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Related Concept Videos

Flow Cytometry01:23

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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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CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets.

Malgorzata Nowicka1,2, Carsten Krieg3, Lukas M Weber1,2

  • 1SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland.

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Summary

This study introduces an R pipeline for analyzing high-dimensional cytometry data. It enables robust differential analysis of cell populations and signaling markers, accommodating complex experimental designs.

Keywords:
CyTOFdifferential analysisflow cytometry

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Area of Science:

  • Immunology
  • Computational Biology
  • Biostatistics

Background:

  • High-dimensional cytometry (HDCyto) is crucial for cell population analysis.
  • Existing analysis pipelines may lack flexibility for complex experimental designs.

Purpose of the Study:

  • To present an updated R-based pipeline for differential analysis of HDCyto data.
  • To provide a flexible framework for analyzing cell type abundance and signaling markers.
  • To accommodate complex experimental designs including batch effects and paired samples.

Main Methods:

  • Utilizes Bioconductor packages and FlowSOM for cell population clustering.
  • Incorporates reproducible manual merging of algorithm-generated clusters.
  • Employs regression frameworks, including generalized linear mixed models and linear mixed models, for differential analyses.
  • Supports exploratory data analysis throughout the workflow.

Main Results:

  • The pipeline facilitates differential analysis of cell type abundance and signaling markers within subpopulations.
  • It allows for differential analysis of aggregated signals.
  • The regression-based approach effectively models complex experimental designs and overdispersion.

Conclusions:

  • The updated R pipeline offers a comprehensive and flexible approach to HDCyto data analysis.
  • It enhances the statistical rigor and reproducibility of differential analyses in cytometry studies.
  • The workflow supports robust modeling of various experimental designs, improving biological insights.