Unsupervised empirical Bayesian multiple testing with external covariates
Egil Ferkingstad, Arnoldo Frigessi, Håvard Rue, Gudmar Thorleifsson, and Augustine Kong
Source: Ann. Appl. Stat. Volume 2, Number 2 (2008), 714-735.
Abstract
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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Keywords: Bioinformatics; multiple hypothesis testing; false discovery rates; data integration; empirical Bayes
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.aoas/1215118535
Digital Object Identifier: doi:10.1214/08-AOAS158
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