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Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Mar 6, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Distance metric learning using random forest for cytometry data.

M Baran Pouyan, J Birjandtalab, M Nourani

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary
    This summary is machine-generated.

    We introduce CytoRFD, a novel cell distance metric for analyzing single-cell mass cytometry (CyTOF) data. This Random Forest-based approach overcomes the curse of dimensionality, improving cell type identification in complex datasets.

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    Area of Science:

    • Computational Biology
    • Data Science
    • Immunology

    Background:

    • Single-cell mass cytometry (CyTOF) enables high-dimensional analysis of cellular populations.
    • Traditional distance metrics struggle with high-dimensional CyTOF data due to the curse of dimensionality.
    • Effective cell type identification is crucial for understanding complex biological systems.

    Purpose of the Study:

    • To develop a novel cell distance metric for improved analysis of high-dimensional CyTOF data.
    • To address the limitations of existing metrics in the face of increasing data complexity.
    • To enhance the accuracy and effectiveness of cell type identification in CyTOF studies.

    Main Methods:

    • Proposed a new cell distance metric, CytoRFD, based on the Random Forest (RF) concept.
    • Evaluated CytoRFD performance against traditional distance metrics.
    • Utilized experimental CyTOF datasets for validation.

    Main Results:

    • CytoRFD demonstrated superior performance in large-scale data analysis compared to traditional metrics.
    • The proposed distance metric significantly improved the quality and effectiveness of cell type identification.
    • CytoRFD effectively mitigates the impact of the curse of dimensionality on CyTOF data.

    Conclusions:

    • CytoRFD offers a robust and effective solution for analyzing high-dimensional CyTOF data.
    • The Random Forest-based approach provides a powerful new tool for single-cell data visualization and clustering.
    • This advancement facilitates more accurate cell type discovery and biological insights from complex CyTOF experiments.