ArticleNature communications2025
STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal.Bioinformatics (Oxford, England) · 2026Article
- DSSMST: A Deterministic State Space Model for Self-Supervised Spatial Domain Identification in Spatial Transcriptomics.Biochemical genetics · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- SINTER3D: continuous 3D reconstruction of spatial transcriptomics via implicit neural representations.Genome biology · 2026Article
- Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Identifying batch-integrated domains from spatial transcriptomics via graph autoencoder with contrastive learning based on cross-modality and data augmentation.Briefings in bioinformatics · 2026Article
- Current and Emerging Therapies Targeting the IL-23/IL-17 Axis in Psoriasis.Biomolecules & therapeutics · 2026Review
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Spatial multi-omics integration by cross-modal graph contrastive learning.Briefings in bioinformatics · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking multi-slice integration and downstream applications in spatial transcriptomics data analysis.Genome biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it is designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrate the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.
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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.