AliGater

AliGater provides a Python framework for automated feature extraction and modular pipeline construction to analyze large-scale, high-dimensional flow cytometry data for immunology and genome-wide association studies.

Key Features:

• Automatic Feature Extraction: Python-based workflows for automated extraction and quantification of markers from high-dimensional flow cytometry datasets.
• Modular Pipeline Construction: Modular components for assembling customized analysis pipelines accommodating diverse experimental designs.
• Scalability and Efficiency: Architecture optimized to process large-scale, high-resolution flow cytometry datasets and execute complex analytical workflows rapidly.

Scientific Applications:

• Immunophenotyping: Characterizing diverse immune cell populations from high-dimensional flow cytometry data.
• Genome-wide association studies (GWAS): Enabling GWAS on large cohorts (e.g., 14,288 individuals across 46 immune cell types) to identify genetic associations with cellular phenotypes.

Methodology:

Python-based automation and optimization of data processing tasks, integration with existing cytometry software tools, automated feature extraction and modular pipeline construction, and support for analyses from descriptive statistics to multivariate analyses.