<scp>CytoNormPy</scp> Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets

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.

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Publication Details

Journal
Cytometry Part A
Published
2025-08-29
DOI
https://doi.org/10.1002/cyto.a.24953
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Yvan Saeys, Nicolaj Hackert, Ricardo Grieshaber‐Bouyer, Hanns‐Martin Lorenz et al.
Cytometry Part A
Single-cell and spatial transcriptomics
article

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

Yvan Saeys, Nicolaj Hackert, Ricardo Grieshaber‐Bouyer, Hanns‐Martin Lorenz, Tarik Exner, Sofie Van Gassen, Luca Leomazzi
article en

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.

Cytometry Part AVol. 107(9)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), Ghent University (BE), Universitätsklinikum Erlangen (DE), VIB-UGent Center for Inflammation Research (BE), Comprehensive Cancer Center Erlangen (DE)
European Commission, Deutsche Forschungsgemeinschaft, Else Kröner-Fresenius-Stiftung, Fonds Wetenschappelijk Onderzoek, Vlaamse regering
Openalex Percentile: Top 80%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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<scp>CytoNormPy</scp> Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets — Yvan Saeys, Nicolaj Hackert, et al. · Cytometry Part A (2025) | TGRS Research Map | TGRS