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Related Concept Videos

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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry.

Md Hasanul Banna Siam1, Md Akkas Ali1, Donald Vardaman1

  • 1Department of Pathology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35205 USA.

Biorxiv : the Preprint Server for Biology
|December 23, 2024
PubMed
Summary

CAFE is a new Python web app simplifying high-dimensional spectral flow cytometry data analysis. It offers automated tools for cell identification and visualization, making complex data accessible without coding.

Keywords:
BioinformaticsCytometryLeidenProtocol

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Area of Science:

  • * Computational Biology
  • * Immunology
  • * Bioinformatics

Background:

  • * Spectral flow cytometry enables simultaneous measurement of up to 50 markers, offering deep insights into cellular heterogeneity.
  • * Analyzing high-dimensional (HD) data from spectral flow cytometry is challenging using traditional manual gating methods.
  • * A need exists for accessible, user-friendly tools to interpret complex cellular data.

Purpose of the Study:

  • * To develop an open-source, Python-based web application named CAFE (Cellular Analysis Flow cytometry Environment) with a graphical user interface.
  • * To provide an accessible platform for analyzing high-dimensional spectral flow cytometry data.
  • * To streamline the process of cell cluster identification and annotation.

Main Methods:

  • * CAFE is built using Streamlit, integrating libraries like Scanpy for single-cell analysis, Pandas and PyArrow for data handling, and Matplotlib/Seaborn/Plotly for visualization.
  • * Features include density-based down-sampling, dimensionality reduction, batch correction, and Leiden-based clustering.
  • * The application offers interactive controls for real-time figure generation and cluster annotation.

Main Results:

  • * Demonstrated successful analysis of a large human peripheral blood mononuclear cell (PBMC) dataset (350,000 cells).
  • * Identified 16 distinct cell clusters within the PBMC dataset using CAFE.
  • * Generated publication-ready figures interactively, showcasing the tool's visualization capabilities.

Conclusions:

  • * CAFE provides an accessible, code-free solution for analyzing high-dimensional spectral flow cytometry data.
  • * The application democratizes complex data analysis, enabling researchers without coding expertise to interpret cellular heterogeneity.
  • * CAFE facilitates the identification and characterization of distinct cell populations, advancing biological discovery.