Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations

Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations

Abstract The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very...

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Bibliographic Details
Main Authors: Chakravarthi Kanduri, Maria Mamica, Emilie Willoch Olstad, Manuela Zucknick, Jingyi Jessica Li, Geir Kjetil Sandve
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Genome Biology
Subjects:
False discovery rate (FDR)
Benjamini-Hochberg (BH)
Multiple testing adjustment
High-dimensional omics data
Online Access:https://doi.org/10.1186/s13059-025-03734-z
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Summary:Abstract The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.
ISSN:1474-760X

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