Learning tissue representation by identification of persistent local patterns in spatial omics data
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 Ka...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59448-0 |
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| author | Jovan Tanevski Loan Vulliard Miguel A. Ibarra-Arellano Denis Schapiro Felix J. Hartmann Julio Saez-Rodriguez |
| author_facet | Jovan Tanevski Loan Vulliard Miguel A. Ibarra-Arellano Denis Schapiro Felix J. Hartmann Julio Saez-Rodriguez |
| author_sort | Jovan Tanevski |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9c847f57e9cc4e55b6e754053b11e50d |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9c847f57e9cc4e55b6e754053b11e50d2025-08-20T02:10:49ZengNature PortfolioNature Communications2041-17232025-04-0116111510.1038/s41467-025-59448-0Learning tissue representation by identification of persistent local patterns in spatial omics dataJovan Tanevski0Loan Vulliard1Miguel A. Ibarra-Arellano2Denis Schapiro3Felix J. Hartmann4Julio Saez-Rodriguez5Institute for Computational Biomedicine, Heidelberg University and Heidelberg University HospitalInstitute for Computational Biomedicine, Heidelberg University and Heidelberg University HospitalInstitute for Computational Biomedicine, Heidelberg University and Heidelberg University HospitalInstitute for Computational Biomedicine, Heidelberg University and Heidelberg University HospitalSystems Immunology and Single-Cell Biology, German Cancer Research Center (DKFZ)Institute for Computational Biomedicine, Heidelberg University and Heidelberg University HospitalAbstract 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.https://doi.org/10.1038/s41467-025-59448-0 |
| spellingShingle | Jovan Tanevski Loan Vulliard Miguel A. Ibarra-Arellano Denis Schapiro Felix J. Hartmann Julio Saez-Rodriguez Learning tissue representation by identification of persistent local patterns in spatial omics data Nature Communications |
| title | Learning tissue representation by identification of persistent local patterns in spatial omics data |
| title_full | Learning tissue representation by identification of persistent local patterns in spatial omics data |
| title_fullStr | Learning tissue representation by identification of persistent local patterns in spatial omics data |
| title_full_unstemmed | Learning tissue representation by identification of persistent local patterns in spatial omics data |
| title_short | Learning tissue representation by identification of persistent local patterns in spatial omics data |
| title_sort | learning tissue representation by identification of persistent local patterns in spatial omics data |
| url | https://doi.org/10.1038/s41467-025-59448-0 |
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