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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative

Bai Zhang1, Mengdi Nan1, Liugen Wang2

  • 1School of Science, Jiangnan University, Wuxi, Jiangsu, China.

BMC Genomics
|March 21, 2025
PubMed
Summary

This study introduces JSNMFuP, a new method for integrating single-cell multi-omics data. It effectively reveals cellular heterogeneity and improves cell clustering performance for biological insights.

Keywords:
Data integrationNon-negative matrix factorizationSingle-cell multi-omics data

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell multi-omics data offers deep insights into cellular phenotypes.
  • Integrating diverse omics data is crucial for understanding cellular heterogeneity.
  • Challenges exist in integrating single-cell data due to differing modalities and noise.

Purpose of the Study:

  • To develop an unsupervised method for integrating single-cell multi-omics data.
  • To enhance the analysis of cellular heterogeneity.
  • To improve cell clustering and downstream biological interpretation.

Main Methods:

  • Proposed JSNMFuP, an unsupervised integration method based on non-negative matrix factorization.
  • Integrated omics information via latent variables using a consensus graph.
  • Incorporated high-dimensional geometric structure and cross-modal feature links using regularization.

Main Results:

  • JSNMFuP demonstrated superior performance in cell clustering on real datasets.
  • The method effectively captures high-dimensional geometric structure.
  • Identified interpretable factors for analyzing cell heterogeneity.

Conclusions:

  • JSNMFuP is an effective method for single-cell multi-omics data integration.
  • Facilitates data visualization, clustering, marker characterization, and gene ontology enrichment.
  • Provides valuable biological insights for downstream analysis of cell heterogeneity.