Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values

TLDR: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities.

Abstract: Motivation: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. Results: Am odel is introduced that frequently describes very accurately the distribution of a set of p-values arising from an array analysis. The model is used to obtain an estimated distribution that is easily expressed as a mixture of null and alternative densities. Given a threshold of significance, the estimated distribution is partitioned into regions corresponding to the occurrences of false positives, false negatives, true positives, and true negatives. Availability: An S-plus function library is available from