Quantifying distribution shifts in single-cell data with scXMatch

Möller A, Schnitzerlein M, Greto E, Zaburdaev V, Uderhardt S, Blumenthal DB (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Bioinformatics Oxford University Press (OUP)

Book Volume: 42

Article Number: btag706

Journal Issue: 10

DOI: 10.1093/bioinformatics/btag706

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).

Authors with CRIS profile

Anna Möller Professur für Biomedical Network Science Miriam Schnitzerlein Department Biologie Eric Greto Department of Medicine 3 – Rheumatology and Immunology Vasily Zaburdaev
Stefan Uderhardt Professur für Integrative Gewebeimmunologie (Heisenberg-Professur) David B. Blumenthal Professur für Biomedical Network Science

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Möller, A., Schnitzerlein, M., Greto, E., Zaburdaev, V., Uderhardt, S., & Blumenthal, D.B. (2026). Quantifying distribution shifts in single-cell data with scXMatch. Bioinformatics, 42(10). https://doi.org/10.1093/bioinformatics/btag706

MLA:

Möller, Anna, et al. "Quantifying distribution shifts in single-cell data with scXMatch." Bioinformatics 42.10 (2026).

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