ArticleNucleic acids research2021
Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- SHEST: single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell-type prediction and spatial transcriptomics reconstruction.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Engineering and evaluation of precision-glycosylated clickable albumin nanoplatform for targeting the tumor microenvironment.Theranostics · 2026Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Generation of super-resolution images from barcode-based spatial transcriptomics by deep image prior.Cell reports methods · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Recent patents on anti-cancer drug discovery · 2025Review
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- GAADE: identification spatially variable genes based on adaptive graph attention network.Briefings in bioinformatics · 2024Article
- IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model.Genome biology · 2024Article
- Article
- MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks.PLoS computational biology · 2024Article
- spVC for the detection and interpretation of spatial gene expression variation.Genome biology · 2024Article
- Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewBriefings in bioinformatics · 2024Article
- PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.Nature communications · 2024Article
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3 authors.
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Abstract
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.
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