JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization Article Swipe
Bai Zhang
,
Mengdi Nan
,
Liugen Wang
,
Wu HanWen
,
Xiang Chen
,
Yongle Shi
,
Yibing Ma
,
Jie Gao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12864-025-11462-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12864-025-11462-8
With the rapid advancement of sequencing technology, the increasing availability of single-cell multi-omics data from the same cells has provided us with unprecedented opportunities to understand the cellular phenotypes. Integrating multi-omics data has the potential to enhance the ability to reveal cellular heterogeneity. However, data integration analysis is extremely challenging due to the different characteristics and noise levels of different molecular modalities in single-cell data. In this paper, an unsupervised integration method (JSNMFuP) based on non-negative matrix factorization is proposed. This method integrates the information extracted from the latent variables of each omic through a consensus graph. High-dimensional geometrical structure is captured in the original data and biologically-related feature links across modalities are incorporated into the model using regularization terms. JSNMFuP can be utilized for data visualization and clustering, facilitating marker characterization and gene ontology enrichment analysis, providing rich biological insights for downstream analysis. The application on real datasets shows that JSNMFuP has superior performance in cell clustering. The factors are interpretable, making it an effective method for analyzing cell heterogeneity using single-cell multi-omics data.
Related Topics
Concepts
Computational biology
Non-negative matrix factorization
Proteomics
Biology
Matrix decomposition
Computer science
Genetics
Physics
Gene
Quantum mechanics
Eigenvalues and eigenvectors
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12864-025-11462-8
- https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-025-11462-8
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408676021
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408676021Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12864-025-11462-8Digital Object Identifier
- Title
-
JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-20Full publication date if available
- Authors
-
Bai Zhang, Mengdi Nan, Liugen Wang, Wu HanWen, Xiang Chen, Yongle Shi, Yibing Ma, Jie GaoList of authors in order
- Landing page
-
https://doi.org/10.1186/s12864-025-11462-8Publisher landing page
- PDF URL
-
https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-025-11462-8Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-025-11462-8Direct OA link when available
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Computational biology, Non-negative matrix factorization, Proteomics, Biology, Matrix decomposition, Computer science, Genetics, Physics, Gene, Quantum mechanics, Eigenvalues and eigenvectorsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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