CytoPipeline

CytoPipeline automates construction, visualization, and benchmarking of pre-processing pipelines for flow cytometry data to enable standardized quality control and event filtering for downstream analysis.

Key Features:

• Automated Pipeline Construction: Assembles pre-processing pipelines that perform signal compensation or unmixing, scale transformation, debris, doublet, and dead cell removal, and batch effect correction for flow cytometry data.
• Customizable Workflow: Allows selection and configurable ordering of elementary pre-processing steps with alternative implementation methods and R packages, where ordering can influence downstream analysis outcomes.
• Diagnostic and Visualization Tools: Provides standardized diagnostic metrics and visualizations to enable objective comparison and assessment of pre-processing methods.
• Benchmarking Capabilities: Implements benchmarking of pipeline configurations, with emphasis on comparing quality-control methods and assessing their impact on downstream analyses.

Scientific Applications:

• Pre-processing for downstream analysis: Produces high-quality, filtered events suitable for downstream tasks such as population identification and quantitative comparisons.
• Method comparison and selection: Enables objective comparison of alternative pre-processing and quality-control methods across datasets.
• Reproducible analysis of high-dimensional data: Supports standardized, reproducible pre-processing of large flow cytometry datasets used in research studies.

Methodology:

Assembles and assesses pre-processing pipelines using real-life datasets; supports elementary steps implemented via multiple methods and R packages, explicitly including signal compensation or unmixing, scale transformation, debris, doublet, and dead cell removal, and batch effect correction, and performs benchmarking of pipeline configurations with standardized diagnostics and visualizations.