Lin Lin, Chan Cliburn, Hadrup Sine R, Froesig Thomas M, Wang Quanli, West Mike
Department of Statistical Science, Duke University, Durham, NC, 27708-0251, USA.
Stat Appl Genet Mol Biol. 2013 Jun;12(3):309-31. doi: 10.1515/sagmb-2012-0001.
Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling approaches in many areas of biomedical research and application. In studies of immune profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays in which each marker is targeted with a probe that is labeled with two or more fluorescent tags. The use of several colors enables the identification of, in principle, combinatorially increasingly numbers of subtypes of cells, each identified by a subset of colors. This represents a major advance in the ability to characterize variation in immune responses involving larger numbers of functionally differentiated cell subtypes. We describe novel classes of Markov chain Monte Carlo methods for model fitting that exploit distributed GPU (graphics processing unit) implementation. We discuss issues of cellular subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments discuss broader questions in applications in immunology, and aspects of statistical computation.
自动流式细胞术技术在测量数千到数百万个细胞上蛋白质标记物水平方面的新用途，正促使生物医学研究和应用的许多领域对相关的、定制的贝叶斯混合建模方法的需求不断增加。在许多生物学领域的免疫分析研究中，传统的流式细胞术使用荧光标记标签来测量标记蛋白的相对丰度水平，这些标签通过单色识别特定标记物。该领域最近一项具体且重要的进展是使用组合标记分析，其中每个标记物都用带有两个或更多荧光标签的探针进行靶向。使用多种颜色原则上能够识别越来越多的细胞亚型组合，每个亚型由一组颜色来识别。这代表了在表征涉及大量功能分化细胞亚型的免疫反应变化能力方面的重大进步。我们描述了用于模型拟合的新型马尔可夫链蒙特卡罗方法，该方法利用分布式图形处理器（GPU）实现。我们在这个新颖的通用模型框架中讨论细胞亚型识别问题，并使用模拟数据提供一个详细示例。然后，我们描述了该方法在一项使用组合编码分析对人血样本中抗原特异性T细胞亚型进行实验研究的数据集上的应用。总结性评论讨论了免疫学应用中的更广泛问题以及统计计算方面的问题。
