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<p>Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales to thousands of genes and millions of cells, and produces signatures representing overlapping spatially-organized multicellular gene programs, allowing more biologically-complex interpretations than hard clustering methods. In benchmark spatial transcriptomics data with expert labels, versus related methods, NNMF shows excellent performance even on hard clustering tasks. On MERFISH human colorectal cancer data, NNMF identifies immunologically relevant signatures in millions of cells.</p>

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Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Ragnhild Laursen,
  • Han Chen,
  • Jack Demaray,
  • Karin Pelka,
  • Barbara E. Engelhardt

摘要

Methods for identifying complex multicellular spatial neighborhoods do not scale to existing spatial transcriptomics data, and often divide tissues into distinct neighborhoods with hard borders. We develop neighborhood NMF (NNMF) that identifies functionally coherent neighborhoods among heterogeneous cells. NNMF scales to thousands of genes and millions of cells, and produces signatures representing overlapping spatially-organized multicellular gene programs, allowing more biologically-complex interpretations than hard clustering methods. In benchmark spatial transcriptomics data with expert labels, versus related methods, NNMF shows excellent performance even on hard clustering tasks. On MERFISH human colorectal cancer data, NNMF identifies immunologically relevant signatures in millions of cells.