Statistical Methods for Analyzing Antigen Receptors Data

描述（由申请人提供）：研究脊椎动物免疫系统的最常见方法之一是统计比较在各种实验/临床条件下（例如，在监测化疗患者时）来自不同组织的抗原受体（免疫球蛋白或t细胞受体）的群体。由于(i)免疫系统维持的抗原受体库极其多样化和（ii）数据收集的技术限制，这个问题很困难，而且不容易适用于标准的统计框架。特别是，当应用于抗原受体研究时，传统的物种丰富度和多样性推断统计方法，例如生态学中使用的统计方法，往往严重低估了TCR库的真实丰富度和多样性。尽管现代分子技术在TCR数据收集方面取得了巨大进步，但这导致了对这些曲目的生物学特性的理解相对较差。本研究项目是一个跨学科的研究项目，由具有应用数学、统计学、生物信息学和实验免疫学背景的研究人员组成。该项目的目标是(i)系统地审查现有的分析抗原受体数据的统计方法，（ii）提出新的、更有效的方法。从广义上讲，抗原受体数据集可以表征为n个观察值的k-way表，其中多个细胞计数低，细胞总数（种群丰富度）未知。为了分析这些表格，我们建议开发一种综合的方法，适用于从标准生物测定中获得的数据，如流式细胞术、光谱分型和DNA测序，在计数数据的层次多项式和泊松模型下。新提出的方法将与传统方法进行比较，使用模拟和TCR-min小鼠的癌症研究数据进行评估，这些小鼠的TCR曲目特别有限。得出的和被认为最成功的统计方法将在公共领域的软件中实施，这些软件将在CRAN和caBIG档案中提供。

DESCRIPTION (provided by applicant): One of the most common ways of studying a vertebrate immune system is to statistically compare populations of antigen receptors (either immunoglobulins or T-cell receptors) derived from different tissues under various experimental/clinical conditions (for instance, when monitoring chemotherapy patients). The problem is difficult and does not fit readily in the standard statistical frameworks due to (i) extremely diverse antigen receptor repertoires maintained by the immune system and (ii) technological limitations on data collection. In particular, when applied to antigen receptor studies, the traditional statistical methods of species richness and diversity inference, as e.g., ones used in ecology, often seriously underreport the true richness and diversity of TCR repertoires. This contributes to the relatively poor understanding of such repertoires' biological traits, despite great advances of modern molecular technology in TCR data collection. The proposed research project is an interdisciplinary undertaking by a team of researchers with backgrounds in applied mathematics, statistics, bioinformatics, and experimental immunology. The project's goal is to (i) systematically review the existing statistical methods for analyzing antigen receptor data and (ii) propose new, more efficient ones. In broad terms, the antigen receptor dataset may be characterized as a k-way table of n observations, with multiple cells of low counts and with a total number of cells (population richness) unknown. To analyze such tables, we propose to develop a comprehensive approach applicable to data obtained from the standard biological assays, like flow cytometry, spectratyping and DNA sequencing, under the hierarchical multinomial and Poisson models for counts data. The new proposed methods will be evaluated vis-a-vis traditional ones using the simulations as well as the data from cancer studies in TCR-min mice which have specially limited TCR repertoire. The statistical methodology derived and deemed most successful will be implemented in the public domain software to be made available at CRAN and caBIG archives.