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Contrastive Graph Regularized Non-negative Matrix Factorization For Domain Identification Of Spatial Transcriptomics.

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Contrastive Graph Regularized Non-negative Matrix Factorization For Domain Identification Of Spatial Transcriptomics.

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Contrastive graph regularized non-negative matrix factorization for domain identification of spatial transcriptomics.

Juntao Li¹, Jiuxi Huang¹, Tianhai Tian²

¹Henan Normal University , Xinxiang, Henan, People's Republic of China.

Journal of the Royal Society, Interface

|May 20, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A new method called contrastive graph-regularized non-negative matrix factorization (CGNMF) improves spatial domain identification in spatial transcriptomics. This approach enhances feature representation and spatial structure preservation for more accurate biological insights.

Keywords:

computational biology contrastive learning non-negative matrix factorization spatial transcriptomics

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics provides gene expression data with spatial resolution, but high dimensionality hinders spatial domain identification.
Existing deep learning methods offer feature extraction but lack interpretability, necessitating dimensionality reduction techniques that preserve spatial and biological relevance.

Purpose of the Study:

To propose a novel contrastive graph-regularized non-negative matrix factorization (CGNMF) model for interpretable dimensionality reduction in spatial transcriptomics.
To enhance feature representation and spatial structure preservation for improved spatial domain identification.

Main Methods:

Developed a CGNMF model integrating graph regularization with a self-supervised contrastive learning framework.
Constructed positive and negative sample pairs using gene expression similarity and spatial proximity.

Incorporated contrastive learning into graph-regularized non-negative matrix factorization to guide factorization towards biologically and spatially coherent dimensions.

Main Results:

CGNMF consistently outperformed seven existing spatial domain identification methods across three public datasets based on clustering metrics.
The model successfully identified biologically relevant functional regions missed by current approaches.
Demonstrated improved interpretability and automatic delineation of spatial domains.

Conclusions:

CGNMF offers a robust and interpretable approach for dimensionality reduction in spatial transcriptomics.
The method enhances the identification of spatial domains and biological insights from spatial transcriptomics data.
CGNMF represents a significant advancement in analyzing high-dimensional spatial transcriptomics data.