论文标题
具有Sonnia的T和B细胞受体库的深层生成选择模型
Deep generative selection models of T and B cell receptor repertoires with soNNia
论文作者
论文摘要
淋巴细胞的亚类具有不同的功能作用，以共同产生免疫反应和持久的免疫力。除这些功能作用外，T和B细胞淋巴细胞依赖于其受体链的多样性来识别不同的病原体。在选择过程中，淋巴细胞亚类来自具有相同受体多样性的共同祖先。在这里，我们利用了通过机器学习模型的选择模型的生物物理模型来识别功能性淋巴细胞曲目和亚替代物的特征特征。特别是仅使用曲目级别序列信息，我们对CD4 $^+$和CD8 $^+$ t细胞进行了分类，在选择过程中引起的受体链之间找到相关性，并识别是致病性表位目标的T细胞子集。我们还展示了简单线性分类器何时进行以及更复杂的机器学习方法的示例。
Subclasses of lymphocytes carry different functional roles to work together to produce an immune response and lasting immunity. Additionally to these functional roles, T and B-cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically using only repertoire level sequence information, we classify CD4$^+$ and CD8$^+$ T-cells, find correlations between receptor chains arising during selection and identify T-cells subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.
