allelic inclusion

allelic inclusion estimates rates of α and β T cell receptor (TCR) allelic inclusion during V(D)J recombination using a fully Bayesian inference model to quantify dual-receptor T cells from emulsion-barcoding single-cell sequencing data.

Key Features:

• Bayesian Inference Model: Implements a fully Bayesian statistical approach that provides estimates of α and β TCR allelic inclusion rates and accounts for uncertainty in single-cell data.
• Experimental Validation: Validated with experimental data generated on two emulsion-barcoding single-cell sequencing platforms.
• Comprehensive Database: Includes a dataset comprising over 51,000 previously unpublished allelic inclusion TCR sequence sets derived from eight healthy individuals.
• Implementation: Provided as a Python implementation of the statistical inference model.

Scientific Applications:

• TCR Repertoire Analysis: Enables quantification of dual-receptor contributions to TCR diversity for studies of repertoire composition and clonality.
• Immunological Research: Supports investigation of how dual receptor T cells influence immune surveillance and responses in contexts such as autoimmunity, infection, and cancer immunotherapy.
• Personalized Medicine: Provides allelic inclusion estimates that can inform individualized analyses of TCR-mediated immunity relevant to vaccine and therapeutic design.

Methodology:

The method applies a Bayesian framework to integrate emulsion-barcoding single-cell sequencing data and infer allelic inclusion rates while accounting for uncertainties and variability in the data.