Benchmarking unsupervised methods for inferring TCR specificity

Charline Jouannet

doi:10.1093/nargab/lqaf150

2025-12-01

PRE

Encarnita Mariotti‐Ferrandiz *

DOI：10.1093/nargab/lqaf150

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摘要

En 中文

鉴定T细胞受体（TCR）特异性对于推动适应性免疫的理解至关重要。尽管已发展出用于推断TCR特异性的计算方法，但它们的聚类行为尚未得到充分比较。我们通过整理一个统一的数据库来解决这个问题，该数据库包含来自121种生物的2313个表位对应的190670个人类TCR，这些TCR具有已知的特异性，数据整合自IEDB、McPAS-TCR和VDJdb。我们探究了广泛使用的TCR聚类方法是否能在同一高置信度数据集上产生可比的结果。我们假设关于保守CDR3基序的共享假设会产生相似的聚类模式，而差异则反映算法设计。针对此数据集，对基于相似性的9种TCR聚类方法进行了基准测试。DeepTCR表现出最佳的重现率，而ClusTCR、TCRMatch和GLIPH2在聚类纯度方面表现优异，但重现率较低。GLIPH2、Levenshtein距离、Hamming距离和clusTCR生成了较大的聚类，与TCRMatch和DeepTCR形成对比。GIANA和iSMART则产生了较小且抗原特异性的聚类。DeepTCR在捕获抗原特异性TCR方面最为敏感。我们使用一个来自10X Genomics的更大数据集（包含抗原特异性标记TCR和非标记细胞）验证了这些观察结果。本研究提供了一个统一的TCR数据库和特异性推断方法的基准测试，为研究人员选择合适工具提供指导。

Keyword:

ALLELIC EXCLUSION

CELL

EXPRESSION

SEQUENCE

期刊

NAR Genomics and Bioinformatics

IF:

2.8

论文数:

259

被引数:

机构

Sorbonne Universite

学者数:

6.2W

论文数: 4.5W

被引数: 605

引用论文

Can we predict T cell specificity with digital biology and machine learning?我们可以通过数字生物学和机器学习预测T细胞特异性吗？

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