  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.
