Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq.
Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq.
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用于 LIBRA-seq 抗原特异性预测中噪声识别的负二项式混合模型。
DOI：
10.1101/2023.10.13.562258
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Georgiev,IvelinS
中科院分区：
文献类型：
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作者：
Wasdin,PerryT;Abu-Shmais,AlexandraA;Irvin,MichaelW;Vukovich,MatthewJ;Georgiev,IvelinS
动机LIBRA-seq（通过测序将B细胞受体与抗原特异性连接）为询问抗原特异性B细胞区室和鉴定针对感兴趣的抗原靶标的抗体提供了有力的工具。识别LIBRA-seq抗原计数数据中的噪声对于改善下游应用（包括抗体发现和机器学习技术）的抗原结合预测至关重要。结果在这项研究中，我们提出了一种对LIBRA-seq数据进行去噪的方法，该方法通过使用负二项混合模型将抗原计数聚类到信号和噪声分量中。这种方法利用了最近的LIBRA-seq研究中包括的VRC 01阴性对照细胞（Abu-Shmais et al.）以提供用于识别技术噪声的数据驱动的手段。我们将这种方法应用于代表单独LIBRA-seq实验的九个供体的数据集，并表明与LIBRA-seq中使用的标准评分方法相比，我们的方法提供了体外抗体-抗原结合的改进预测，尽管样本之间的数据大小和噪声结构存在差异。随着LIBRA-seq数据库的不断增长，这一开发将提高LIBRA-seq识别抗原特异性B细胞的能力，并有助于为未来基于机器学习的方法提供更可靠的数据集来预测抗体-抗原结合。
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 technical noise. We apply this method to a dataset of 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 used in LIBRA-seq, 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 future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow.