CytoNorm 2.0 : a flexible normalization framework for cytometry data without requiring dedicated controls
- Author
- Katrien Quintelier (UGent) , Marcella Willemsen, Victor Bosteels, Joachim G. J. V. Aerts, Yvan Saeys (UGent) and Sofie Van Gassen (UGent)
- Organization
- Project
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- Computational cytometry techniques for clinical applications
- Unravelling cellular heterogeneity and dynamics in normal and malignant hematopoiesis using singlecell bioinformatics
- ProFILE - Standardized multi-parameter flow cytometry as tool for early diagnosis, risk assessment and appropriate treatment in primary immunodefiency
- Abstract
- Cytometry is a single cell, high-dimensional, high-throughput technique that is being applied across a range of disciplines. However, many elements alongside the data acquisition process might give rise to technical variation in the dataset, called batch effects. CytoNorm is a normalization algorithm for batch effect removal in cytometry data that was originally published in 2020 and has been applied on a variety of datasets since then. Here, we present CytoNorm 2.0, discussing new, illustrative use cases to increase the applicability of the algorithm and showcasing new visualizations that enable thorough quality control and understanding of the normalization process. We explain how CytoNorm can be used without the need for technical replicates or controls, show how the goal distribution can be tailored toward the experimental design and we elaborate on the choice of markers for CytoNorm's internal FlowSOM clustering step.
- Keywords
- batch effects, CytoNorm, data integration, normalization, quality control, FLOW-CYTOMETRY
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JM217BGB52EPSYZAKTMWBN3M
- MLA
- Quintelier, Katrien, et al. “CytoNorm 2.0 : A Flexible Normalization Framework for Cytometry Data without Requiring Dedicated Controls.” CYTOMETRY PART A, vol. 107, no. 2, 2025, pp. 69–87, doi:10.1002/cyto.a.24910.
- APA
- Quintelier, K., Willemsen, M., Bosteels, V., Aerts, J. G. J. V., Saeys, Y., & Van Gassen, S. (2025). CytoNorm 2.0 : a flexible normalization framework for cytometry data without requiring dedicated controls. CYTOMETRY PART A, 107(2), 69–87. https://doi.org/10.1002/cyto.a.24910
- Chicago author-date
- Quintelier, Katrien, Marcella Willemsen, Victor Bosteels, Joachim G. J. V. Aerts, Yvan Saeys, and Sofie Van Gassen. 2025. “CytoNorm 2.0 : A Flexible Normalization Framework for Cytometry Data without Requiring Dedicated Controls.” CYTOMETRY PART A 107 (2): 69–87. https://doi.org/10.1002/cyto.a.24910.
- Chicago author-date (all authors)
- Quintelier, Katrien, Marcella Willemsen, Victor Bosteels, Joachim G. J. V. Aerts, Yvan Saeys, and Sofie Van Gassen. 2025. “CytoNorm 2.0 : A Flexible Normalization Framework for Cytometry Data without Requiring Dedicated Controls.” CYTOMETRY PART A 107 (2): 69–87. doi:10.1002/cyto.a.24910.
- Vancouver
- 1.Quintelier K, Willemsen M, Bosteels V, Aerts JGJV, Saeys Y, Van Gassen S. CytoNorm 2.0 : a flexible normalization framework for cytometry data without requiring dedicated controls. CYTOMETRY PART A. 2025;107(2):69–87.
- IEEE
- [1]K. Quintelier, M. Willemsen, V. Bosteels, J. G. J. V. Aerts, Y. Saeys, and S. Van Gassen, “CytoNorm 2.0 : a flexible normalization framework for cytometry data without requiring dedicated controls,” CYTOMETRY PART A, vol. 107, no. 2, pp. 69–87, 2025.
@article{01JM217BGB52EPSYZAKTMWBN3M,
abstract = {{Cytometry is a single cell, high-dimensional, high-throughput technique that is being applied across a range of disciplines. However, many elements alongside the data acquisition process might give rise to technical variation in the dataset, called batch effects. CytoNorm is a normalization algorithm for batch effect removal in cytometry data that was originally published in 2020 and has been applied on a variety of datasets since then. Here, we present CytoNorm 2.0, discussing new, illustrative use cases to increase the applicability of the algorithm and showcasing new visualizations that enable thorough quality control and understanding of the normalization process. We explain how CytoNorm can be used without the need for technical replicates or controls, show how the goal distribution can be tailored toward the experimental design and we elaborate on the choice of markers for CytoNorm's internal FlowSOM clustering step.}},
author = {{Quintelier, Katrien and Willemsen, Marcella and Bosteels, Victor and Aerts, Joachim G. J. V. and Saeys, Yvan and Van Gassen, Sofie}},
issn = {{1552-4922}},
journal = {{CYTOMETRY PART A}},
keywords = {{batch effects,CytoNorm,data integration,normalization,quality control,FLOW-CYTOMETRY}},
language = {{eng}},
number = {{2}},
pages = {{69--87}},
title = {{CytoNorm 2.0 : a flexible normalization framework for cytometry data without requiring dedicated controls}},
url = {{http://doi.org/10.1002/cyto.a.24910}},
volume = {{107}},
year = {{2025}},
}
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