LEAPS-MPS: Statistical Learning on Next Generation Sequencing of T/B Cell Receptor Repertoire Data

适应性免疫系统的两个关键组成部分是所谓的T细胞和B细胞，它们的功能是识别和应对“身体入侵者”，例如冠状病毒或癌细胞。在每一个识别和反应过程之后，B细胞和T细胞会在细胞表面留下一生的遗产，称为B/T细胞受体，或bcr / tcr，一旦再次检测到病原体，身体就会利用这些受体快速而强烈地做出反应。BCR/TCR库在个体一生中不断形成，以应对疾病和感染，也可以作为一个人当前免疫状况的指纹。最近，新技术已经能够从单个血液或组织样本中分析BCR/TCR库。然而，由于免疫库数据的复杂性，需要新的统计机器学习方法和计算工具来分析免疫库数据。这个项目将产生统计分析方法，这不仅将帮助我们了解免疫系统是如何对疾病或感染做出反应的，而且还将帮助我们推进精准医学和免疫疗法，在这些疗法中，针对个体开发和定制治疗方法，以获得更大的疗效。这项研究旨在吸引数学和统计学的本科生和研究生，从而使他们接触到科学发现的兴奋，并为他们在学术界和工业界的高级学位课程和职业生涯中取得成功做好准备。通过专注于从代表性不足的群体中招募和培训学生，该计划将有助于科学劳动力的多样化。T细胞和B细胞是适应性免疫系统的重要组成部分，已被证明可介导抗体液免疫和对呼吸道冠状病毒的免疫反应。下一代T细胞和B细胞受体（TCR和BCR）测序可作为分析TCR/BCR库的平台。由于基因库数据的复杂性（异质性、高维性，呈现基因使用、丰度、克隆网络三层信息），现有的统计模型和推理工具非常有限。目前的分析工具缺乏识别与感兴趣的结果相关的保留表签名或集成多层信息的能力。该项目的主要目标是开发先进的统计方法和机器学习方法，以1)使用曲目的基因使用层识别与结果相关的基因和基因家族；2)利用库的网络层对与结果相关的网络属性进行优先排序；3)整合多层次的储备，评价异质性储备剖面对产出的联合效应。特别是，贝叶斯层次模型将被开发用于差异基因使用，置换辅助组套索将被开发用于优先考虑局部和全局属性的网络分析，以及各种核方法将被用来模拟库特征和结果之间的复杂关系。将在公共可用的covid数据库上进行模拟研究和真实数据分析，以演示这些方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Two crucial components of the adaptive immune system are the so-called T cells and B cells, whose function is identifying and responding to “body invaders”, such as for example coronavirus or cancer cells. Following each of the identify-and-respond processes, B cells and T cells leave a lifetime lasting legacy on cell surfaces known as B/T cell receptors, or BCRs/TCRs, which the body uses to respond quickly and strongly once the pathogen is detected again. BCR/TCR repertoire, which are continually shaped throughout the lifetime of an individual in response to diseases and infections, can also serve as a fingerprint of one’s current immunological profile. Recently, new technologies have enabled the profiling of BCR/TCR repertoire from a single sample of blood or tissue. However, due to the complex nature of the repertoire data, there is a need for novel statistical machine learning approaches and computational tools for immune repertoire data analysis. This project will produce statistical analysis methods, which will not only help us understand how the immune system is responding to disease or infection, but also help us advance precision medicine and immunotherapy, where treatments are developed and tailored to an individual for greater efficacy. This research has been designed to engage undergraduate and graduate students of mathematics and statistics, thus exposing them to the excitement of scientific discovery and preparing them for success in advanced degree programs and careers in academia and industry. By focusing on recruiting and training students from underrepresented groups, the PI will contribute to the diversification of the scientific workforce.T cells and B cells represent a crucial component of the adaptive immune system and have been shown to mediate anti-humoral immunity and mediate immune response to respiratory coronavirus. Next generation sequencing of the T and B cell receptors (TCRs and BCRs) can be used as a platform to profile the TCR/BCR repertoire. Due to the complex characteristics of repertoire data (heterogeneous, high-dimensional, presents three layers of information: gene usage, abundance, clone network), there are very limited statistical models and inference tools existing in the literature. The current analyses tools lack the ability to identify the repertoire signatures that are associated with the outcome of interest or to integrate multiple layers of information. The main goal of this project is to develop advanced statistical methods and machine learning methods to 1) identify the gene and gene families associated with the outcome using the gene usage layer of repertoire; 2) prioritize the network properties associated with outcome using the network layer of repertoire; 3) integrate the multiple layers of repertoire to evaluate the joint effect of heterogenous repertoire profile on the outcome. Particularly, a Bayesian hierarchical model will be developed to differential gene usages, a permutation-assisted group lasso will be developed to prioritize both local and global properties for network analysis, and various kernel methods will be utilized to model the complex relationship between repertoire features and outcome. Simulation studies and real data analysis on a public-available covid database will be performed to demonstrate the methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.