ArticleNature communications2022
Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- 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
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Glycolysis-driven cancer-associated fibroblasts shape T-Cell exclusion via the CXCL16-CXCR6 Axis: a metabolic-chemokine mechanism in tumor immunity.Journal of translational medicine · 2026Review
- Alignment of spatial transcriptomics slices across diseases, platforms and conditions.Genome medicine · 2026Article
- stGCL: a versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics.Genome biology · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy.Cancer drug resistance (Alhambra, Calif.) · 2026Review
- Research on the Mechanism of "Cold Tumor" Formation and Immunotherapy for Its Transformation into "Hot Tumor".Oncology research · 2026Review
- Computational decoding of cell-cycle phase effects on cancer hallmarks across breast cancer subtypes.Breast cancer research : BCR · 2025Article
- Vector-guided graph learning for spatial multi-slice multi-omics alignment.Cell reports methods · 2025Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Next generation sequencing and beyond: a review of genomic sequencing methods.Functional & integrative genomics · 2025Article
- Spatiotemporal multi-omics analysis uncovers NAD-dependent immunosuppressive niche triggering early gastric cancer.Signal transduction and targeted therapy · 2025Article
- SpatialCVGAE: Consensus Clustering Improves Spatial Domain Identification of Spatial Transcriptomics Using VGAE.Interdisciplinary sciences, computational life sciences · 2025Article
- SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data.Cell genomics · 2025Article
- Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.
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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.