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spAttClu: a spatial domain clustering model leveraging spatially weighted graph attention and contrastive learning
Tianjiao Zhang1, Ruolan Zhang1, Hongfei Zhang1
1School of Computer Science and Artificial Intelligence, Northeast Forestry University, Harbin, 150040, China.
Bioinformatics (Oxford, England)
|June 15, 2026
Summary
We developed spAttClu, a new spatial transcriptomics clustering model. It improves spatial domain recognition by adaptively learning neighbor importance, enhancing accuracy and robustness for tissue analysis.
Area of Science:
- Computational biology
- Genomics
Background:
- Spatial transcriptomics data is rapidly growing, offering insights into tissue heterogeneity.
- Recognizing spatial domains is crucial for understanding tissue architecture and function.
- Existing models struggle with dynamic neighbor importance, limiting accuracy.
Purpose of the Study:
- To develop an advanced clustering model for spatial transcriptomics data.
- To improve the accuracy and robustness of spatial domain recognition.
- To enable better analysis of tissue heterogeneity and spatial specificity.
Main Methods:
- Proposed spAttClu, a model integrating spatially-weighted graph attention and contrastive learning.
- Employed a distance-weighted graph attention mechanism for adaptive neighbor weighting.
- Utilized multi-level contrastive learning to enhance embedding discriminability.
Main Results:
- spAttClu demonstrated superior clustering performance on the DLPFC dataset.
- The model exhibits cross-platform adaptability.
- spAttClu facilitates the integration of multiple tissue slices.
Conclusions:
- spAttClu offers a robust and accurate method for spatial domain recognition in transcriptomics.
- The model's adaptive learning improves upon existing static approaches.
- spAttClu has potential for diverse applications in spatial transcriptomics analysis.
