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Author (up) Egil Ferkingstad, Arnoldo Frigessi, Håvard Rue, Gudmar Thorleifsson, and Augustine Kong doi  openurl
  Title Unsupervised empirical Bayesian multiple testing with external covariates Type Journal Article
  Year 2008 Publication The Annals of Applied Statistics Abbrev Journal Ann. Appl. Stat.  
  Volume 2 Issue 2 Pages 714-735  
  Corporate Author University of Oslo and Centre for Integrative Genetics Thesis  
  Address  
  Keywords Bioinformatics; multiple hypothesis testing; false discovery rates; data integration; empirical Bayes  
  Abstract n 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.  
  Publisher Place Editor  
  Language Summary Language Orig Title  
  Series Editor Series Title Abbrev Series Title  
  Series Volume Series Issue Edition  
  Issn Isbn Medium  
  Area Expedition Conference  
  Notes Approved no  
  Location Torfinn Nome (torfinn.nome@umb.no)  
  Call Number Cigene @ torfinn.nome @ Serial 1544  
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