flowClust: a Bioconductor package for automated gating of flow cytometry data

Area of Science:

• Computational Biology
• Bioinformatics
• Data Science

Background:

• Flow cytometry (FCM) is a high-throughput technology for cell analysis in research and diagnostics.
• Current FCM data analysis is manual, time-consuming, and often overlooks high-dimensional data.
• There is a need for automated software to parallelize FCM data generation.

Purpose of the Study:

• To develop an automated analysis platform for flow cytometry data.
• To address the limitations of manual FCM data analysis.
• To provide a robust and user-friendly software tool for the cytometry community.

Main Methods:

• Developed the R package 'flowClust' for automated FCM analysis.
• Implemented a robust model-based clustering approach using multivariate t mixture models.
• Incorporated Box-Cox transformation for data handling and outlier identification.

Main Results:

• flowClust automates cell population identification in FCM data.
• The package handles outlier detection and data transformation effectively.
• Provides tools for summarizing and visualizing clustering results.

Conclusions:

• flowClust offers a theoretically sound, automated solution for FCM analysis.
• Reduces subjectivity and human time costs in FCM data interpretation.
• Enhances reproducibility and facilitates technological advancement in cytometry.