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Peer-reviewedCC BYBioinformatics (Oxford, England)

Seumetry: a versatile and comprehensive R toolkit to accelerate high-dimensional flow and mass cytometry data analysis.

  • Malte Borggrewe1,2,3,4
  • Markus Flosbach1,2,4
  • Lucia Testa1,2,3,4
  • Cedric Ly3,4,5
  • Martin Baumdick1,2,4
  • Immo Prinz4,5
  • Hamburg Intestinal Tissue Study Group
  • Elena Billeb1,2,4
  • Stefan Bonn3,4,6
  • Madeleine J Bunders1,2,4,6
6 affiliations
  1. 1III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  2. 2Department of Virus Immunology, Leibniz Institute of Virology, Hamburg, Germany.
  3. 3Institute of Medical Systems Biology, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg Eppendorf, Hamburg, Germany.
  4. 4Hamburg Center for Translational Immunology, University Medical Center Hamburg Eppendorf, Hamburg, Germany.
  5. 5Institute of Systems Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  6. 6German Center for Child and Adolescent Health (DZKJ), partner site Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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Abstract

Motivation

Recent progress in flow and mass cytometry technologies enables the simultaneous measurement of over 50 parameters for an individual cell. The resulting increase in data volume and complexity present challenges for conventional analysis methods, which are based on manual gating, are time-consuming and fail to capture unknown or minor cell populations. Advances in single-cell RNA sequencing (scRNAseq) technologies have prompted the development of computational analysis tools designed to process and analyze high-dimensional biological data. Building on these advances, we here present Seumetry, a framework that combines flow and mass cytometry data-specific analysis methods with the capabilities of Seurat, a powerful tool for the analysis of scRNAseq data.Results

Seumetry offers methods for advanced quality control, data visualizations, and differential population abundance and protein expression analysis. We tested Seumetry on an in-house generated complex dataset of immune cells from different layers of human intestines, demonstrating that Seumetry accurately identifies distinct immune cell populations. Furthermore, benchmarking against public datasets showed that Seumetry aligns with manual-gated results, further validating its use for high-dimensional flow and mass cytometry data. In summary, Seumetry provides a new scalable framework for the comprehensive analysis of high-dimensional cytometry data with seamless integration into commonly used scRNAseq analysis tools, enabling in-depth analysis methods to facilitate biological interpretations.Availability

Seumetry code is available open source under an MIT license and the current version can be viewed and used via Github: https://github.com/imsb-uke/Seumetry; and archived on Zenodo: https://doi.org/10.5281/zenodo.21216681. Flow cytometry data generated for this manuscript is available via Zenodo: https://doi.org/10.5281/zenodo.11935872.

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Cite

BibTeXRIS
BibTeX
@article{borggrewe2026seumetry,
  title = {{Seumetry: a versatile and comprehensive R toolkit to accelerate high-dimensional flow and mass cytometry data analysis.}},
  author = {Malte Borggrewe and Markus Flosbach and Lucia Testa and Cedric Ly and Martin Baumdick and Immo Prinz and Hamburg Intestinal Tissue Study Group and Elena Billeb and Stefan Bonn and Madeleine J Bunders},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag730},
  url = {https://europepmc.org/article/MED/42867153},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Seumetry: a versatile and comprehensive R toolkit to accelerate high-dimensional flow and mass cytometry data analysis.
AU  - Malte Borggrewe
AU  - Markus Flosbach
AU  - Lucia Testa
AU  - Cedric Ly
AU  - Martin Baumdick
AU  - Immo Prinz
AU  - Hamburg Intestinal Tissue Study Group
AU  - Elena Billeb
AU  - Stefan Bonn
AU  - Madeleine J Bunders
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag730
UR  - https://europepmc.org/article/MED/42867153
AB  - Motivation  Recent progress in flow and mass cytometry technologies enables the simultaneous measurement of over 50 parameters for an individual cell. The resulting increase in data volume and complexity present challenges for conventional analysis methods, which are based on manual gating, are time-consuming and fail to capture unknown or minor cell populations. Advances in single-cell RNA sequencing (scRNAseq) technologies have prompted the development of computational analysis tools designed to process and analyze high-dimensional biological data. Building on these advances, we here present Seumetry, a framework that combines flow and mass cytometry data-specific analysis methods with the capabilities of Seurat, a powerful tool for the analysis of scRNAseq data.Results  Seumetry offers methods for advanced quality control, data visualizations, and differential population abundance and protein expression analysis. We tested Seumetry on an in-house generated complex dataset of immune cells from different layers of human intestines, demonstrating that Seumetry accurately identifies distinct immune cell populations. Furthermore, benchmarking against public datasets showed that Seumetry aligns with manual-gated results, further validating its use for high-dimensional flow and mass cytometry data. In summary, Seumetry provides a new scalable framework for the comprehensive analysis of high-dimensional cytometry data with seamless integration into commonly used scRNAseq analysis tools, enabling in-depth analysis methods to facilitate biological interpretations.Availability  Seumetry code is available open source under an MIT license and the current version can be viewed and used via Github: https://github.com/imsb-uke/Seumetry; and archived on Zenodo: https://doi.org/10.5281/zenodo.21216681. Flow cytometry data generated for this manuscript is available via Zenodo: https://doi.org/10.5281/zenodo.11935872.
SN  - 1367-4803
SN  - 1367-4811
ER  -