Statistical Methods for Analyzing Antigen Receptors Data

分析抗原受体数据的统计方法

• 批准号: 8259188
• 负责人: Grzegorz A Rempala
• 金额: $ 16.62万
• 依托单位: AUGUSTA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-01-07
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8259188
• 关键词: Algorithms; Animal Experiments; Antigen Receptors; Archives; Bayesian Method; Benchmarking; Biodiversity; Bioinformatics; Biological; Biological Assay; Categories; Cell Count; Cells; Clinical; Code; Collection; Computer software; DNA Sequence; Data; Data Analyses; Data Collection; Data Set; Development; Documentation; Ecology; Entropy; Evolution; Family; Flow Cytometry; Frequencies; General Population; Goals; Immune response; Immune system; Immunoglobulins; Immunology; Immunotherapy; Inequality; Language; Lead; Libraries; Linear Models; Literature; Log-Linear Models; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Measures; Methodology; Methods; Modeling; Molecular; Monitor; Motivation; Mus; Nature; Online Systems; Patients; Performance; Population; Procedures; Public Domains; Regression Analysis; Research; Research Personnel; Research Project Grants; Sample Size; Sampling; Series; Simulate; Staging; Statistical Methods; T-Cell Receptor; T-Lymphocyte; Techniques; Technology; Testing; Time; Time Series Analysis; Tissues; Validation; Weight; Work; Writing; analytical tool; base; cancer Biomedical Informatics Grid; cancer immunotherapy; cancer therapy; chemotherapy; comparative; computerized data processing; computerized tools; cost; improved; interest; loss of function; melanoma; novel strategies; public health relevance; receptor; simulation; software development; statistics; theories; tool; trait; trend

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. PUBLIC HEALTH RELEVANCE: The proposed research will develop analytical and computational tools for analyzing antigen receptor data. Proper analysis of such data is one of the fundamental issues in studying vertebrate immune responses and therefore, the methods developed in this proposal will have broad applications to immunological studies in general. The tools developed in this proposal will help us to understand better the nature and various functions of T-cells which will lead to the development of more effective approaches to immunotherapy and cancer treatment.

描述(申请人提供)：研究脊椎动物免疫系统最常见的方法之一是在不同的实验/临床条件下(例如，当监测化疗患者时)统计比较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(1)免疫系统维持的抗原受体谱系极为多样化，以及(2)数据收集方面的技术限制，这个问题很难解决，而且不适合标准的统计框架。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的统计方法，例如用于生态学的方法，往往严重低估了TCR谱系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很大进展，但这导致对这些曲目的生物学特性的了解相对较差。拟议的研究项目是由一组具有应用数学、统计学、生物信息学和实验免疫学背景的研究人员进行的跨学科研究。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，并(Ii)提出新的、更有效的方法。广义地说，抗原受体数据集可以被描述为n个观察的k向表，具有多个低计数的细胞，并且细胞总数(群体丰富度)未知。为了分析这些表格，我们建议开发一种全面的方法，适用于在计数数据的分层多项式和泊松模型下从标准生物分析获得的数据，如流式细胞术、分型和DNA测序。新提出的方法将使用模拟和TCR-min小鼠癌症研究的数据与传统方法进行比较，TCR-min小鼠的TCR谱系特别有限。得出并被认为是最成功的统计方法将在公共领域软件中实施，可供CRAN和CABIG档案使用。 公共卫生相关性：拟议的研究将开发分析抗原受体数据的分析和计算工具。对这些数据的正确分析是研究脊椎动物免疫反应的基本问题之一，因此，本提案中开发的方法将广泛应用于免疫学研究。这项建议中开发的工具将帮助我们更好地了解T细胞的性质和各种功能，这将导致开发更有效的免疫治疗和癌症治疗方法。