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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  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