NEMO: cancer subtyping by integration of partial multi-omic data
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Abstract
MOTIVATION: Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. RESULTS: We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. AVAILABILITY AND IMPLEMENTATION: Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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SUMO slightly outperformed NEMO in robustness to varying levels of missing samples, except at extreme missingness of 80% and greater.
“These comparisons indicated a slight outperformance by SUMO, except for in the cases of extreme missingness (80% and greater).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport and Shamir, 2019
SUMO's range of adjusted rand index values was typically narrower than NEMO's, suggesting less variability in expected performance.
“As well, the range of adjusted rand index values (the performance metric used) was typically observed to be narrower for SUMO, suggesting less variability in expected performance.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport and Shamir, 2019
NEMO can handle partially observed samples provided that these samples are observed in at least one view common to other observed samples.
“NEMO can handle partially observed samples, provided that these samples are observed in at least one view common to other observed samples.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Rappoport and Shamir, 2019
NEMO can work on partial datasets without imputation where measurements are available for only a subset of omics.
“It can work on partial data sets (no imputation needed), where measurements are only available for a subset of omics data.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Rappoport and Shamir, 2019
NEMO combines omics matrices into one matrix and analyses it using spectral-based clustering.
“These omics matrices are then combined into one matrix and then analysed using spectral-based clustering.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Rappoport and Shamir, 2019
NEMO uses an inter-patient similarity matrix-based distance metric to evaluate input omic datasets individually.
“Recently, NEMO (NEighborhood based Multi-Omics clustering) (Rappoport and Shamir, 2019) is introduced which uses an inter-patient similarity matrix-based distance metric for evaluating the input omic datasets individually.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Rappoport and Shamir, 2019
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“Neighborhood based Multi-Omics clustering (NEMO; Rappoport and Shamir, 2019) is an algorithm that generates clusters based on individual data view similarity matrices.”
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“…(e.g., Perturbation clustering for data integration and disease subtyping(PINSPlus) [106], Neighborhood-based multi-omics clustering (NEMO) [107]), and other multivariate approaches (e.g., mixOMICS [108], moCluster [109], Integrative nonnegative matrix factorization (NMF) [110]) [111,112].”
BackgroundMONET: Multi-omic module discovery by omic selection
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“In terms of runtime, SNF and NEMO required seconds per dataset, MONET and MOFA+ a few minutes, and the remaining methods were an order of magnitude slower ( MONET discovered modules that use different combinations of omics ( Fig 2F ).”
MethodInfluentialDetecting molecular subtypes from multi-omics datasets using SUMO
Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., & Ratan, A. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100152 · Cited by 18 · see citing works
“A more recent method, NEMO (Rappoport and Shamir, 2019b), calculates an average similarity matrix and then detects the clusters using spectral clustering.”
Influential
- Authors
- Rappoport, N., & Shamir, R.
- Affiliations
- Tel Aviv University (IL)
- Blavatnik School of Computer Science, Tel Aviv University , Tel Aviv, Israel
- ORCID
- Rappoport, Nimrod
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-01-30
- Volume
- 35
- Issue
- 18
- Pages
- 3348-3356
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6748715
- OpenAlex
- W2950878899
- Cited by
- 286
- References
- 44
- Citations by year
- 2026: 38 · 2025: 70 · 2024: 48 · 2023: 39 · 2022: 36 · 2021: 34 · 2020: 18 · 2019: 3
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- United States - Israel Binational Science Foundation (2016694)
- BSF
- United States National Science Foundation
- NSF
- Naomi Prawer Kadar Foundation
- Bella Walter Memorial Fund of the Israel Cancer Association
- Edmond J. Safra Center for Bioinformatics at Tel-Aviv University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- cancer subtyping, multi-omics clustering, neighborhood-based clustering, acute myeloid leukemia, multi-view clustering
- MeSH terms
- Algorithms, Humans, Neoplasms, Software, Cluster Analysis
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