ArticleNature cell biology2026
The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.
Article in Nature cell biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.Bioinformatics (Oxford, England) · 2026Article
- SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement.Scientific reports · 2026Article
- Spatial-aware detection of copy number alterations from spatial transcriptomics using SpaCNA.Nature communications · 2026Article
- SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data.Genome biology · 2026Article
- A lightweight deep learning architecture for automatic shrimp disease classification.Scientific reports · 2026Article
- The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.Nature cell biology · 2026Article
- Neuroimmune regulation of post-traumatic bone regeneration: focus on inflammatory switching and functional recovery.Frontiers in immunology · 2026Review
- Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.Bioinformatics advances · 2026Article
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12 authors.
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Abstract
Spatial transcriptomics (ST) technologies revolutionized tissue architecture studies by capturing gene expression with spatial context. However, high-dimensional ST data often have limited spatial resolution and exhibit considerable noise and sparsity, posing substantial challenges in deciphering subtle spatial structures and underlying biological activities. Here we introduce 'spatial high-definition embedding mapping' (SpaHDmap), an interpretable dimension reduction framework that enhances spatial resolution by integrating ST gene expression with high-resolution histology images. SpaHDmap incorporates non-negative matrix factorization into a deep learning framework, enabling the identification of high-resolution spatial metagenes (embeddings). Furthermore, SpaHDmap can simultaneously analyse multiple samples and is compatible with various types of histology images. Extensive evaluations on synthetic, public and newly sequenced ST datasets from various technologies and tissue types demonstrate that SpaHDmap can effectively produce high-resolution spatial metagenes, and detect refined spatial structures. SpaHDmap represents a powerful approach for integrating ST data and histology images, offering deeper insights into complex tissue structures and functions.
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