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Peer-reviewedCC BY 4.0Bioinformatics

Quantifying distribution shifts in single-cell data with scXMatch

  • Anna Möller1,2
  • Miriam Schnitzerlein3,4,5
  • Eric Greto2,6,7
  • Vasily Zaburdaev3,4
  • Stefan Uderhardt2,6,7
  • David B Blumenthal1
7 affiliations
  1. 1Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91052,
  2. 2Department of Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen , Erlangen, 91054,
  3. 3Department of Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91058,
  4. 4Max-Planck-Zentrum für Physik und Medizin , Erlangen, 91058,
  5. 5Max Planck Institute for the Science of Light , Erlangen, 91058,
  6. 6Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91054,
  7. 7Exploratory Research Unit, Optical Imaging Competence Center Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91058,
Open at sourcePDFDOICite

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.

Results: 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.

Availability 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).

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Cite

BibTeXRIS
BibTeX
@article{moller2026quantifying,
  title = {{Quantifying distribution shifts in single-cell data with scXMatch}},
  author = {Anna Möller and Miriam Schnitzerlein and Eric Greto and Vasily Zaburdaev and Stefan Uderhardt and David B Blumenthal},
  journal = {Bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag706},
  url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag706/8860685},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Quantifying distribution shifts in single-cell data with scXMatch
AU  - Anna Möller
AU  - Miriam Schnitzerlein
AU  - Eric Greto
AU  - Vasily Zaburdaev
AU  - Stefan Uderhardt
AU  - David B Blumenthal
PY  - 2026
DA  - 2026/10/03
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btag706
UR  - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag706/8860685
AB  - 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.  Results: 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.  Availability 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).
SN  - 1367-4803
SN  - 1367-4811
ER  -