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Cited by 126Gene expression and cancer classificationGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

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Van den Berge, Koen, Soneson, Charlotte, Robinson, Mark D., Clement, Lieven·2017·Genome Biology
DOI 10.1186/s13059-017-1277-0PMC PMC5547545

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Abstract

Abstract Background Reductions in sequencing cost and innovations in expression quantification have prompted an emergence of RNA-seq studies with complex designs and data analysis at transcript resolution. These applications involve multiple hypotheses per gene, leading to challenging multiple testing problems. Conventional approaches provide separate top-lists for every contrast and false discovery rate (FDR) control at individual hypothesis level. Hence, they fail to establish proper gene-level error control, which compromises downstream validation experiments. Tests that aggregate individual hypotheses are more powerful and provide gene-level FDR control, but in the RNA-seq literature no methods are available for post-hoc analysis of individual hypotheses. Results We introduce a two-stage procedure that leverages the increased power of aggregated hypothesis tests while maintaining high biological resolution by post-hoc analysis of genes passing the screening hypothesis. Our method is evaluated on simulated and real RNA-seq experiments. It provides gene-level FDR control in studies with complex designs while boosting power for interaction effects without compromising the discovery of main effects. In a differential transcript usage/expression context, stage-wise testing gains power by aggregating hypotheses at the gene level, while providing transcript-level assessment of genes passing the screening stage. Finally, a prostate cancer case study highlights the relevance of combining gene with transcript level results. Conclusion Stage-wise testing is a general paradigm that can be adopted whenever individual hypotheses can be aggregated. In our context, it achieves an optimal middle ground between biological resolution and statistical power while providing gene-level FDR control, which is beneficial for downstream biological interpretation and validation.

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  • The standard error of interaction effect sex × Braak stage is typically much larger than those for main Braak stage effects.

    “because the standard error of interaction effect sex × Braak stage is typically much larger than those for main Braak stage effects”

    From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [36]

    The Background states that interaction effects in complex differential-expression studies typically have much larger standard errors than main effects. It does not examine sex × Braak stage; its example is a treatment × time interaction, so the specific application is not established by this paper. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The conventional approach for controlling false discovery rate often results in low power for discovering interaction effects.

    “the conventional approach for controlling false discovery rate often results in low power for discovering interaction effects [36]”

    From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [36]

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Authors
Van den Berge, K., Soneson, C., Robinson, M. D., & Clement, L.
Affiliations
  • SIB Swiss Institute of Bioinformatics (CH)
  • Ghent University (BE)
Corresponding author
Clement, Lieven
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2017-08-07
Volume
18
Issue
1
Pages
151-151
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-017-1277-0
PMC
PMC5547545
OpenAlex
W2744783701
Cited by
126
References
4
Citations by year
2026: 21 · 2025: 20 · 2024: 18 · 2023: 11 · 2022: 15 · 2021: 21 · 2020: 8 · 2019: 5 · 2018: 6 · 2017: 1
FWCI
2.23
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • IAP research network: StUDyS grant (P7/06)
  • Research Foundation - Flanders (FWO)
  • Forschungskredit (FK-16-107)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
false discovery rate control, differential gene expression, differential transcript usage, RNA sequencing, two-stage testing, interaction effects, multiple comparisons
MeSH terms
Algorithms, Animals, Computer Simulation, Gene Expression Regulation, Humans, Sensitivity and Specificity, Reproducibility of Results, Computational Biology, Gene Expression Profiling, Genetic Association Studies

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