CytoPy

CytoPy performs automated, data-centric analysis of high-dimensional single-cell flow and mass cytometry data within Python to support reproducible immunophenotyping and mitigate batch effects.

Key Features:

• Automated Analysis: Automates the analysis process to reduce reliance on manual intervention and enable handling of large cytometry datasets.
• Document-Based Database Integration: Uses a document-based database to enable a data-centric approach and support iterative analysis.
• Algorithm-Agnostic Design: Provides an algorithm-agnostic framework that allows integration of diverse computational methods within Python.
• Batch Effect Mitigation: Manages significant batch effects arising from technical and user variation to support robust phenotype calling.
• Immunophenotyping Application: Has been applied to immunophenotype local inflammatory infiltrate in individuals with and without acute bacterial infection.

Scientific Applications:

• Immunophenotyping: Supports detailed cellular phenotyping and immune profiling from flow and mass cytometry data.
• T cell Subset Phenotyping: Enables phenotyping of T cell subsets from whole blood while addressing batch effects.
• Clinical Inflammatory Infiltrate Analysis: Used to immunophenotype local inflammatory infiltrate in studies of acute bacterial infection.
• Multi-batch Cytometry Studies: Supports analysis across samples with technical and user-induced batch variation.

Methodology:

Leverages a document-based database, implements an algorithm-agnostic framework to integrate diverse computational methods within Python, automates analysis workflows, and includes approaches to mitigate batch effects.