Smaller $p$-values in genomics studies using distilled historical information

TL;DR

This paper introduces a novel FAB hypothesis testing procedure that leverages extensive historical genomics data to enhance the power of discoveries in new studies, while controlling error rates.

Contribution

The paper proposes a new probability model and FAB testing method that incorporate historical data to improve hypothesis testing in genomics research.

Findings

FAB tests increase discovery power when historical data is relevant.

FAB maintains control of false discovery rates.

Simulation studies confirm improved detection of effects.

Abstract

Medical research institutions have generated massive amounts of biological data by genetically profiling hundreds of cancer cell lines. In parallel, academic biology labs have conducted genetic screens on small numbers of cancer cell lines under custom experimental conditions. In order to share information between these two approaches to scientific discovery, this article proposes a "frequentist assisted by Bayes" (FAB) procedure for hypothesis testing that allows historical information from massive genomics datasets to increase the power of hypothesis tests in specialized studies. The exchange of information takes place through a novel probability model for multimodal genomics data, which distills historical information pertaining to cancer cell lines and genes across a wide variety of experimental contexts. If the relevance of the historical information for a given study is high, then the resulting FAB tests can be more powerful than the corresponding classical tests. If the relevance is low, then the FAB tests yield as many discoveries as the classical tests. Simulations and practical investigations demonstrate that the FAB testing procedure can increase the number of effects discovered in genomics studies while still maintaining strict control of type I error and false discovery rates.