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GATCL: graph attention network meets contrastive learning for spatial domain identification.

Jichong Mu1,2, Yachen Yao1, Qiuhao Chen1,2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Xidazhi St 90, 150000, Harbin, Heilongjiang, China.

Briefings in Bioinformatics
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

GATCL, a new deep learning framework, enhances spatial domain identification by using graph attention and contrastive learning to better model cellular interactions and align multi-omics data for improved tissue analysis.

Keywords:
contrastive learninggraph attention networkspatial domain identificationspatial multi-omics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Spatial domain identification is crucial for understanding tissue heterogeneity and cellular microenvironments.
  • Spatial multi-omics advances insights into cell community dynamics but faces challenges with static graph structures and modality-specific noise.

Purpose of the Study:

  • To present GATCL, a novel deep learning framework for robust spatial domain identification.
  • To overcome limitations of existing methods in capturing nuanced cellular interactions and aligning multi-modal spatial data.

Main Methods:

  • GATCL integrates a graph attention network (GAT) to dynamically weight neighboring cells, capturing complex cellular architecture.
  • A cross-modal contrastive learning (CL) strategy aligns multi-omics data by enforcing similarity for same-location data and dissimilarity for different-location data.

Main Results:

  • GATCL demonstrates superior performance in spatial domain identification compared to seven representative methods.
  • Experiments across six diverse datasets (transcriptome, proteome, chromatin) validate GATCL's effectiveness.

Conclusions:

  • GATCL offers a robust and effective approach for spatial domain identification using spatial multi-omics data.
  • The framework's ability to model cellular architecture and align modalities advances the analysis of complex biological tissues.