ArticleGenome biology2026
stGCL: a versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- stGCL: a versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics.Genome biology · 2026Article
- SLGCA: spatial cross-level graph contrastive autoencoder for multislice spatial domain identification and microenvironment exploration.Briefings in bioinformatics · 2025Article
- soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.Briefings in bioinformatics · 2025Article
- spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation.Genome biology · 2025Article
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.PLoS computational biology · 2025Article
- Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks.BMC genomics · 2024Article
- stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Advances in spatial transcriptomics have enabled high-resolution mapping of tissue architecture at the molecular level, yet integrating its multi-modal data remains challenging. Here, we present stGCL, a framework for accurate and robust integration of gene expression, spatial coordinates, and histological features. stGCL employs a histology-based Vision Transformer to extract morphological features and a multi-modal graph autoencoder with contrastive learning for cross-modal fusion. In addition, we introduce a spatial coordinate correction and registration strategy to support multi-slice integration. We demonstrate that stGCL reliably identifies spatial domains, integrates vertical and horizontal tissue slices, and highlight its generalizability across platforms and resolutions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.