ArticleBriefings in bioinformatics2025
SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- ProST: an image prompt-guided multimodal representation learning framework for spatial domain identification.Bioinformatics (Oxford, England) · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Representation learning for multi-modal spatially resolved transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.Briefings in bioinformatics · 2026Article
- So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.Nucleic acids research · 2026Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
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2 authors.
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Abstract
Spatial transcriptomics, by capturing both gene expression and spatial information, holds great promise for unraveling the complex organization of tissues. In this study, we introduce SpaICL, an image-guided curriculum strategy-based graph contrastive learning framework for spatial transcriptomics clustering. SpaICL integrates gene expression, spatial coordinates, and histological image features to construct a low-dimensional latent representation that enhances the de-lineation of spatial functional domains. The model employs a complementary masking strategy and a shared graph neural network encoder to generate dual embeddings, while a dual cross-attention mechanism aligns local and global features across multiple modalities. Additionally, the curriculum learning module further facilitates the gradual integration of neighborhood information, effectively mitigating the over-smoothing issues associated with fixed adjacency matrices. We evaluated the performance of SpaICL on five benchmark spatial transcriptomics datasets, achieving superior results compared to existing baseline methods. Moreover, SpaICL demonstrates significant potential in downstream analytical applications. The code of SpaICL is available at https://github.com/wenwenmin/SpaICL.
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