PICAFlow: a complete R workflow dedicated to flow/mass cytometry data, from pre-processing to deep and comprehensive...

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Flow and mass cytometry generate complex, high-dimensional datasets.
• Existing software often lacks comprehensive features for data handling and analysis.
• Batch effects and heterogeneity can confound experimental results.

Purpose of the Study:

• To introduce PICAFlow, an integrated R workflow for flow and mass cytometry data.
• To provide a user-friendly, all-in-one solution for comprehensive data analysis.
• To address limitations in current software, such as real-time data transformation and batch effect correction.

Main Methods:

• PICAFlow is an R-written package.
• It includes interactive R Shiny applications for data transformation and compensation.
• Normalization methods are implemented to mitigate batch effects and heterogeneity.
• Features include dimensionality reduction, cell clustering, and statistical analyses.

Main Results:

• PICAFlow offers a user-friendly interface for complex data analysis.
• It integrates multiple analysis steps from pre-processing to visualization.
• Interactive tools facilitate real-time data manipulation and quality control.
• Methods for batch effect removal and heterogeneity reduction are incorporated.

Conclusions:

• PICAFlow provides a powerful and comprehensive solution for flow and mass cytometry data analysis.
• Its integrated approach and user-friendly design enhance data handling and interpretation.
• The package addresses key challenges in cytometry data analysis, improving reproducibility and accuracy.