Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq.

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 LIBRA-seq antigen count data 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 the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of techn

SUBMITTER: Wasdin PT 

PROVIDER: S-EPMC10614817 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature