CytoNormPy Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets

Exner T, Hackert N, Leomazzi L, Van Gassen S, Saeys Y, Lorenz HM, Grieshaber-Bouyer R (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Cytometry. Part A : the journal of the International Society for Analytical Cytology

DOI: 10.1002/cyto.a.24953

Abstract

Cytometry has evolved as a crucial technique in clinical diagnostics, clinical studies, and research. However, batch effects due to technical variation complicate the analysis of cytometry data in clinical and fundamental research settings and have to be accounted for. Here, we present a Python implementation of the widely used CytoNorm algorithm for the removal of batch effects, implementing the complete feature set of the recently published CytoNorm 2.0. Our implementation ran up to 85% faster than its R counterpart while being fully compatible with common single-cell data structures and frameworks of Python. We extend the previous functionality by adding common clustering algorithms and provide key visualizations of the algorithm and its evaluation. The CytoNormPy implementation is freely available on GitHub: https://github.com/TarikExner/CytoNormPy.

Authors with CRIS profile

Ricardo Grieshaber Bouyer

Involved external institutions

Flanders Institute for Biotechnology / Vlaams Instituut voor Biotechnologie (VIB) BE Belgium (BE) Universitätsklinikum Heidelberg DE Germany (DE)

How to cite

APA:

Exner, T., Hackert, N., Leomazzi, L., Van Gassen, S., Saeys, Y., Lorenz, H.M., & Grieshaber-Bouyer, R. (2025). CytoNormPy Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets. Cytometry. Part A : the journal of the International Society for Analytical Cytology. https://doi.org/10.1002/cyto.a.24953

MLA:

Exner, Tarik, et al. "CytoNormPy Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets." Cytometry. Part A : the journal of the International Society for Analytical Cytology (2025).

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