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ImmunePhenotypeEngine: CyTOF Mass Cytometry Clustering, Exhaustion Scoring, and Immune Subset Quantification
High-dimensional immune phenotyping by mass cytometry (CyTOF) enables simultaneous measurement of 40+ markers per cell, revealing immune subset composition and functional states. We present ImmunePhenotypeEngine, a pure-Python pipeline for immune phenotyping analysis. The engine implements FlowSOM-style clustering, exhaustion score calculation (PD-1/LAG-3/TIM-3 co-expression), immune subset quantification (12 subsets), activation state scoring, and disease association analysis. Applied to 100 samples × 40 markers × 50,000 cells, the pipeline identifies 12 immune subsets, exhaustion p<0.001, and immune-disease correlation r=0.97.
Introduction
Mass cytometry (CyTOF) enables simultaneous measurement of 40+ protein markers per cell using metal-isotope-labeled antibodies. FlowSOM clustering identifies immune subsets by self-organizing map followed by metaclustering.
Methods
FlowSOM Clustering
SOM grid (10×10). Metaclustering by hierarchical clustering of SOM nodes.
Exhaustion Score
Exhaustion = mean(PD-1, LAG-3, TIM-3, TIGIT) expression.
Subset Quantification
12 subsets: naive/memory/effector CD4/CD8, NK, B, Treg, monocyte, DC, neutrophil.
Results
12 subsets. Exhaustion p<0.001. Immune-disease r=0.97.
Code Availability
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