Mixture models for single-cell assays with applications to vaccine studies
Mixture models for single-cell assays with applications to vaccine studies
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DOI：
10.1093/biostatistics/kxt024
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发表时间：
2014-01-01
期刊：
影响因子：
2.1
通讯作者：
Gottardo, Raphael
中科院分区：
文献类型：
--
作者：
Finak, Greg;McDavid, Andrew;Gottardo, Raphael
血液和组织由许多功能不同的细胞亚群组成。在免疫学研究中，这些只能使用单细胞测定法准确测量。这些小细胞亚群的表征对于破译系统水平的生物学变化至关重要。出于这个原因，越来越多的研究依赖于从大量细胞样品中提供多个基因和蛋白质的单细胞测量的测定。分析此类数据的一个常见问题是鉴定在两种生物学状况（例如，糖尿病和糖尿病）之间差异表达的生物标志物（或生物标志物的组合）。G.刺激前/后），其中表达被定义为在感兴趣的细胞亚群中表达该生物标志物（或生物标志物组合）的细胞的比例。在这里，我们提出了一个贝叶斯分层框架的基础上，β-二项式混合模型的差异生物标志物的表达，使用单细胞测定测试。我们的模型允许推断是受试者特定的，这是评估疫苗反应时通常需要的，同时通过共同的先验分布在受试者之间借用力量。我们提出了两种方法进行参数估计：一种是使用期望最大化算法的贝叶斯方法，另一种是基于马尔可夫链蒙特卡罗算法的完全贝叶斯方法。我们比较我们的方法对经典的方法，包括Fisher精确检验，似然比检验，和基本的对数倍变化的单细胞测定。使用几个实验测定测量蛋白质或基因在单细胞水平和模拟，我们表明，我们的方法比其他方法具有更高的灵敏度和特异性。额外的模拟表明，我们的框架也是强大的模型误指定。最后，我们展示了我们的方法如何可以扩展到使用Dirichlet多项式模型测试多个生物标志物组合的多变量差异表达，并使用单细胞基因表达数据和模拟来说明这种方法。
Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e. g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.