Network-based analysis of omics data: the LEAN method Article Swipe
Frederik Gwinner
,
Gwénola Boulday
,
Claire Vandiedonck
,
Minh Arnould
,
Cécile Cardoso
,
Iryna Nikolayeva
,
Oriol Guitart-Pla
,
Cécile V. Denis
,
Olivier D. Christophe
,
Johann Beghain
,
Elisabeth Tournier‐Lasserve
,
Benno Schwikowski
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btw676
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btw676
Motivation Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters. Results We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments. Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset. Availability and Implementation The R-package LEANR implementing LEAN is supplied as supplementary material and available on CRAN (https://cran.r-project.org). Supplementary information Supplementary data are available at Bioinformatics online.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btw676
- https://academic.oup.com/bioinformatics/article-pdf/33/5/701/25148345/btw676.pdf
- OA Status
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- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2542210920Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btw676Digital Object Identifier
- Title
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Network-based analysis of omics data: the LEAN methodWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
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2016-10-25Full publication date if available
- Authors
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Frederik Gwinner, Gwénola Boulday, Claire Vandiedonck, Minh Arnould, Cécile Cardoso, Iryna Nikolayeva, Oriol Guitart-Pla, Cécile V. Denis, Olivier D. Christophe, Johann Beghain, Elisabeth Tournier‐Lasserve, Benno SchwikowskiList of authors in order
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https://doi.org/10.1093/bioinformatics/btw676Publisher landing page
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https://academic.oup.com/bioinformatics/article-pdf/33/5/701/25148345/btw676.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/article-pdf/33/5/701/25148345/btw676.pdfDirect OA link when available
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Computer science, Software, Data mining, Computational biology, Biology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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24Total citation count in OpenAlex
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2024: 1, 2023: 3, 2022: 1, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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