openCyto

openCyto provides automated analysis of flow cytometry and CyTOF data to enable reproducible, scalable gating and identification of cell populations for immunological and clinical studies.

Key Features:

• Automated Analysis Framework: Provides an infrastructure for end-to-end automated analysis of flow cytometry data emphasizing reproducibility.
• Scalability: Handles large, high-dimensional datasets with memory-efficient data handling suitable for clinical trial–scale analyses.
• Hierarchical Analysis: Integrates domain-specific knowledge via hierarchical relationships among cell populations to improve gating accuracy.
• Pipeline Definition via CSV: Defines analysis pipelines using text-based CSV files to enable data-agnostic, repeatable analyses.
• Automated Gating Methods: Implements automated gating methods that mimic traditional manual gating strategies to increase reproducibility.
• BioConductor Integration: Builds upon existing BioConductor flow cytometry infrastructure.

Scientific Applications:

• Intracellular Cytokine Staining (ICS) — HIV vaccine trial: Applied to an ICS dataset from a published HIV vaccine trial and identified a novel subset of CD8 T-cells with a vaccine-regimen-specific response not detectable by manual analysis.
• CyTOF T-cell phenotyping: Applied to a CyTOF T-cell phenotyping dataset for high-dimensional characterization of T-cell subsets.
• Rare antigen-specific T-cell detection and vaccine development: Facilitates uncovering rare antigen-specific T-cell populations relevant to immunological research and vaccine development.

Methodology:

Built on BioConductor flow cytometry infrastructure, incorporating scalable data handling and domain-specific hierarchical knowledge integration, supporting automated gating methods and pipeline definitions via text-based CSV files.