ArticleNature communications2023
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 419 papers, 1 of them a synthesis that pooled it.
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Who cites it
419 citing papers in PubMed, 1 synthesis or guideline pooled it, 521 citations in OpenAlex.
- Enhancing RNA Capture Efficiency in Spatial Transcriptomics: A Review of Innovative Technologies and Strategies.International journal of molecular sciences · 2025Pooled it
- Spatial transcriptomics profiling across tissue boundaries in the developing mouse brain.iScience · 2026Article
- SpatialZoomer: multi-scale feature analysis of spatial transcriptomics.Nature biomedical engineering · 2026Article
- Spatially resolved multiomics of human meningeal development reveal lineage and disease dynamics.Nature cell biology · 2026Article
- Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks.Nature communications · 2026Article
- Pan-tumor spatial transcriptomics reveals conserved properties of tumor organization.Cell reports. Medicine · 2026Article
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Review
- Spatial transcriptomics reveals laminar and cell-type-specific A-to-I RNA editing signatures in the macaque cortex.Nature communications · 2026Article
- Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.Briefings in bioinformatics · 2026Article
- DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.Briefings in bioinformatics · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- Advances in single-cell and spatial multi-omics for plant development and stress responses.Plant diversity · 2026Review
- A multiperspective evaluation framework of spatial transcriptomics clustering methods.NAR genomics and bioinformatics · 2026Article
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome-proteome data.Bioinformatics (Oxford, England) · 2026Article
- SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
359 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.
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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.