Negative binomial mixture model for identification of noise in antibody-antigen specificity predictions from single-cell data.

Abstract

Motivation: LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in single-cell B cell receptor sequencing data, such as LIBRA-seq, is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies.

Results: In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages single-cell sequencing reads from a large, multi-donor dataset described in a recent LIBRA-seq study to develop a data-driven means for identification of technical noise. We apply this method to nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for machine learning based approaches as the corpus of single-cell B cell sequencing data continues to grow.

Availability and implementation: All data and code are available at https://github.com/IGlab-VUMC/mixture_model_denoising.