Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
|Authors:||Ignatiadis N, Klaus B, Zaugg JB, Huber W|
|CellNetworks People:||Huber Wolfgang|
|Journal:||Nat Methods. 2016 Jul;13(7):577-80. doi: 10.1038/nmeth.3885|
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 (http://www.bioconductor.org/packages/IHW). 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.