Meta-analysis of RNA-seq studies with an adaptive weight and truncation p-value combination test.

For RNA-seq studies, an important task is to identify genes that are differentially expressed between groups. Nevertheless, the identification of differentially expressed (DE) genes from a single study can be challenging as the small number of samples and the high sensitivity of data perturbations. Combining p -values from multiple RNA-seq studies can increase the statistical power and the accuracy in detecting DE genes. In this paper, we propose a weight and truncation p -value combination test for meta-analyzing RNA-seq studies. We show that with proper weight and truncation parameters, our new test has a higher statistical power over the existing tests, when the genes are weakly expressed in most studies and the sample size is unbalanced. We then present simulations and apply to two real data analysis. Finally, we provide some practical guidance for our new test and some existing p -value combination tests. • We propose a new p-value combination test for meta-analyzing multiple RNA-seq studies. • The new proposed test adopts data-driven weights and truncation p-values. • The performance of our new test exhibit some superiorities over the existing tests. • We provide some guidelines for the use of existing p-value combination tests. [ABSTRACT FROM AUTHOR]