{
  "abstract": "As spatial molecular data grow in scope, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method combining deep learning with principled statistics to discover disease-associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to summarize the contents of every small tissue patch in a dataset via numeric ‘fingerprints’. It uses these to define many data-dependent, overlapping ‘microniches’ and meta-analyzes them to identify microniches whose abundance correlates significantly with case−control status. We confirm VIMA’s calibration, power and spatial accuracy in simulations. We then apply VIMA to spatial datasets spanning three diseases and spatial modalities, recapitulating known biology and identifying new spatial features of disease.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA USA",
    "Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA",
    "Broad Institute of MIT and Harvard, Cambridge, MA USA",
    "Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA",
    "Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA",
    "NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and Department of Inflammation and Ageing, College of Medicine & Health, University of Birmingham, Birmingham, UK",
    "Birmingham Tissue Analytics, College of Medicine and Health, University of Birmingham, Birmingham, UK",
    "University of Colorado Anschutz Medical Campus, Division of Rheumatology, Aurora, CO USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Yakir A. Reshef",
      "orcid": "0000-0001-6463-4203"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Lakshay Sood"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Michelle Curtis",
      "orcid": "0000-0002-6843-4839"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Laurie Rumker"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Daniel J. Stein",
      "orcid": "0000-0002-9442-2604"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Mukta G. Palshikar"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Saba Nayar"
    },
    {
      "affs": [
        5
      ],
      "name": "Andrew Filer"
    },
    {
      "affs": [
        7
      ],
      "name": "Anna Helena Jonsson"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Ilya Korsunsky",
      "orcid": "0000-0003-4848-3948"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4
      ],
      "name": "Soumya Raychaudhuri",
      "orcid": "0000-0002-1901-8265"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/474rsk3qh2hh5.bib",
  "cited_by": 1,
  "doi": "10.1038/s41592-026-03236-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736648/",
  "id": "474rsk3qh2hh5",
  "issns": [
    "1548-7091",
    "1548-7105"
  ],
  "landing_url": "https://doi.org/10.1038/s41592-026-03236-1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7220679039",
  "pdf_url": "https://www.nature.com/articles/s41592-026-03236-1.pdf",
  "pmcid": "PMC13736648",
  "pmid": "42839109",
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/474rsk3qh2hh5.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:03Z",
      "native_id": "PMC13736648",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41592-026-03236-1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Accurate and well-powered case–control analysis of spatial molecular data",
  "title_text": "Accurate and well-powered case–control analysis of spatial molecular data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:03Z",
  "url": "https://research.draconitic.com/paper/474rsk3qh2hh5",
  "venue": "Nature Methods",
  "versions": []
}