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Review
. 2023 Sep;23(18):e2200406.
doi: 10.1002/pmic.202200406. Epub 2023 Jun 25.

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations

Thomas Burger  1
Affiliations
Free article
Review

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations

Thomas Burger. Proteomics. 2023 Sep.
Free article

Abstract

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

Keywords: biomarker discovery; data processing; false discovery rate (FDR); one way analysis of variance (OW-ANOVA); post-hoc tests (PHTs); quantitative proteomics.

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References

REFERENCES

    1. Burger, T. (2018). Gentle introduction to the statistical foundations of false discovery rate in quantitative proteomics. Journal of Proteome Research, 17(1), 12-22.
    1. Barber, R. F., & Candès, E. J. (2015). Controlling the false discovery rate via knockoffs. The Annals of Statistics, 43(5), 2055-2085.
    1. Stephens, M. (2017). False discovery rates: A new deal. Biostatistics, 18(2), 275-294.
    1. Ebrahimpoor, M., & Goeman, J. J. (2021). Inflated false discovery rate due to volcano plots: Problem and solutions. Briefings in Bioinformatics, 22(5), bbab053.
    1. Etourneau, L., Varoquaux, N., & Burger, T. (2021). Unveiling the links between peptide identification and differential analysis FDR controls by means of a practical introduction to knockoff filters. In Statistical analysis of proteomic data: Methods and tools (pp. 1-24). Springer US.

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