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False discovery rate control for grouped hypotheses: application to miRNAome data.

Nilanjana Laha1, Salil Koner2, Austin Labowitz1

  • 1Department of Statistics, Texas A&M University, College Station, United States of America.

Peerj
|June 1, 2026
PubMed
Summary

Group-adaptive methods improve false discovery rate control in bioinformatics by leveraging hypothesis structures. These advanced techniques enhance statistical power in omics studies, identifying more significant results than traditional methods.

Keywords:
Benjamini Hochberg methodFalse discovery rateGrouped Benjamini HochbergMiRNA deregulationMultiple hypothesis testingOral squamous cell carcinoma

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • Bioinformatics studies involve many statistical tests, risking false discoveries.
  • The Benjamini-Hochberg (BH) method controls the false discovery rate (FDR) but can be conservative, especially with small samples or without using group structures.
  • Group structures, like co-regulated genes, can improve statistical power.

Purpose of the Study:

  • To demonstrate the practical application of group-adaptive Benjamini-Hochberg (BH) methods in bioinformatics.
  • To show how these methods can improve statistical power and FDR control by utilizing pre-defined group information.
  • To evaluate the effectiveness of group-adaptive BH methods on a microRNA (miRNA) dataset.

Main Methods:

  • Applied group-adaptive BH methods to a microRNA (miRNA) dataset with groupings based on chromosomal location.
  • Compared the performance of group-adaptive methods against the traditional BH method for FDR control.
  • Analyzed the impact of different grouping strategies on method sensitivity.

Main Results:

  • Group-adaptive BH methods identified more significantly deregulated miRNAs (FDR-adjusted p-value < 0.05) compared to the traditional BH method.
  • New discoveries were largely supported by existing literature and previous studies.
  • While method sensitivity varied with grouping strategy, most group-adaptive methods showed improved detection due to incorporating group information.

Conclusions:

  • Group-adaptive BH methods offer enhanced FDR control and statistical power in omics studies with known group structures.
  • These specialized methods are underutilized in bioinformatics practice but show significant potential.
  • Further evaluation of group-adaptive BH methods across diverse datasets is warranted to assess their generalizability.