ArticleResearch (Washington, D.C.)2023
Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Combining Spatial Multi-Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self-Supervised Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Evolution of Cancer Metastases via Lineage Trans-Differentiation.Research (Washington, D.C.) · 2026Article
- A Single-Cell Atlas of Pan-Cancer Liver Metastasis Reveals Dynamic Cellular Programs Driving Metastatic Progression and Immune Modulation.Research (Washington, D.C.) · 2026Article
- Methyltransferase-Like 3 Inhibition-Activated cGAS/STING Axis Enhances Immunotherapy and Poly(ADP-Ribose) Polymerase Inhibitor Sensitivity in Lung Adenocarcinoma.Research (Washington, D.C.) · 2026Article
- Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning.Communications biology · 2025Article
- MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.Genome medicine · 2025Article
- Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.Research (Washington, D.C.) · 2025Article
- Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.Research (Washington, D.C.) · 2025Review
- Integrative single-cell and spatial transcriptomics uncover ELK4-mediated mechanisms inFrontiers in immunology · 2025Article
- Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).Research (Washington, D.C.) · 2024Article
- eMCI: An Explainable Multimodal Correlation Integration Model for Unveiling Spatial Transcriptomics and Intercellular Signaling.Research (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially resolved transcriptomics (SRT) is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition. To fully understand the organizational complexity and tumor immune escape mechanism, we propose stMGATF, a multiview graph attention fusion model that integrates gene expression, histological images, spatial location, and gene association. To better extract information, stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view. A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation. Applied to diverse SRT datasets, stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms. In particular, stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains. Importantly, considering the associations between genes in tumors, stMGATF can identify the spatial dark genes ignored by traditional methods, which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms, providing theoretical evidence for the development of new immunotherapeutic strategies.
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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.