Efficient estimation of the number of false positives in high-throughput screening

Journal article, 2015

This paper develops tail estimation methods to handle false positives in multiple testing problems where testing is done at extreme significance levels and with low degrees of freedom, and where the true null distribution may differ from the theoretical one. We show that the number of false positives, conditional on the total number of positives, has an approximately binomial distribution, and we find estimators of the distribution parameter. We also develop methods for estimation of the true null distribution, as well as techniques to compare it with the theoretical one. Analysis is based on a simple polynomial model for very small p-values. Asymptotics that motivate the model, properties of the estimators, and model-checking tools are provided. The methods are applied to two large genomic studies and an fMRI brain scan experiment.