A compression algorithm for pre-simulated Monte Carlo p-value functions: Application to the ontological analysis of microarray studies
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.
ontological analysis microarrays bioinformatics biomedical pattern recognition data compression