ArticleNature communications2025
Learning tissue representation by identification of persistent local patterns in spatial omics data.
Article in Nature communications, 2025. 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.
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- Heterogeneity of Cancer-Associated Fibroblasts and Precision Targeting Strategies for Cancer Therapy.Thoracic cancer · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Topography-aware optimal transport for alignment of spatial omics data.Cell reports methods · 2026Article
- Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis.Nature communications · 2026Article
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- Hypergraph-driven spatial multimodal fusion for precise domain delineation and tumor microenvironment decoding.Communications biology · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Learning tissue representation by identification of persistent local patterns in spatial omics data.Nature communications · 2025Article
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Authors and funding
6 authors.
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Abstract
Spatial omics data provide rich molecular and structural information on tissues. Their analysis provides insights into local heterogeneity of tissues and holds promise to improve patient stratification by associating clinical observations with refined tissue representations. We introduce Kasumi, a method for identifying spatially localized neighborhood patterns of intra- and intercellular relationships that are persistent across samples and conditions. The tissue representation based on these patterns can facilitate translational tasks, as we show for stratification of cancer patients for disease progression and response to treatment using data from different experimental platforms. On these tasks, Kasumi outperforms related approaches and offers explanations of spatial coordination and relationships at the cell-type or marker level. We show that persistent patterns comprise regions of different sizes, and that non-abundant, localized relationships in the tissue are strongly associated with unfavorable outcomes.
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