Oct 31, 2018

A practical guide to methods controlling false discoveries in computational biology

BioRxiv : the Preprint Server for Biology
Keegan KorthauerStephanie C Hicks

Abstract

In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p-values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as "informative covariates" to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigated the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology. Methods that incorporate informative covariates were modestly more powerful than classic approaches, and did not underperform classic approaches, even when the covariate was completely uninformative. The majority of methods were successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we found the improvement of the modern FDR methods over the classic methods increased with the informativeness of the covariate,...Continue Reading

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Mentioned in this Paper

Study
Laboratory Procedures
Active Control
Research Personnel
Computational Biology
Simulation
Statistical Technique
High-Throughput RNA Sequencing
Disorder Control
Computational Molecular Biology

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