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June 2011 An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies
Jia Li, George C. Tseng
Ann. Appl. Stat. 5(2A): 994-1019 (June 2011). DOI: 10.1214/10-AOAS393

Abstract

Global expression analyses using microarray technologies are becoming more common in genomic research, therefore, new statistical challenges associated with combining information from multiple studies must be addressed. In this paper we will describe our proposal for an adaptively weighted (AW) statistic to combine multiple genomic studies for detecting differentially expressed genes. We will also present our results from comparisons of our proposed AW statistic to Fisher’s equally weighted (EW), Tippett’s minimum p-value (minP) and Pearson’s (PR) statistics. Due to the absence of a uniformly powerful test, we used a simplified Gaussian scenario to compare the four methods. Our AW statistic consistently produced the best or near-best power for a range of alternative hypotheses. AW-obtained weights also have the additional advantage of filtering discordant biomarkers and providing natural detected gene categories for further biological investigation. Here we will demonstrate the superior performance of our proposed AW statistic based on a mix of power analyses, simulations and applications using data sets for multi-tissue energy metabolism mouse, multi-lab prostate cancer and lung cancer.

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Jia Li. George C. Tseng. "An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies." Ann. Appl. Stat. 5 (2A) 994 - 1019, June 2011. https://doi.org/10.1214/10-AOAS393

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 05961700
MathSciNet: MR2840184
Digital Object Identifier: 10.1214/10-AOAS393

Keywords: adaptively weighted statistics , genomic study , Meta-analysis

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2A • June 2011
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