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SpatialLeiden: spatially aware Leiden clustering

Niklas Müller‐Bötticher, Shashwat Sahay, Roland Eils, Naveed Ishaque

2025Genome biology15 citationsDOIOpen Access PDF

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.

Topics & Concepts

Cluster analysisBiologySpatial analysisClustering high-dimensional dataComputational biologyComputer scienceData miningArtificial intelligenceMathematicsStatisticsSingle-cell and spatial transcriptomicsGene Regulatory Network AnalysisCell Image Analysis Techniques
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