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 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.
Authors
- Shashwat Sahay (ORCID: https://orcid.org/0000-0002-8533-6149)
- Roland Eils (ORCID: https://orcid.org/0000-0002-0034-4036)
- Naveed Ishaque (ORCID: https://orcid.org/0000-0002-8426-901X)
- Niklas Müller‐Bötticher (ORCID: https://orcid.org/0000-0001-5103-7282)
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Freie Universität Berlin (DE)
Publication Details
- Journal
- Genome biology
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1186/s13059-025-03489-7
- Citations
- 8
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 6.45
Funders
- Bundesministerium für Bildung und Forschung
- Charité – Universitätsmedizin Berlin