Learning tissue representation by identification of persistent local patterns in spatial omics data
Learning tissue representation by identification of persistent local patterns in spatial omics data
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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 neighbor...
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Learning tissue representation by identification of persistent local patterns in spatial omics data
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TN_cdi_doaj_primary_oai_doaj_org_article_9c847f57e9cc4e55b6e754053b11e50d
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9c847f57e9cc4e55b6e754053b11e50d
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-025-59448-0
