Quantifying distribution shifts in single-cell data with scXMatch.

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).