ArticleBriefings in bioinformatics2024
A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.Briefings in bioinformatics · 2026Article
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- Diffusion-based representation integration for foundation models improves spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- MGCL-ST: multi-view graph contrastive learning for spatial transcriptomics imputation.Genome medicine · 2026Article
- GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2026Article
- Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- SGCD: High-Resolution Spatial Domain Characterization via Data Interpolation and Cell-Type Deconvolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data.Briefings in bioinformatics · 2025Article
- spCLUE: a contrastive learning approach to unified spatial transcriptomics analysis across single-slice and multi-slice data.Genome biology · 2025Article
- STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.Nature communications · 2025Article
- Article
- A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.Bioinformatics (Oxford, England) · 2024Article
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3 authors.
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Abstract
Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the spatial pattern of gene expression and the spatial architecture of cell types. Much work has been done to exploit the genomic spatial architectures of cells. Such work is based on the common assumption that gene expression profiles of spatially adjacent spots are more similar than those of more distant spots. However, related work might not consider the nonlocal spatial co-expression dependency, which can better characterize the tissue architectures. Therefore, we propose MuCoST, a Multi-view graph Contrastive learning framework for deciphering complex Spatially resolved Transcriptomic architectures with dual scale structural dependency. To achieve this, we employ spot dependency augmentation by fusing gene expression correlation and spatial location proximity, thereby enabling MuCoST to model both nonlocal spatial co-expression dependency and spatially adjacent dependency. We benchmark MuCoST on four datasets, and we compare it with other state-of-the-art spatial domain identification methods. We demonstrate that MuCoST achieves the highest accuracy on spatial domain identification from various datasets. In particular, MuCoST accurately deciphers subtle biological textures and elaborates the variation of spatially functional patterns.
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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.