False discovery rate control: Moving beyond the Benjamini–Hochberg method

Abstract

Abstract Modern bioinformatics studies often involve numerous simultaneous statistical tests, increasing the risk of false discoveries. To control the false discovery rate (FDR), these studies typically employ a statistical method called the Benjamini–Hochberg (BH) method. Often, the BH approach tends to be overly conservative and overlooks valuable biological insights associated with data structures, particularly those of groups. Group structures can manifest when closely located genomic coordinates are functionally active and closely related because of co-regulation. Recent statistical advancements have led to the development of updated BH methods tailored for datasets featuring pre-existing group structures. These methods can improve the statistical power and potentially enhance scientific discoveries. In this study, we elucidated the advantages of contemporary group-aware BH methods using a previously published microRNA (miRNA) dataset. For this dataset, group-aware BH methods identified a larger set of miRNAs with significantly deregulated expression (p-value < 0.05) than the traditional BH method. These new findings are supported by existing literature on miRNAs and a related 2017 study. Our results underscore the potential of specialized BH methods for controlling the FDR in high throughput omics studies with pre-defined group structures.