Pattern recognition letters Vol. 29; no. 6; pp. 768 - 772
• Main Author: Nilsson, Björn
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.04.2008; Elsevier
• Subjects: Applied sciences; Basic Medicine; Bioinformatics; Biomedical pattern recognition; Coding, codes; Data compression; Exact sciences and technology; Information, signal and communications theory; Medical and Health Sciences; Medical Genetics and Genomics (including Gene Therapy); Medicin och hälsovetenskap; Medicinsk genetik och genomik (Här ingår: Genterapi); Medicinska och farmaceutiska grundvetenskaper; Microarrays; Ontological analysis; Pattern recognition; Signal and communications theory; Signal processing; Telecommunications and information theory; Biomedical pattern recognition; Microarrays; Bioinformatics; Data compression; Ontological analysis; Monte Carlo method; Test statistic; Pattern recognition; Algorithm; Implementation; Approximation error; Numerical simulation; Miniaturization; Biomedical engineering
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2007.12.007

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.