Model for comparative analysis of antigen receptor repertoires.
Model for comparative analysis of antigen receptor repertoires.
复制标题
用于抗原受体曲目比较分析的模型。
DOI：
10.1016/j.jtbi.2010.10.001
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发表时间：
2011-01-21
影响因子：
2
通讯作者：
Ignatowicz, Leszek
中科院分区：
文献类型：
--
作者：
Rempala, Grzegorz A.;Seweryn, Michal;Ignatowicz, Leszek
关键词：
在现代分子生物学中，分析脊椎动物免疫系统的标准方法之一是对不同组织在不同实验或临床条件下产生的特异性抗原受体克隆(免疫球蛋白或T细胞受体)进行测序和比较。由此产生的统计挑战是困难的，而且不能很容易地纳入列联表的标准统计框架，主要是因为对受体人口的抽样严重不足。这种采样不足一方面是由免疫系统维持的抗原受体谱系的极端多样性造成的，另一方面是由于受体数据收集过程的高成本和劳动强度造成的。在大多数最近的免疫学文献中，抗原受体种群之间的差异是通过借鉴生态学研究中的物种重叠和多样性的非参数统计度量来检验的。虽然这种方法在广泛的情况下是稳健的，但它似乎对潜在的克隆大小分布和区分受体群体的整体机制提供了很少的洞察力。作为一种可能的选择，本文提出了一种对欠采样数据进行调整的参数方法，并利用现代无监督学习统计工具提供了一种统一的方法来同时比较多个受体组。该参数模型基于灵活的多变量泊松-对数正态分布，是生物多样性生态研究中单变量泊松-对数正态分布的自然推广。描述了评估模型适合性的程序以及为执行必要的诊断而开发的公共领域软件。当应用于转基因小鼠群体中T细胞受体的数据时，模型驱动的分析被认为比传统方法更有利。
In modern molecular biology one of the standard ways of analyzing a vertebrate immune system is to sequence and compare the counts of specific antigen receptor clones (either immunoglobulins or T-cell receptors) derived from various tissues under different experimental or clinical conditions. The resulting statistical challenges are difficult and do not fit readily into the standard statistical framework of contingency tables primarily due to the serious under-sampling of the receptor populations. This under-sampling is caused, on one hand, by the extreme diversity of antigen receptor repertoires maintained by the immune system and, on the other, by the high cost and labor intensity of the receptor data collection process. In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of the species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method that adjusts for the data under-sampling as well as provides a unifying approach to a simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in the ecological studies of biodiversity patterns. The procedure for evaluating a model’s fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.
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