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

Overview

Cytofast is a visualization tool designed to analyze mass cytometry data by comparing clustering methods such as FlowSOM and Cytosplore. It provides a rapid quantitative and qualitative overview of the data, highlighting differences between clustering algorithms.

Key Study Components

Area of Science

• Mass cytometry
• Data visualization
• Clustering analysis

Background

• Mass cytometry generates complex data that require efficient visualization.
• Cytofast identifies immunological patterns within immune cell populations.
• It can be applied after clustering methods like FlowSOM or Cytosplore.
• Understanding cell subsets linked to clinical treatments is crucial for research.

Purpose of Study

• To provide a tool for visualizing and quantifying mass cytometry data.
• To compare different clustering methods effectively.
• To facilitate the discovery of cell subsets related to clinical outcomes.

Methods Used

• Clusters are created using Cytosplore or FlowSOM.
• Data is transformed and cleaned in R for analysis.
• Clustering is performed, and results are visualized through heat maps and box plots.
• MSI plot function is used to detect global changes in marker expression.

Main Results

• Cytofast generates heat maps and dendrograms to illustrate clustering results.
• It highlights the impact of treatments on natural killer cell phenotypes.
• Quantitative results are presented through box plots and MSI plots.
• The tool allows for rapid visualization and quantification of cellular responses.

Conclusions

• Cytofast is an effective tool for analyzing mass cytometry data.
• It enhances the understanding of immune cell subsets in relation to treatments.
• The method supports the discovery of novel cellular responses post-therapy.

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