SpatialLeiden: spatially aware Leiden clustering
Abstract Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms of choice in the single-cell community. In the fie...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03489-7 |
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| _version_ | 1823862197246230528 |
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| author | Niklas Müller-Bötticher Shashwat Sahay Roland Eils Naveed Ishaque |
| author_facet | Niklas Müller-Bötticher Shashwat Sahay Roland Eils Naveed Ishaque |
| author_sort | Niklas Müller-Bötticher |
| collection | DOAJ |
| description | Abstract Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms of choice in the single-cell community. In the field of spatial omics, Leiden is often categorized as a “non-spatial” clustering method. However, we show that by integrating spatial information at various steps Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art spatial clustering algorithms. |
| format | Article |
| id | doaj-art-8ad0e99b26a24fa1a46d019f1167098c |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-8ad0e99b26a24fa1a46d019f1167098c2025-02-09T12:39:25ZengBMCGenome Biology1474-760X2025-02-012611810.1186/s13059-025-03489-7SpatialLeiden: spatially aware Leiden clusteringNiklas Müller-Bötticher0Shashwat Sahay1Roland Eils2Naveed Ishaque3Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital HealthBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital HealthBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital HealthBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Digital HealthAbstract Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms of choice in the single-cell community. In the field of spatial omics, Leiden is often categorized as a “non-spatial” clustering method. However, we show that by integrating spatial information at various steps Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art spatial clustering algorithms.https://doi.org/10.1186/s13059-025-03489-7Spatial omicsClusteringLeidenDomainsNichesSpatial clustering |
| spellingShingle | Niklas Müller-Bötticher Shashwat Sahay Roland Eils Naveed Ishaque SpatialLeiden: spatially aware Leiden clustering Genome Biology Spatial omics Clustering Leiden Domains Niches Spatial clustering |
| title | SpatialLeiden: spatially aware Leiden clustering |
| title_full | SpatialLeiden: spatially aware Leiden clustering |
| title_fullStr | SpatialLeiden: spatially aware Leiden clustering |
| title_full_unstemmed | SpatialLeiden: spatially aware Leiden clustering |
| title_short | SpatialLeiden: spatially aware Leiden clustering |
| title_sort | spatialleiden spatially aware leiden clustering |
| topic | Spatial omics Clustering Leiden Domains Niches Spatial clustering |
| url | https://doi.org/10.1186/s13059-025-03489-7 |
| work_keys_str_mv | AT niklasmullerbotticher spatialleidenspatiallyawareleidenclustering AT shashwatsahay spatialleidenspatiallyawareleidenclustering AT rolandeils spatialleidenspatiallyawareleidenclustering AT naveedishaque spatialleidenspatiallyawareleidenclustering |