A unified and practical taxonomy for false discovery rate control in omics research

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False discovery rate (FDR) control is central to omics research, yet the diverse procedures used rely on different assumptions. This Comment proposes a 2 × 2 matrix that helps sequencing-based and mass-spectrometry-based omics researchers choose the appropriate family of FDR procedures, or alternative methods that may be more rigorous, depending on data and study design.

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Fig. 1: Schematic representation of the proposed taxonomy.
This figure compares theoretical-null and numerical-null approaches (horizontal axis), as well as featured-pooled and feature-specific approaches (vertical axis), for feature selection in omics data analysis. On the left, the theoretical-null (feature-pooled or feature-specific) approaches use feature-pooled P values with a multiple test correction plug-in, which is illustrated using a histogram of P values for omics features and a theoretical distribution for the null group. A selection threshold separates true positives from false negatives. On the top right, the empirical-null approaches (numerical-null, feature-pooled) show histograms of importance scores for all omics features and for null features, with a selection threshold distinguishing true positives and false positives. On the bottom right, proteomics-inspired methods (numerical-null, feature-specific) compare importance scores of real features and fictional features, producing contrast scores. A histogram of contrast scores applies symmetric selection thresholds at negative T and positive T to separate false positives, true negatives, false negatives, and true positives. False discovery rate is approximated as the number of false positives divided by the sum of true positives and false positives in all cases.

References

  1. Benjamini, Y. & Hochberg, Y. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Article  Google Scholar 

  2. Benjamini, Y. & Yekutieli, D. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  3. Elias, J. E. & Gygi, S. P. Nat. Methods 4, 207–214 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Scheubert, K. et al. Nat. Commun. 8, 1494 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Barber, R. F. & Candès, E. J. Ann. Stat. 43, 2055–2085 (2015).

    Article  Google Scholar 

  6. Ge, X. et al. Genome Biol. 22, 288 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Efron, B. J. Am. Stat. Assoc. 99, 96–104 (2004).

    Article  Google Scholar 

  8. Tusher, V. G., Tibshirani, R. & Chu, G. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Käll, L., Storey, J. D., MacCoss, M. J. & Noble, W. S. J. Proteome Res. 7, 29–34 (2008).

    Article  PubMed  Google Scholar 

  10. Li, Y., Ge, X., Peng, F., Li, W. & Li, J. J. Genome Biol. 23, 79 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Danilova, Y., Voronkova, A., Sulimov, P. & Kertész-Farkas, A. J. Proteome Res. 18, 2354–2358 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Etourneau, L. & Burger, T. J. Proteome Res. 21, 2840–2845 (2022).

    Article  CAS  PubMed  Google Scholar 

  13. Wen, B., Freestone, J., Riffle, M., MacCoss, M. J., Noble, W. S. & Keich, U. Nat. Methods 22, 1454–1463 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhou, H. J., Ge, X. & Li, J. J. Genome Biol. 26, 207 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Freestone, J., Short, T., Noble, W. S. & Keich, U. Bioinformatics 38, ii82–ii88 (2022). (Suppl. 2).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Changhu Wang and Chandra Sekhar Mukherjee at the Fred Hutchinson Cancer Center for their valuable feedback. T.B. is supported by the following grants from the French National Research Agency: ProFI project (ANR-10-INBS-08), GRAL CBH project (ANR-17-EURE-0003), France 2030 programme (ANR-19-P3IA-0003), PeptidOMS project (ANR-24-CE45-3296) and ProteoVir project (ANR-24-RRII-0001). J.J.L. is supported by the following grants from the US National Institutes of Health: NIGMS R35GM140888 and NHGRI R01HG014687.

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Authors and Affiliations

  1. University Grenoble Alpes, CNRS, CEA, INSERM, UA13 BGE, FR2048 ProFI, Grenoble, France

    Thomas Burger

  2. Department of Statistics and Data Science, Oregon State University, Corvallis, OR, USA

    Xinzhou Ge

  3. Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Jingyi Jessica Li

Authors

  1. Thomas Burger
  2. Xinzhou Ge
  3. Jingyi Jessica Li

Corresponding authors

Correspondence to Thomas Burger or Jingyi Jessica Li.

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The authors declare no competing interests.

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Burger, T., Ge, X. & Li, J.J. A unified and practical taxonomy for false discovery rate control in omics research. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03379-8

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  • DOI: https://doi.org/10.1038/s41587-026-03379-8

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