New paper: Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

Abstract: Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test’s power or prior probability of the null hypothesis: IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.

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Welcome Arne Smits

Arne has a PhD degree from the Radboud University (Nijmegen, The Netherlands). His previous research in the laboratory of Prof. Michiel Vermeulen focused on the identification and quantification of protein-protein and DNA-protein interactions using quantitative proteomics. As a EIPOD postdoctoral fellow at EMBL, he is studying the systems-wide effects of drug treatment in collaboration with Cellzome and the Steinmetz group.