UNSUPERVISED EMPIRICAL BAYESIAN MULTIPLE TESTING WITH EXTERNAL COVARIATES

被引：38

作者：

Ferkingstad, Egil ^{[1
]}

Frigessi, Arnoldo ^{[2
]}

Rue, Havard ^{[3
]}

Thorleifsson, Gudmar ^{[4
]}

Kong, Augustine ^{[4
]}

机构：

[1] Univ Oslo, Dept Biostat, N-0317 Oslo, Norway

[2] Norwegian Comp Ctr, Stat Innovat SFI2, N-0314 Oslo, Norway

[3] Norwegian Univ Sci & Technol, Dept Math Sci, N-7194 Trondheim, Norway

[4] Decode Genet, IS-101 Reykjavik, Iceland

来源：

ANNALS OF APPLIED STATISTICS | 2008年 / 2卷 / 02期

关键词：

Bioinformatics; multiple hypothesis testing; false discovery rates; data integration; empirical Bayes;

D O I：

10.1214/08-AOAS158

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：714 / 735

页数：22

共 38 条

[1] Adapting to unknown sparsity by controlling the false discovery rate [J].