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June 2014 Hypothesis setting and order statistic for robust genomic meta-analysis
Chi Song, George C. Tseng
Ann. Appl. Stat. 8(2): 777-800 (June 2014). DOI: 10.1214/13-AOAS683

Abstract

Meta-analysis techniques have been widely developed and applied in genomic applications, especially for combining multiple transcriptomic studies. In this paper we propose an order statistic of $p$-values ($r$th ordered $p$-value, rOP) across combined studies as the test statistic. We illustrate different hypothesis settings that detect gene markers differentially expressed (DE) “in all studies,” “in the majority of studies” or “in one or more studies,” and specify rOP as a suitable method for detecting DE genes “in the majority of studies.” We develop methods to estimate the parameter $r$ in rOP for real applications. Statistical properties such as its asymptotic behavior and a one-sided testing correction for detecting markers of concordant expression changes are explored. Power calculation and simulation show better performance of rOP compared to classical Fisher’s method, Stouffer’s method, minimum $p$-value method and maximum $p$-value method under the focused hypothesis setting. Theoretically, rOP is found connected to the naïve vote counting method and can be viewed as a generalized form of vote counting with better statistical properties. The method is applied to three microarray meta-analysis examples including major depressive disorder, brain cancer and diabetes. The results demonstrate rOP as a more generalizable, robust and sensitive statistical framework to detect disease-related markers.

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Chi Song. George C. Tseng. "Hypothesis setting and order statistic for robust genomic meta-analysis." Ann. Appl. Stat. 8 (2) 777 - 800, June 2014. https://doi.org/10.1214/13-AOAS683

Information

Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333776
MathSciNet: MR3262534
Digital Object Identifier: 10.1214/13-AOAS683

Keywords: $p$-value , genomics , Meta-analysis , order statistic

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 2 • June 2014
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