A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data
Open accessAbstract
MOTIVATION: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects. RESULTS: We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/yangzi4/iNMF. CONTACT: gmichail@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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jNMF, PLS, and CCA are frequently employed in multi-omics integration tasks, aiding in the analysis of gene modules and exploring underlying biological mechanisms in the multi-omics latent space.
“These methods (jNMF, PLS, and CCA) are frequently employed in multi-omics integration tasks, aiding in the analysis of gene modules and exploring underlying biological mechanisms in the multi-omics latent space [42,46].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [42]
Section 2.2 (page unavailable) describes jNMF as integrating multiple datasets and detecting multi-dimensional modules, and names multiple CCA and multi-block PLS as other integration methods. The paper links its module analysis to biological patterns, but does not establish that PLS and CCA are frequently employed for gene-module analysis or for exploring mechanisms in a multi-omics latent space. (LLM, conf=0.84)
Experimental — accuracy not yet measuredShallow learning methods including jNMF, PLS, CCA, and MKL have been developed for multimodal representation learning.
“In the early stages of multimodal representation learning, numerous shallow learning methods were developed and utilized. Notable approaches include joint non-negative matrix factorization (jNMF) methods [42], partial least square (PLS) [43], canonical correlation analysis (CCA) [44], and multiple kernel learning (MKL) [45].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [42]
The authors developed a novel block coordinate descent algorithm for integrative non-negative matrix factorization.
“We developed a novel block coordinate descent algorithm for performing integrative non-negative matrix factorization (Yang and Michailidis, 2016).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Yang and Michailidis, 2016
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- Authors
- Yang, Z., & Michailidis, G.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Michailidis, George
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-09-15
- Volume
- 32
- Issue
- 1
- Pages
- 1-8
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5006236
- OpenAlex
- W2180374961
- Cited by
- 295
- References
- 48
- Citations by year
- 2026: 18 · 2025: 26 · 2024: 35 · 2023: 30 · 2022: 35 · 2021: 48 · 2020: 29 · 2019: 25 · 2018: 23 · 2017: 14 · 2016: 11
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1161759, U01 CA167234, 1228164)
- National Cancer Institute (U01CA167234)
- National Institute of General Medical Sciences (R21GM101719)
- 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 › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- non-negative matrix factorization, multimodal data integration, module detection, sparsity, DNA methylation, gene expression, microRNA expression, ovarian cancer, tumor subtypes, cancer pathways, integrative network analysis
- MeSH terms
- Algorithms, Computer Simulation, Female, Humans, Ovarian Neoplasms, Reproducibility of Results, DNA Methylation, Genomics, Databases, Genetic, MicroRNAs
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