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

Authors

Malte Borggrewe, Markus Flosbach, Lucia Testa, Cedric Ly, Martin Baumdick, Immo Prinz, Hamburg Intestinal Tissue Study Group , Elena Billeb, Stefan Bonn, Madeleine J Bunders

Published in

Bioinformatics (Oxford, England). Oct 13, 2026. Epub Oct 13, 2026.

Abstract

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.
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.
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.
Supplementary data are available at Bioinformatics online.

PMID:
42867153
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

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