Bridging the Divide between Manual Gating and Bioinformatics with the Bioconductor Package flowFlowJo

Area of Science:

• Bioinformatics
• Computational Biology
• Flow Cytometry Data Analysis

Background:

• Traditional flow cytometry relies on geometric gating for cell type selection, which is challenging to manage computationally.
• Extracting and analyzing gating results alongside raw data is crucial for robust analysis and validation.
• Existing workflows lack seamless integration for examining gating robustness and combining manual and automated approaches.

Purpose of the Study:

• To develop a bioinformatics tool for improved computational analysis of flow cytometry gating data.
• To enable examination of gating robustness and facilitate the combination of manual and automated gating strategies.
• To support exploratory data analysis within the Bioconductor framework.

Main Methods:

• Development of the Bioconductor package 'flowFlowJo'.
• Implementation of functionality to import gates defined in the commercial FlowJo software.
• Ensuring compatibility with existing Bioconductor flow cytometry packages for integrated analysis.

Main Results:

• The flowFlowJo package successfully imports FlowJo-defined gates.
• The package allows computational manipulation and analysis of gating results.
• Demonstrated utility in examining gating robustness and supporting exploratory data analysis.

Conclusions:

• The flowFlowJo package provides essential bioinformatics capabilities for flow cytometry data analysis.
• It enhances the ability to work computationally with gating strategies, improving data interpretation and reproducibility.
• This tool facilitates advanced analyses, including gating validation and integration of diverse gating methods.