FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational...

Area of Science:

• * Computational Biology
• * Immunology
• * Bioinformatics

Background:

• * Increasing dimensionality, throughput, and complexity of cytometry data necessitates advanced analysis tools.
• * Existing tools often require coding expertise or down-sampling, limiting accessibility and data integrity.
• * Integration of user-friendly interfaces with high-performance machine learning is crucial for modern cytometry analysis.

Purpose of the Study:

• * To introduce FlowAtlas, an interactive web application for cytometry data analysis.
• * To enable dimensionality reduction without data down-sampling, preserving data integrity.
• * To provide a coding-free solution for complex cytometry data, bridging user-friendly interfaces with powerful machine learning frameworks.

Main Methods:

• * Development of an interactive web application, FlowAtlas.
• * Implementation of dimensionality reduction techniques compatible with non-identical staining panels.
• * Integration with high-performance machine learning frameworks in Julia.
• * Application to a human multi-tissue, multi-donor immune cell dataset.

Main Results:

• * FlowAtlas facilitates intuitive and rapid discovery and detection of cell populations, including rare ones.
• * The application successfully handles high-dimensional cytometry data without down-sampling.
• * Compatibility with datasets stained with non-identical panels is demonstrated.
• * Key immunological findings were highlighted using a complex human immune cell dataset.

Conclusions:

• * FlowAtlas provides a user-friendly, no-code solution for advanced cytometry data analysis.
• * It effectively addresses the challenges posed by high-dimensional and complex cytometry datasets.
• * The tool enhances the ability to discover and analyze cell populations, accelerating immunological research.