ArticleComputational and structural biotechnology journal2024
Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- Spatial multi-omics integration by cross-modal graph contrastive learning.Briefings in bioinformatics · 2026Article
- SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.Computational and structural biotechnology journal · 2026Article
- Computational pathology in precision oncology: Evolution from task-specific models to foundation models.Chinese medical journal · 2025Review
- VARGG: a deep learning framework advancing precise spatial domain identification and cellular heterogeneity analysis in spatial transcriptomics.Briefings in functional genomics · 2025Article
- Pan-cancer single-cell transcriptomic analysis reveals CD83 as a hallmark of tumor-associated neutrophils with senescent and pro-tumor properties.Computational and structural biotechnology journal · 2025Article
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3 authors.
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Abstract
Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of tissues, facilitating a comprehensive understanding of organ function and the tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. Here, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and a subspace analysis model to accurately distinguish different spatial domains by capturing the tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological image priors, preserving structural information crucial for the clustering task. Experimental results on eight ST datasets from five different platforms show that GRAS4T outperforms five state-of-the-art competing methods. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.
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