staRgate

staRgate automates flow cytometry gating to identify flow biomarker positive populations relative to unimodal background populations, enabling analysis of T-cell lineage, differentiation, and functional states from multi-marker panels.

Key Features:

• Density-based gating strategy: A flexible approach that captures varying marker expression scenarios by defining positive populations relative to unimodal background populations.
• Multi-marker panel analysis: Supports analysis of a 29-marker flow panel for comprehensive T-cell characterization.
• Manual gating mimicry: Replicates manual gating logic to include cells with varying intensities of marker expression rather than applying rigid threshold cutoffs.

Scientific Applications:

• T-cell lineage characterization: Identifies T-cell subpopulations based on lineage marker expression patterns in flow cytometry data.
• T-cell differentiation state analysis: Distinguishes T-cells at different stages of differentiation using multi-parameter flow cytometry.
• T-cell functional state profiling: Assesses functional marker expression to characterize T-cell activation and effector status.

Methodology:

The pipeline uses a density-based gating strategy implemented in R that analyzes marker expression patterns to define positive populations relative to unimodal background distributions.