replicateFest: An R Package and Shiny App for Analysis of T Cell Receptor Repertoire Data from the Functional...

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Functional Expansion of Specific T cell (FEST)-based assays are crucial for identifying antigen-specific T cell responses.
• These assays are vital for neoantigen detection, vaccine development, and evaluating cancer immunotherapy efficacy.
• Existing computational tools lack robust methods for analyzing replicate-level data in FEST assays, hindering reproducibility.

Purpose of the Study:

• To develop a computational framework, replicateFest, for analyzing FEST-based T cell receptor sequencing data.
• To address challenges in reproducibility and interpretation caused by biological and technical variability in FEST assays.
• To provide tools for accurate identification of antigen-specific T cell clonotypes, even with replicate data.

Main Methods:

• Developed replicateFest, an R package and Shiny web application for FEST assay data analysis.
• Implemented Fisher's exact test for non-replicate datasets and negative binomial modeling for replicate experiments.
• Utilized adjusted p-values and odds ratios to identify significantly expanded clonotypes under antigen stimulation.

Main Results:

• replicateFest accurately identifies FEST-expanded and FEST-positive clonotypes.
• Validation with synthetic datasets confirmed the framework's ability to detect antigen-specific clonotypes.
• Application to HIV-1 epitope data reproduced previous findings and demonstrated utility for quality control.

Conclusions:

• replicateFest provides a robust computational solution for analyzing FEST-based TCR sequencing data, accommodating replicate variability.
• The framework enhances the reproducibility and interpretability of T cell response data.
• replicateFest is freely available, facilitating its adoption in immunological research.