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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification.

Juan Liang1, Jiuxi Huang2, Chenxi Xi2

  • 1School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan, China.

Peerj
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

We developed Joint Graph-Regularized Non-negative Matrix Factorization (JGR-NMF) for accurate spatial domain identification in tissues. This method enhances spatial transcriptomics analysis by optimizing neighborhood size and integrating graph structures.

Keywords:
Adjacency matrixNon-negative matrix factorizationSpatial transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics offers novel insights into cellular distribution and tissue architecture.
  • Accurate identification of spatial domains is crucial for understanding tissue function.

Purpose of the Study:

  • To introduce Joint Graph-Regularized Non-negative Matrix Factorization (JGR-NMF) for improved spatial domain identification.
  • To enhance the accuracy and robustness of spatial transcriptomics data analysis.

Main Methods:

  • Developed an adaptive neighborhood graph construction strategy using nth-power transformation.
  • Integrated the adaptive kNN graph with the spatial adjacency matrix within the JGR-NMF framework.

Main Results:

  • JGR-NMF significantly outperformed five state-of-the-art methods on breast cancer, mouse kidney, and mouse embryo datasets.
  • Ablation studies confirmed the importance of graph regularization for performance enhancement.

Conclusions:

  • JGR-NMF provides a robust and accurate approach for spatial domain identification.
  • The adaptive graph construction and graph regularization are key components for improving spatial transcriptomics analysis.