FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows

Area of Science:

• Biotechnology
• Bioinformatics
• Computational Biology

Background:

• Cytometry data analysis faces challenges in collaboration between domain and quantitative experts.
• Increasing data complexity necessitates automated workflows in cytometry.
• Traditional tools like FlowJo limit access to advanced Single Cell Data Science algorithms.

Purpose of the Study:

• To develop a solution for seamless collaboration in cytometry data analysis.
• To bridge the gap between domain experts and advanced computational tools.
• To facilitate the integration of FlowJo with modern data science algorithms.

Main Methods:

• Developed FlowKit, a Gating-ML 2.0-compliant Python package.
• Implemented functionality to read and write FCS files and FlowJo workspaces.
• Demonstrated workflow construction for reporting and analysis.

Main Results:

• FlowKit enables bidirectional data transfer between FlowJo and Python environments.
• Facilitated joint analysis by domain and quantitative experts.
• Showcased the construction of automated analysis and reporting workflows.

Conclusions:

• FlowKit enhances collaboration in cytometry data analysis.
• The package integrates traditional cytometry software with advanced data science.
• Enables domain experts to leverage a wider range of analytical tools for improved results validation and interpretation.