An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies

Details An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies

2011-08-16

arxiv.org/abs/1108.3180v1

Annals of Applied Statistics 2011, Vol. 5, No. 2A, 994-1019

Statistics

Applications

Published in at http://dx.doi.org/10.1214/10-AOAS393 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS393

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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