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The large-scale multiple testing inherent to high throughput biological datanecessitates very high statistical stringency and thus true effects in data aredifficult to detect unless they have high effect sizes. One promising approachfor reducing the multiple testing burden is to use independent information toprioritize the features most likely to be true effects. However, using theindependent data effectively is challenging and often does not lead tosubstantial gains in power. Current state-of-the-art methods sort features intogroups by the independent information and calculate weights for each group.However, when true effects are weak and rare (the typical situation for highthroughput biological studies), all groups will contain many null tests andthus their weights are diluted, and performance suffers. We introduce CovariateRank Weighting (CRW), a method for calculating approximate optimal weightsconditioned on the ranking of tests by an external covariate. This approachuses the probabilistic relationship between covariate ranking and test effectsize to calculate individual weights for each test that are more informativethan group weights and are not diluted by null effects. We show how thisrelationship can be calculated theoretically for normally distributedcovariates. It can be estimated empirically in other cases. We show viasimulations and applications to data that this method outperforms existingmethods by as much as 10-fold in the rare/low effect size scenario common tobiological data and has at least comparable performance in all scenarios.

Optimal Covariate Weighting Increases Discoveries in High-throughput Biology