ImmunoCluster

ImmunoCluster analyzes high-dimensional cytometry datasets for immune profiling and assessment of cellular heterogeneity.

Key Features:

• Implementation: Implemented as an R package.
• Scalability: Scales to datasets comprising millions of cells.
• Input Modalities: Supports liquid mass cytometry, imaging mass cytometry, and flow cytometry data.
• Data Structure Compatibility: Uses the SingleCellExperiment data structure for data representation and interoperability.
• Visualization Integration: Integrates visualization tools from the scDataViz Bioconductor package.
• Dimensionality Reduction and Clustering: Implements dimensionality reduction and unsupervised clustering for population discovery.
• Annotation and Differential Testing: Provides cluster annotation and differential testing to identify significant differences between groups.
• Data Import and Quality Control: Includes data import and quality control procedures to ensure data integrity.

Scientific Applications:

• Immune Monitoring: Enables immune monitoring across health conditions and diseases using mass and flow cytometry.
• Cellular Heterogeneity Analysis: Characterizes cellular heterogeneity within complex samples.
• Biomarker Discovery: Identifies potential biomarkers through comparative analysis of annotated cell populations.

Methodology:

The computational workflow comprises data import and quality control; dimensionality reduction and unsupervised clustering; and cluster annotation and differential testing.