Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Mass cytometry offers higher dimensionality than fluorescence flow cytometry for cellular analysis.
• Analyzing high-dimensional mass cytometry data presents significant technical challenges.
• Existing tools lack comprehensive integration for mass cytometry data interpretation.

Purpose of the Study:

• To introduce cytofkit, a novel Bioconductor package for comprehensive mass cytometry data analysis.
• To provide an integrated bioinformatics toolset for pre-processing, visualization, and cell subset identification.
• To facilitate the interpretation of complex cellular heterogeneity in tissues.

Main Methods:

• Development of the cytofkit R package integrating state-of-the-art bioinformatics methods and novel algorithms.
• Implementation of functions for data pre-processing, dimensionality reduction (linear/non-linear), and cell subset identification.
• Inclusion of a graphical user interface (GUI) and a Shiny application (APP) for interactive visualization and ease of use.

Main Results:

• Cytofkit accurately identified lymphocyte subsets in a CD14-CD19- PBMCs dataset.
• The package uncovered multiple subtypes of TFH cells within human CD4+ T cells from blood and tonsils.
• Demonstrated applicability for both mass cytometry and flow cytometry data analysis.

Conclusions:

• Cytofkit provides a powerful and user-friendly solution for mass cytometry data analysis.
• The package enhances the resolution of cellular diversity and facilitates biological discovery.
• Cytofkit is freely available, promoting wider adoption in immunological and biological research.