Comparing T cell receptor repertoires using optimal transport.-猫眼课题宝

DOI：

10.1371/journal.pcbi.1010681

发表时间：

2022-12

期刊：

PLoS computational biology

影响因子：

4.3

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728925/

整个T细胞受体（TCR）库的复杂性使它们的比较成为一项困难但重要的任务。目前的TCR曲目比较方法由于考虑过于简单的序列或曲目水平特征，可能导致分布信息的高度损失。在样本空间中给定值之间的距离或度量，具有吸引人的理论和计算性质的最优传输方法形成了这种比较的合适方法。在本文中，我们引入了一种非参数方法来比较经验TCR库，该方法应用了Sinkhorn距离，一种快速的当代最佳传输方法，以及最近创建的TCR之间的距离，称为TCRdist。我们表明，我们的方法在几个案例研究中识别出不同TCR分布的样本之间有意义的差异，并在最小的建模假设和更简单的管道下与更复杂的方法竞争。T细胞对于成功的适应性免疫反应至关重要，这主要是由于其表面表达高度多样化的受体蛋白。这些T细胞受体（TCRs）识别可能是外来入侵者（如病毒或细菌）的肽。正因为如此，免疫学家经常对比较这些tcr的不同集合（或集合）感兴趣，希望能够识别出特别感兴趣的群体，例如使用接种前和接种后样本对特定疫苗有反应的tcr。目前比较TCR库的方法要么依赖于可能不能充分描述数据的统计模型，要么使用可能丢失信息的汇总统计，要么难以解释。我们提出了一种比较TCR曲目的互补方法，该方法使用距离而不是模型、汇总统计或降维来检测两个给定曲目之间显著不同的TCR。通过几个案例研究，我们证明了我们的方法可以识别生物学上有意义的曲目差异。

The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline. T cells are critical for a successful adaptive immune response, largely due to the expression of highly diverse receptor proteins on their surfaces. These T cell receptors (TCRs) recognize peptides that may be foreign invaders such as viruses or bacteria. Because of this, immunologists are often interested in comparing different sets (or repertoires) of these TCRs in hopes of identifying groups of particular interest, such as TCRs that are responding to a particular vaccination using pre- and post-vaccination samples. Current methods of comparing TCR repertoires either rely on statistical models which may not adequately describe the data, use summary statistics that may lose information, or are difficult to interpret. We present a complementary method of comparing TCR repertoires that detects significantly different TCRs between two given repertoires using a distance rather than a model, summary statistics, or dimension reduction. We demonstrate that our method can identify biologically meaningful repertoire differences using several case studies.