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STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph
Qi Gao1, Junliang Shang1, Shasha Yuan1
1School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
Interdisciplinary Sciences, Computational Life Sciences
|June 15, 2026
Summary
This study introduces STNMAE, a new self-supervised learning framework for spatial domain identification in spatial transcriptomics (ST) data. STNMAE effectively captures complex gene expression and spatial relationships, improving ST data analysis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Spatial transcriptomics (ST) enables gene expression analysis with spatial context.
- Identifying spatial domains is vital for ST research.
- Existing methods struggle with complex gene expression and spatial relationships.
Purpose of the Study:
- To develop a novel self-supervised learning framework for enhanced spatial domain recognition in ST data.
- To address limitations of current methods in capturing intricate spatial-gene expression dependencies.
Main Methods:
- Proposed STNMAE, a neighbor-aware multi-view masked graph autoencoder framework.
- Constructed multiple neighbor views using distinct similarity measures.
- Employed a feature-masked encoder and multi-view autoencoder for expressive embeddings.
- Integrated regularization techniques to prevent overfitting.
Main Results:
- Applied STNMAE to seven diverse ST datasets.
- Demonstrated STNMAE's superiority over state-of-the-art methods.
- Indicated substantial improvements in ST data analysis capabilities.
Conclusions:
- STNMAE effectively identifies spatial domains by leveraging complex relationships in ST data.
- The framework offers a significant advancement for spatial transcriptomics analysis.
- STNMAE shows robust performance across various ST datasets and platforms.
