Aug 2026· Cytometry Part A· 0 citations· 13 references
Medicine
TL;DR
Cytoflow is open source, user-friendly point-and-click software written in Python that allows non-programmers to apply modern computational flow cytometry methods to their data sets and can save an experimenter time and improve the reliability and reproducibility of their analysis.
Abstract
Modern flow cytometry experiments routinely measure 18 or more fluorescent markers across many samples, patients, and tissues. As these experiments' complexity increases, manual gating becomes unacceptably inefficient and can introduce operator-to-operator variation. Computational cytometry algorithms such as unsupervised clustering, automated gating, and dimensionality reduction can increase the speed and reliability of these analyses, but using them requires time and expertise that many biomedical scientists lack. Cytoflow was built to bridge this gap. Cytoflow is open source, user-friendly point-and-click software written in Python that allows non-programmers to apply modern computational flow cytometry methods to their data sets. Additionally, Cytoflow's modules can be used directly in a Python script or a JupyterLab notebook. Finally, extending Cytoflow with new modules that support future applications is straightforward, and feedback from an active user community continues to guide ongoing development. As a result, Cytoflow can save an experimenter time and improve the reliability and reproducibility of their analysis. Cytoflow is available to download at https://cytoflow.github.io. Source code is hosted at https://github.com/cytoflow/cytoflow, and documentation is available at https://cytoflow.readthedocs.io.
FlowLOT is introduced, an optimal-transport-based framework that models the single-cell measurement data of a pa- tient sample as an empirical cellular distribution and maps it directly into a fixed-length feature vector and unifies high-dimensional classification, interpretable visualization, and continuous quantitati...
Naqib Sad Pathan, M. Shifat-E.-Rabbi, Kristofor E. Pas et al.· arXiv.org· 0 citations
Fine-tuning CytoBERT for sample-level classification demonstrates that transfer learning across heterogeneous cytometry datasets is feasible, providing a starting point for scalable, generalizable cytometry analysis.
S. A. Qadri, Bjarne C. Hiller, F. Blanke et al.· 0 citations
Flow cytometry is a powerful analytical technique that generates high-dimensional single-cell data widely used in biological research and clinical diagnostics. This survey traces the evolution of flow cytometry analysis from traditional manual gating to modern algorithmic approaches and explores emerging deep-learning...
Tony Xu, Givanna H. Putri, Aaron Chuah et al.· ACM Computing Surveys· 0 citations
Flow cytometers are highly sensitive scientific instruments that need to be in optimal condition to provide accurate and reliable data. To ensure consistent performance, cytometer performance is regularly assessed by measuring standardized reference particles during a quality control (QC) measurement. Cytometer manufac...
B. Kolen, G. I. Reyneveld, Tom B. Kuipers et al.· Cytometry Part A· 0 citations
Manual expert gating remains common practice for defining specific cell populations in flow cytometry data, but increasing numbers of measured parameters and high inter-rater variability limit consistency across studies. Cluster-based approaches use the full marker space to define cell populations more consistently, bu...
V. Friedrich, Karola Mai, T. Hofer et al.· Nature Communications· 0 citations
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