CytoPy: An autonomous cytometry analysis framework

Area of Science:

• Computational Biology
• Bioinformatics
• Immunology

Background:

• Cytometry technologies are advancing, enabling higher dimensional single-cell data acquisition.
• New computational methods are needed to analyze this high-dimensional data effectively.
• Widespread adoption of computational tools in immunology remains a challenge.

Purpose of the Study:

• To present CytoPy, a Python framework for automated cytometry data analysis.
• To provide a data-centric and iterative analytical environment.
• To foster open-source cytometry bioinformatics within the Python ecosystem.

Main Methods:

• Developed CytoPy, a Python framework integrating a document-based database.
• Designed an algorithm-agnostic platform for flexible analysis.
• Applied the CytoPy pipeline to phenotype T cell subsets and analyze inflammatory infiltrates.

Main Results:

• CytoPy successfully phenotyped T cell subsets in whole blood, robust to batch effects.
• The framework demonstrated efficacy in analyzing inflammatory infiltrates in infection models.
• CytoPy provides an open-source, iterative, and data-centric analytical environment.

Conclusions:

• CytoPy offers a powerful and adaptable solution for high-dimensional cytometry data analysis.
• The framework promotes open-source collaboration in cytometry bioinformatics.
• CytoPy facilitates robust immunophenotyping, even with complex datasets and batch variations.