High-dimensional false discovery rate control for dependent variables Article Swipe
Jasin Machkour
,
Michael Muma
,
Daniel P. Palomar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.sigpro.2025.109990
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.sigpro.2025.109990
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples. However, these methods often fail to reliably control the FDR in the presence of highly dependent variable groups, a common characteristic in fields such as genomics and finance. To tackle this critical issue, we introduce a novel framework that accounts for general dependency structures. Our proposed dependency-aware T-Rex selector integrates hierarchical graphical models within the T-Rex framework to effectively harness the dependency structure among variables. Leveraging martingale theory, we prove that our variable penalization mechanism ensures FDR control. We further generalize the FDR-controlling framework by stating and proving a clear condition necessary for designing both graphical and non-graphical models that capture dependencies. Numerical experiments and a breast cancer survival analysis use-case demonstrate that the proposed method is the only one among the state-of-the-art benchmark methods that controls the FDR and reliably detects genes that have been previously identified to be related to breast cancer. An open-source implementation is available within the R package TRexSelector on CRAN.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.sigpro.2025.109990
- OA Status
- hybrid
- Cited By
- 2
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408476096
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408476096Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.sigpro.2025.109990Digital Object Identifier
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High-dimensional false discovery rate control for dependent variablesWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-15Full publication date if available
- Authors
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Jasin Machkour, Michael Muma, Daniel P. PalomarList of authors in order
- Landing page
-
https://doi.org/10.1016/j.sigpro.2025.109990Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.sigpro.2025.109990Direct OA link when available
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False discovery rate, Computer science, Mathematics, Biology, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.experiments | 147 |
| abstract_inverted_index.open-source | 189 |
| abstract_inverted_index.structures. | 87 |
| abstract_inverted_index.TRexSelector | 197 |
| abstract_inverted_index.hierarchical | 94 |
| abstract_inverted_index.large-scale, | 6 |
| abstract_inverted_index.multivariate | 19 |
| abstract_inverted_index.penalization | 117 |
| abstract_inverted_index.reproducible | 3 |
| abstract_inverted_index.applications. | 15 |
| abstract_inverted_index.dependencies. | 145 |
| abstract_inverted_index.non-graphical | 141 |
| abstract_inverted_index.characteristic | 64 |
| abstract_inverted_index.implementation | 190 |
| abstract_inverted_index.FDR-controlling | 126 |
| abstract_inverted_index.dependency-aware | 90 |
| abstract_inverted_index.high-dimensional | 7, 32 |
| abstract_inverted_index.state-of-the-art | 166 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.96563211 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |