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Extracting associations that recur across multiple studies while controllingthe false discovery rate is a fundamental challenge. Here, we consider anextension of Efron's single-study two-groups model to allow joint analysis ofmultiple studies. We assume that given a set of p-values obtained from eachstudy, the researcher is interested in associations that recur in at least$k>1$ studies. We propose new algorithms that differ in how the studydependencies are modeled. We compared our new methods and others using varioussimulated scenarios. The top performing algorithm, SCREEN (ScalableCluster-based REplicability ENhancement), is our new algorithm that is based onthree stages: (1) clustering an estimated correlation network of the studies,(2) learning replicability (e.g., of genes) within clusters, and (3) mergingthe results across the clusters using dynamic programming. We applied SCREEN totwo real datasets and demonstrated that it greatly outperforms the resultsobtained via standard meta-analysis. First, on a collection of 29 case-controllarge-scale gene expression cancer studies, we detected a large up-regulatedmodule of genes related to proliferation and cell cycle regulation. These genesare both consistently up-regulated across many cancer studies, and are wellconnected in known gene networks. Second, on a recent pan-cancer study thatexamined the expression profiles of patients with or without mutations in theHLA complex, we detected an active module of up-regulated genes that arerelated to immune responses. Thanks to our ability to quantify the falsediscovery rate, we detected thrice more genes as compared to the originalstudy. Our module contains most of the genes reported in the original study,and many new ones. Interestingly, the newly discovered genes are needed toestablish the connectivity of the module.

Extracting replicable associations across multiple studies: algorithms for controlling the false discovery rate