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Statistical Methods for Analyzing Antigen Receptors Data

分析抗原受体数据的统计方法

基本信息

批准号：
8658025
负责人：
Grzegorz A Rempala
金额：
$ 21.17万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2016-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8658025
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

项目摘要

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.

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在各种实验/临床条件下（例如，当监测化疗患者时）统计学比较来自不同组织的抗原受体（免疫球蛋白或T细胞受体）群体。由于（i）免疫系统维持的抗原受体库极其多样，以及（ii）数据收集的技术限制，该问题很困难，并且不容易适合标准统计框架。特别是，当应用于抗原受体研究时，物种丰富度和多样性推断的传统统计方法，例如，生态学中使用的，往往严重低估了真正的丰富性和多样性的TCR剧目。尽管现代分子技术在TCR数据收集方面取得了很大进展，但这导致了对此类谱系的生物学性状的理解相对较差。拟议的研究项目是一个跨学科的事业，由一组研究人员在应用数学，统计学，生物信息学和实验免疫学的背景。该项目的目标是（i）系统地回顾现有的抗原受体数据分析统计方法，（ii）提出新的，更有效的方法。广义而言，抗原受体数据集可以被表征为n个观察的k路表，其中多个细胞具有低计数并且细胞总数（群体丰富度）未知。为了分析这样的表，我们建议开发一个全面的方法，适用于从标准的生物测定，如流式细胞术，光谱和DNA测序，根据分层多项式和泊松模型计数数据。新提出的方法将使用模拟以及来自具有特别有限的TCR库的TCR-min小鼠中的癌症研究的数据进行评估。所得出并被认为最成功的统计方法将在公共领域软件中实施，该软件将在CRAN和caBIG档案馆提供。