ArticleGenome biology2026
Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data.Genome biology · 2026Article
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5 authors.
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Abstract
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.
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