Quantifying distribution shifts in single-cell data with scXMatch.

scXMatch detects significant distribution shifts in single-cell data with high robustness, outperforming traditional methods across gene expression, chromatin accessibility, and morphology datasets.

• Why it matters: Accurately identifying global distribution changes in single-cell profiles is crucial for understanding biological differences, but current methods lack statistical rigor and are often unreliable.
• What they did: The authors developed scXMatch, a non-parametric, graph-based statistical test that quantifies distribution shifts using appropriate distance measures, evaluated on multiple single-cell data types.
• The result: scXMatch provides a principled, stable alternative to manual workflows, enabling more reliable detection of distribution changes and setting a new standard in single-cell analysis.