{
  "abstract": "Spatial transcriptomics enables direct profiling of cellular organization within intact tissues, yet two challenges limit its full exploitation: accurate reference-free deconvolution of mixed expression signals (including species-admixed models such as xenografts), and lack of tools that can translate per-spot program usage values into spatial niches. We developed spOT-NMF, an optimal transport-based non-negative matrix factorization method that replaces standard reconstruction loss with the Wasserstein distance to disentangle mixed signals without requiring matched single-cell atlases. While the generalized OT-NMF factorization is applicable to any expression matrix, a proximity-based niche inference module further translates program co-usage from spatial transcriptomes into directional co-occurrence networks that define tissue compartments and their boundaries. Benchmarking across five gold-standard datasets spanning sub-cellular to multi-cellular resolutions, multiple platforms, and two-species admixture scenarios, spOT-NMF ranks first among reference-free methods, with particular advantage under the signal imbalance conditions characteristic of xenografts. We show that reference-free deconvolution recovers cell populations absent from matched single-cell atlases, highlighting the value of unsupervised analysis. Applied to glioblastoma xenografts and human colorectal cancer, spOT-NMF reveals spatially restricted interferon signaling, invasion routes, and CAF–tumor boundaries. spOT-NMF is available as an open-source Python package with GPU, CPU, and HPC support.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Aly O Abdelkareem",
      "orcid": "0000-0002-2203-9577"
    },
    {
      "name": "Gurveer S Gill",
      "orcid": "0009-0004-6860-6233"
    },
    {
      "name": "Varsha Thoppey Manoharan",
      "orcid": "0009-0009-6547-2221"
    },
    {
      "name": "Theodore B Verhey",
      "orcid": "0000-0002-4292-0839"
    },
    {
      "name": "A Sorana Morrissy",
      "orcid": "0000-0001-8526-562X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9xtpyxqfwr4ep.bib",
  "cited_by": 1,
  "doi": "10.1093/bib/bbag606",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13738212/",
  "id": "9xtpyxqfwr4ep",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://doi.org/10.1093/bib/bbag606",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Problem Solving Protocol"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W4412867617",
  "pdf_url": "https://academic.oup.com/bib/article-pdf/27/6/bbag606/73132120/bbag606.pdf",
  "pmcid": "PMC13738212",
  "pmid": "42986684",
  "published": "2026-11-05",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/9xtpyxqfwr4ep.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:23:14Z",
      "native_id": "PMC13738212",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bib/bbag606"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "spOT-NMF: gene expression deconvolution via optimal transport with proximity-aware niche mapping for spatial transcriptomics",
  "title_text": "spOT-NMF: gene expression deconvolution via optimal transport with proximity-aware niche mapping for spatial transcriptomics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:23:14Z",
  "url": "https://research.draconitic.com/paper/9xtpyxqfwr4ep",
  "venue": "Briefings in Bioinformatics",
  "versions": []
}