A novel statistical method for decontaminating T-cell receptor sequencing data.
A novel statistical method for decontaminating T-cell receptor sequencing data.
复制标题
一种净化 T 细胞受体测序数据的新统计方法。
DOI：
10.1093/bib/bbad230
复制
发表时间：
2023
影响因子：
9.5
通讯作者：
Li,Ziyi
中科院分区：
文献类型：
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作者：
Li,Ruoxing;Altan,Mehmet;Reuben,Alexandre;Lin,Ruitao;Heymach,JohnV;Tran,Hai;Chen,Runzhe;Little,Latasha;Hubert,Shawna;Zhang,Jianjun;Li,Ziyi
T细胞受体（TCR）库在人群中高度多样化，在启动多种免疫过程中起着至关重要的作用。已经开发了TCR测序（TCR-seq）来分析T细胞库。与其他高通量实验类似，污染可能发生在TCR-seq的几个步骤中，包括样品收集，制备和测序。这种污染会在数据中产生伪影，导致不准确甚至有偏见的结果。大多数现有的方法假设“干净的”TCR-seq数据作为起点，没有处理数据污染的能力。在这里，我们开发了一种新的统计模型来系统地检测和去除TCR-seq数据中的污染。我们将观察到的污染总结为两个来源，成对和交叉队列。对于这两种污染源，我们都提供可视化和汇总统计数据，以帮助用户评估污染的严重程度。从14个现有的具有最小污染的TCR-seq数据集中提取先验信息，我们开发了一个简单的贝叶斯模型来统计识别污染样本。我们还提供了去除受影响序列的策略，以允许下游分析，从而避免任何重复实验的需要。我们所提出的模型显示了稳健性的污染检测相比，一些现成的检测方法在模拟研究。我们在本地生成的两个TCR-seq数据集上说明了我们提出的方法的使用。
The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume ‘clean’ TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally.