Journal Article

A practical guide to methods controlling false discoveries in computational biology

Korthauer, Keegan, Kimes, Patrick K., Reyes, Alejandro, Teng, Mingxiang, Alm, Eric J., Shukla, Chinmay, Hicks, Stephanie C., Duvallet, Claire, Subramanian, Ayshwarya

2019

Overview

Background 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 investigate 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. Results Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses. Conclusions Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Animal Genetics and Genomics / Benchmarking Studies / Bioinformatics / Biology / Biomedical and Life Sciences / Biotechnology & Applied Microbiology / case studies / ChIP-seq / Computational Biology - methods / Computational Biology - standards / Computer applications / Computer Simulation / control methods / Evolutionary Biology / False discovery rate / Gene expression / Gene set analysis / Generalized linear models / Genetics & Heredity / genome / Genomes / GWAS / Human Genetics / Life Sciences / Methods / Microbial Genetics and Genomics / Microbiome / Microbiota / Multiple hypothesis testing / Plant Genetics and Genomics / Power / RNA-seq / ScRNA-seq / statistical analysis / testing