Quantifying distribution shifts in single-cell data with scXMatch

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ID: 331528
2026
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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). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W4411721707 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anna Cäcilie Möller, Miriam Schnitzerlein, Eric Greto, Vasily Zaburdaev, Stefan Uderhardt, David B. Blumenthal
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag706
URL
https://doi.org/10.1093/bioinformatics/btag706
Keywords Keywords not found

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