VoPo

VoPo performs predictive modeling and visualization of large single-cell and mass cytometry datasets to define phenotypically and functionally homogeneous cell populations and identify immune-correlates associated with clinically relevant parameters.

Key Features:

• Predictive Modeling: Uses machine learning to leverage cellular heterogeneity for predictive modeling of complex single-cell and mass cytometry data.
• Comprehensive Visualization: Produces visualizations to explore and interpret patterns of heterogeneity within large single-cell datasets.
• Phenotypic and Functional Classification: Defines phenotypically and functionally homogeneous cell populations for downstream analysis.
• Performance Superiority: Demonstrates superior classification performance versus state-of-the-art machine learning algorithms on three mass cytometry datasets, including datasets with hundreds of millions of cells across numerous samples.
• Clinical Relevance: Identifies immune-correlates associated with clinically relevant parameters.

Scientific Applications:

• Immunology: Identifies immune-correlates and supports analysis of immune system dynamics.
• Cancer Research: Classifies phenotypic and functional cell populations to investigate tumor heterogeneity.
• Stem Cell Biology: Supports analysis of differentiation and cellular heterogeneity in stem cell studies.

Methodology:

Employs machine learning algorithms to process and analyze large-scale single-cell and mass cytometry datasets, tailored to handle high-throughput data volume and complexity.