MONET: Multi-omic module discovery by omic selection
Open accessPublished version of a preprint
Abstract
Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis.
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MONET has no constraint on the number of detected modules and no upper bound on module size.
“Notably, there is no constraint on the number of detected modules, nor is there an upper bound on module size.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport et al., 2020
MONET incorporates partially observed samples by representing their missing components as edgeless nodes within their corresponding edge-weighted graphs.
“This algorithm incorporates partially observed samples by representing their missing components as edgeless nodes within their corresponding edge-weighted graphs.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport et al., 2020
MONET's authors propose a heuristic algorithm that identifies a local maximum and recommend using the best solution from repeated runs to better approximate the globally maximal solution.
“The authors circumvent this issue by proposing a heuristic algorithm that identifies a local maximum and recommend using the best solution from repeated runs of the algorithm to better approximate the globally maximal solution.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport et al., 2020
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- Authors
- Rappoport, N., Safra, R., & Shamir, R.
- Affiliations
- Tel Aviv University (IL)
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-09-15
- Volume
- 16
- Issue
- 9
- Pages
- e1008182
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W3087138092
- Cited by
- 26
- References
- 38
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 2
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Update notices
- new_version (2020-09-25)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft
- Israel Science Foundation (1339/18, 3165 / 19)
- Tel Aviv University
- Planning and Budgeting Committee of the Council for Higher Education of Israel
- National Human Genome Research Institute
- National Cancer Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- Claude Monet, sample clustering, tumor subtypes, ovarian serous cystadenocarcinoma, single-cell multi-omics, cell type clustering, gene clustering, missing data robustness, TCGA cancer datasets
- MeSH terms
- Algorithms, Humans, Neoplasms, Cluster Analysis, Genomics, Databases, Genetic, Single-Cell Analysis
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