{
  "abstract": "Motivation: A basic task that frequently arises when analyzing single-cell data is to assess if there is a global distribution shift between the data profiles of cells from two different conditions. Widely used approaches to address this task such as visual inspection of two-dimensional representations or clustering-based workflows lack a solid statistical underpinning and are notoriously unstable and prone to confirmation bias.\n\nResults: To promote more rigorous analysis, we present scXMatch. scXMatch is based on a non-parametric graph-based test to quantify distribution shifts in arbitrary data spaces for which a suitable distance measure is available. We evaluated scXMatch on single-cell gene expression, chromatin accessibility, and imaging-derived cell morphology data, showing that it can robustly detect distribution shifts for different types of single-cell data. scXMatch thus aims to set a new standard in the single-cell biology field, replacing easy-to-manipulate semi-manual distribution shift quantification workflows by principled statistical testing.\n\nAvailability and implementation: Python source code of scXMatch is available at https://github.com/bionetslab/scxmatch, a packaged version at https://anaconda.org/bioconda/scxmatch, and scripts to reproduce our results at https://github.com/bionetslab/scXMatch_paper (stable releases at https://doi.org/10.5281/zenodo.20665675).",
  "abstract_shown": true,
  "affiliations": [
    "Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91052,",
    "Department of Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen , Erlangen, 91054,",
    "Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91058,",
    "Max-Planck-Zentrum für Physik und Medizin , Erlangen, 91058,",
    "Max Planck Institute for the Science of Light , Erlangen, 91058,",
    "Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91054,",
    "Exploratory Research Unit, Optical Imaging Competence Center Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91058,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Anna Möller",
      "orcid": "0000-0001-7517-8549"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Miriam Schnitzerlein",
      "orcid": "0009-0002-6609-4479"
    },
    {
      "affs": [
        1,
        5,
        6
      ],
      "name": "Eric Greto"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Vasily Zaburdaev",
      "orcid": "0000-0001-8234-3442"
    },
    {
      "affs": [
        1,
        5,
        6
      ],
      "name": "Stefan Uderhardt",
      "orcid": "0000-0002-2489-2109"
    },
    {
      "affs": [
        0
      ],
      "name": "David B Blumenthal",
      "orcid": "0000-0001-8651-750X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/983j8z3shwm2e.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag706",
  "fulltext_url": null,
  "id": "983j8z3shwm2e",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag706/8860685",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W4411721707",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag706/71581559/btag706.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/983j8z3shwm2e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag706",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag706/8860685"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Quantifying distribution shifts in single-cell data with scXMatch",
  "title_text": "Quantifying distribution shifts in single-cell data with scXMatch",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/983j8z3shwm2e",
  "venue": "Bioinformatics",
  "versions": []
}