Unsupervised empirical Bayesian multiple testing with external covariates

Details Unsupervised empirical Bayesian multiple testing with external covariates Unsupervised empirical Bayesian multiple testing with external covariates

2008-07-29

arxiv.org/abs/0807.4658v1

Annals of Applied Statistics 2008, Vol. 2, No. 2, 714-735

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Published in at http://dx.doi.org/10.1214/08-AOAS158 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/08-AOAS158

In an empirical Bayesian setting, we provide a new multiple testing method, useful when an additional covariate is available, that influences the probability of each null hypothesis being true. We measure the posterior significance of each test conditionally on the covariate and the data, leading to greater power. Using covariate-based prior information in an unsupervised fashion, we produce a list of significant hypotheses which differs in length and order from the list obtained by methods not taking covariate-information into account. Covariate-modulated posterior probabilities of each null hypothesis are estimated using a fast approximate algorithm. The new method is applied to expression quantitative trait loci (eQTL) data.

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