ArticleBriefings in bioinformatics2024
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.Bioinformatics (Oxford, England) · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- SpaBatch: Deep Learning-Based Cross-Slice Integration and 3D Spatial Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 2025Article
- Improving cell-type composition inference in spatial transcriptomics with SpaDAMA.PLoS computational biology · 2025Article
- SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering.Briefings in bioinformatics · 2025Article
- m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spatial transcriptomics (ST technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization, and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer and Graph Convolutional Network that integrates histological images, spot-based ST data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately and aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Our source code and all used datasets are available at https://github.com/wenwenmin/scstGCN and https://zenodo.org/records/12800375.
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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.