Integrative multi-omics module network inference with lemon-tree
Open accessAbstract
Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.
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Lemon-Tree implemented ensemble methods for reconstructing module networks using somatic copy number alterations and gene expression in brain tumour samples.
“Lemon-Tree (Bonnet et al., 2015) implemented ensemble methods for reconstructing module networks which used somatic copy number alterations and gene expression in brain tumour samples.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Bonnet et al., 2015
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Using machine learning approaches for multi-omics data analysis: A review
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- Authors
- Bonnet, E., Calzone, L., & Michoel, T.
- Affiliations
- Inserm (FR)
- Roslin Institute (GB)
- Corresponding author
- Michoel, Tom
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-02-13
- Volume
- 11
- Issue
- 2
- Pages
- e1003983
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4332478
- OpenAlex
- W2108191052
- Cited by
- 98
- References
- 58
- Citations by year
- 2025: 3 · 2024: 10 · 2023: 8 · 2022: 13 · 2021: 17 · 2020: 9 · 2019: 8 · 2018: 14 · 2017: 9 · 2016: 3 · 2015: 4
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- European Commission (259348)
- Universiteit Gent
- Biotechnology and Biological Sciences Research Council (BBS/E/D/20211552, BBS/E/D/20211551)
- FP7 Health (FP7/2007-2013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics integration, lemon trees, ensemble methods, gene co-expression modules, cancer driver genes, glioblastoma, somatic copy number alterations, gene expression, oncogenes, tumor suppressor, open source software
- MeSH terms
- Glioblastoma, Humans, Software, Signal Transduction, Cluster Analysis, Computational Biology, Internet, Gene Expression Profiling, Databases, Genetic, Kaplan-Meier Estimate
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