ArticleGenome biology2024
STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Predicting fine-grained cell types from histology images through cross-modal learning in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression.Briefings in bioinformatics · 2024Article
- STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning.Genome biology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.
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Registered trials
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