ClustIRR

ClustIRR analyzes repertoires of B- and T-cell receptors by identifying communities of immune receptors with similar specificities based on complementarity-determining region (CDR) sequences, then employs Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires for identifying expanding or contracting communities.

Key Features:

• Community Detection: Identifies communities of immune receptors with similar specificities based on the sequences of their complementarity-determining regions (CDRs).
• Differential Community Occupancy (DCO): Employs Bayesian probabilistic models to quantify DCO between repertoires.
• Repertoire Comparison: Enables identification of expanding or contracting communities in response to conditions such as infection or cancer treatment.

Scientific Applications:

• Immune Repertoire Analysis: Analyzes B- and T-cell receptor repertoires to characterize immune receptor communities.
• Differential Repertoire Analysis: Identifies expanding or contracting immune receptor communities between repertoires in response to infection or cancer treatment.

Methodology:

ClustIRR identifies communities of immune receptors with similar specificities based on CDR sequences, then employs Bayesian probabilistic models to quantify differential community occupancy between repertoires.