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SpatialLeiden: spatially aware Leiden clustering
by
Müller-Bötticher, Niklas
, Ishaque, Naveed
, Eils, Roland
, Sahay, Shashwat
in
Algorithms
/ Animal Genetics and Genomics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Brief Report
/ Cluster Analysis
/ Clustering
/ Datasets
/ Domains
/ Evolutionary Biology
/ Gene expression
/ genome
/ Human Genetics
/ Humans
/ Leiden
/ Life Sciences
/ Microbial Genetics and Genomics
/ Neighborhoods
/ Niches
/ Performance evaluation
/ phenotype
/ Phenotypes
/ Plant Genetics and Genomics
/ Single-Cell Analysis - methods
/ Spatial clustering
/ spatial data
/ Spatial omics
2025
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SpatialLeiden: spatially aware Leiden clustering
by
Müller-Bötticher, Niklas
, Ishaque, Naveed
, Eils, Roland
, Sahay, Shashwat
in
Algorithms
/ Animal Genetics and Genomics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Brief Report
/ Cluster Analysis
/ Clustering
/ Datasets
/ Domains
/ Evolutionary Biology
/ Gene expression
/ genome
/ Human Genetics
/ Humans
/ Leiden
/ Life Sciences
/ Microbial Genetics and Genomics
/ Neighborhoods
/ Niches
/ Performance evaluation
/ phenotype
/ Phenotypes
/ Plant Genetics and Genomics
/ Single-Cell Analysis - methods
/ Spatial clustering
/ spatial data
/ Spatial omics
2025
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Do you wish to request the book?
SpatialLeiden: spatially aware Leiden clustering
by
Müller-Bötticher, Niklas
, Ishaque, Naveed
, Eils, Roland
, Sahay, Shashwat
in
Algorithms
/ Animal Genetics and Genomics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Brief Report
/ Cluster Analysis
/ Clustering
/ Datasets
/ Domains
/ Evolutionary Biology
/ Gene expression
/ genome
/ Human Genetics
/ Humans
/ Leiden
/ Life Sciences
/ Microbial Genetics and Genomics
/ Neighborhoods
/ Niches
/ Performance evaluation
/ phenotype
/ Phenotypes
/ Plant Genetics and Genomics
/ Single-Cell Analysis - methods
/ Spatial clustering
/ spatial data
/ Spatial omics
2025
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Journal Article
SpatialLeiden: spatially aware Leiden clustering
2025
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Overview
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.
Publisher
BioMed Central,Springer Nature B.V,BMC
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