ArticleNature communications2024
Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Translational insights and clinical challenges of targeting cancer stem cells.Signal transduction and targeted therapy · 2026Review
- Spatial biology of crowded tumor cells: A new map for designing drug combinations.Current opinion in structural biology · 2026Review
- Spatial transcriptomic profiling of ovarian clear cell carcinoma reveals heterogeneity in OXPHOS and EMT gradients.Nature communications · 2026Article
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Review
- HarveST uses a heterogeneous graph learning framework to reveal spatial transcriptomics patterns.Communications biology · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Clinical relevance of tissue copper, selenium, and cadmium alterations in colorectal cancer.Scientific reports · 2026Article
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Imaging the fate of CAR-T cellsFrontiers in immunology · 2026Review
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
- Research on the Mechanism of "Cold Tumor" Formation and Immunotherapy for Its Transformation into "Hot Tumor".Oncology research · 2026Review
- Vector-guided graph learning for spatial multi-slice multi-omics alignment.Cell reports methods · 2025Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.Advances in neural information processing systems · 2025Article
- Spatiotemporal multi-omics analysis uncovers NAD-dependent immunosuppressive niche triggering early gastric cancer.Signal transduction and targeted therapy · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.
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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.