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<p>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.</p>

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SpatialLeiden: spatially aware Leiden clustering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月7日

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  • Niklas Müller-Bötticher,
  • Shashwat Sahay,
  • Roland Eils,
  • Naveed Ishaque

摘要

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.