ArticleNucleic acids research2024
High-density generation of spatial transcriptomics with STAGE.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics.Nature communications · 2026Article
- Image-guided spatial omics enhancement reveals hidden spatial microstructures.Bioinformatics (Oxford, England) · 2026Article
- STRESS: spatial transcriptomics resolution enhancing method based on the state-space model.Briefings in bioinformatics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- SpaBiT: enhancing spatial transcriptomics resolution via bidirectional attention transformers.Bioinformatics (Oxford, England) · 2026Article
- CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.Bioinformatics (Oxford, England) · 2026Article
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- STORM: spatial transcriptomics optimization by resolution via matrix factorization.Briefings in bioinformatics · 2026Article
- UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction.bioRxiv : the preprint server for biology · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.Briefings in bioinformatics · 2024Article
- Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN.Briefings in bioinformatics · 2024Article
- STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning.Genome biology · 2024Article
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5 authors.
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Abstract
Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.
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