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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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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Model-based clustering for flow and mass cytometry data with clinical information.

Ko Abe1, Kodai Minoura1,2, Yuka Maeda3

  • 1Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 4668550, Japan.

BMC Bioinformatics
|September 17, 2020
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Summary

This study introduces LAMBDA, a new statistical framework for analyzing cytometry data. It simultaneously identifies cell populations and links them to clinical information, improving upon traditional two-step methods.

Keywords:
Bayesian mixture modelFlow cytometyMass cytometoryStochastic EM algorithm

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Area of Science:

  • Single-cell biology
  • Computational biology
  • Biostatistics

Background:

  • High-dimensional cytometry (flow and mass) enables single-cell protein profiling for biological insights.
  • Traditional analysis involves separate cell population identification and statistical testing, risking bias and information loss.
  • Integrating clinical data with cytometry requires robust analytical methods.

Purpose of the Study:

  • To develop a novel statistical framework for simultaneous cell population identification and clinical association discovery.
  • To overcome limitations of traditional two-step analysis in cytometry data.

Main Methods:

  • Proposed LAMBDA (Latent Allocation Model with Bayesian Data Analysis), a unified statistical framework.
  • Utilized specified probabilistic models tailored for flow and mass cytometry data distributions.
  • Employed a zero-inflated distribution for mass cytometry data, validated through simulation studies.

Main Results:

  • LAMBDA successfully performs simultaneous cell population identification and clinical association discovery.
  • Simulation studies confirmed the accuracy of LAMBDA's parameter estimation.
  • Real-world data analysis demonstrated LAMBDA's capability to identify significant cell population-clinical outcome associations.

Conclusions:

  • LAMBDA offers a powerful, integrated approach for analyzing high-dimensional cytometry data.
  • The framework enhances the discovery of biologically relevant associations between cell populations and clinical outcomes.
  • LAMBDA is implemented in R and publicly available, facilitating its adoption in research.