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December 2012 Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data
Yunting Sun, Nancy R. Zhang, Art B. Owen
Ann. Appl. Stat. 6(4): 1664-1688 (December 2012). DOI: 10.1214/12-AOAS561

Abstract

In high throughput settings we inspect a great many candidate variables (e.g., genes) searching for associations with a primary variable (e.g., a phenotype). High throughput hypothesis testing can be made difficult by the presence of systemic effects and other latent variables. It is well known that those variables alter the level of tests and induce correlations between tests. They also change the relative ordering of significance levels among hypotheses. Poor rankings lead to wasteful and ineffective follow-up studies. The problem becomes acute for latent variables that are correlated with the primary variable. We propose a two-stage analysis to counter the effects of latent variables on the ranking of hypotheses. Our method, called LEAPP, statistically isolates the latent variables from the primary one. In simulations, it gives better ordering of hypotheses than competing methods such as SVA and EIGENSTRAT. For an illustration, we turn to data from the AGEMAP study relating gene expression to age for 16 tissues in the mouse. LEAPP generates rankings with greater consistency across tissues than the rankings attained by the other methods.

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Yunting Sun. Nancy R. Zhang. Art B. Owen. "Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data." Ann. Appl. Stat. 6 (4) 1664 - 1688, December 2012. https://doi.org/10.1214/12-AOAS561

Information

Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62115
MathSciNet: MR3058679
Digital Object Identifier: 10.1214/12-AOAS561

Keywords: EIGENSTRAT , empirical null , surrogate variable analysis

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 4 • December 2012
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