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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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03734-z |
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| author | Chakravarthi Kanduri Maria Mamica Emilie Willoch Olstad Manuela Zucknick Jingyi Jessica Li Geir Kjetil Sandve |
| author_facet | Chakravarthi Kanduri Maria Mamica Emilie Willoch Olstad Manuela Zucknick Jingyi Jessica Li Geir Kjetil Sandve |
| author_sort | Chakravarthi Kanduri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2b453fd8bd04b4990dc4e7e852a7e8a |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-b2b453fd8bd04b4990dc4e7e852a7e8a2025-08-20T03:46:54ZengBMCGenome Biology1474-760X2025-08-0126111710.1186/s13059-025-03734-zBeware of counter-intuitive levels of false discoveries in datasets with strong intra-correlationsChakravarthi Kanduri0Maria Mamica1Emilie Willoch Olstad2Manuela Zucknick3Jingyi Jessica Li4Geir Kjetil Sandve5Scientific Computing and Machine Learning Section, Department of Informatics, University of OsloScientific Computing and Machine Learning Section, Department of Informatics, University of OsloUiORealArt Convergence Environment, University of OsloDepartment of Biostatistics, Faculty of Medicine, University of OsloDepartment of Statistics and Data Science, University of CaliforniaScientific Computing and Machine Learning Section, Department of Informatics, University of OsloAbstract 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.https://doi.org/10.1186/s13059-025-03734-zFalse discovery rate (FDR)Benjamini-Hochberg (BH)Multiple testing adjustmentHigh-dimensional omics data |
| spellingShingle | Chakravarthi Kanduri Maria Mamica Emilie Willoch Olstad Manuela Zucknick Jingyi Jessica Li Geir Kjetil Sandve Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations Genome Biology False discovery rate (FDR) Benjamini-Hochberg (BH) Multiple testing adjustment High-dimensional omics data |
| title | Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations |
| title_full | Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations |
| title_fullStr | Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations |
| title_full_unstemmed | Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations |
| title_short | Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations |
| title_sort | beware of counter intuitive levels of false discoveries in datasets with strong intra correlations |
| topic | False discovery rate (FDR) Benjamini-Hochberg (BH) Multiple testing adjustment High-dimensional omics data |
| url | https://doi.org/10.1186/s13059-025-03734-z |
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