Unsupervised empirical Bayesian multiple testing with external covariates

Egil Ferkingstad*, Arnoldo Frigessi, Håvard Rue, Gudmar Thorleifsson, Augustine Kong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)714-735
Number of pages22
JournalAnnals of Applied Statistics
Volume2
Issue number2
DOIs
StatePublished - Jun 2008
Externally publishedYes

Keywords

  • Bioinformatics
  • Data integration
  • Empirical Bayes
  • False discovery rates
  • Multiple hypothesis testing

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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