FlowAnd

FlowAnd analyzes and integrates large-scale, multi-color flow cytometry data to automate preprocessing, clustering, visualization, and statistical analysis for high-dimensional cytometry studies.

Key Features:

• Data Importing: Supports importing flow cytometry data from various file formats and accommodates different experimental setups.
• Transformations: Provides transformation methods for preprocessing including normalization and scaling of flow cytometry measurements.
• Clustering Algorithms: Incorporates multiple clustering algorithms for automated gating of cell populations, with automated gating using three different algorithms shown to closely match manual gating results.
• Visualization Tools: Offers visualization tools for validation and interpretation of clustering results in multi-parameter datasets.
• Statistical Testing: Includes statistical testing capabilities for rigorous analysis of flow cytometry experiments.
• Extensibility and HPC Support: Enables integration of external tools written in Java, R, Python, or MATLAB and supports high-performance computing environments.

Scientific Applications:

• Phosphoproteomics in CML: Applied to a phosphoproteomics dataset from 34 chronic myeloid leukemia patients treated with kinase inhibitors to compare automated and manual gating.
• Large-scale multi-color studies: Used for analysis and integration of over 70 flow cytometry experiments involving high-dimensional parameter sets.
• Panel and parameter adaptation: Supports adaptation to different marker panels and parameter settings for diverse experimental conditions.

Methodology:

Computational steps include data import, preprocessing transformations (normalization and scaling), automated clustering/gating using multiple algorithms (three algorithms reported), visualization for clustering validation, statistical testing, integration of external tools (Java, R, Python, MATLAB), and support for high-performance computing environments.