ArticleNature communications2023
SiGra: single-cell spatial elucidation through an image-augmented graph transformer.
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 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.Genome biology · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.Bioinformatics (Oxford, England) · 2026Article
- PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.Briefings in bioinformatics · 2026Article
- An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data.PLoS computational biology · 2026Article
- Spatial omics: applications and utility in profiling the tumor microenvironment.Cancer metastasis reviews · 2025Review
- Cracking PRMT5: Mechanistic insights, clinical advances, and AI-driven strategies.Cancer letters · 2025Review
- Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- SpaTM: topic models for inferring spatially informed transcriptional programs.Briefings in bioinformatics · 2025Article
- Spatial transcriptomic data denoising and domain identification by a community strength-augmented graph autoencoder.Briefings in bioinformatics · 2025Article
- Spatial transcriptomics: a bibliometric analysis with large language model on English literatures.Briefings in bioinformatics · 2025Article
- SpaIM: single-cell spatial transcriptomics imputation via style transfer.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.
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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.