SpatialLeiden: spatially aware Leiden clustering
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 spa...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
07 February 2025
|
| In: |
Genome biology
Year: 2025, Jahrgang: 26, Pages: 1-8 |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-025-03489-7 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s13059-025-03489-7
|
| Verfasserangaben: | Niklas Müller-Bötticher, Shashwat Sahay, Roland Eils and Naveed Ishaque |
| Zusammenfassung: | 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. |
|---|---|
| Beschreibung: | Gesehen am 30.07.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-025-03489-7 |