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Global expression analyses using microarray technologies are becoming morecommon in genomic research, therefore, new statistical challenges associatedwith combining information from multiple studies must be addressed. In thispaper we will describe our proposal for an adaptively weighted (AW) statisticto combine multiple genomic studies for detecting differentially expressedgenes. We will also present our results from comparisons of our proposed AWstatistic 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 AWstatistic consistently produced the best or near-best power for a range ofalternative hypotheses. AW-obtained weights also have the additional advantageof filtering discordant biomarkers and providing natural detected genecategories for further biological investigation. Here we will demonstrate thesuperior performance of our proposed AW statistic based on a mix of poweranalyses, simulations and applications using data sets for multi-tissue energymetabolism mouse, multi-lab prostate cancer and lung cancer.

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