单细胞分析的混合模型及其在疫苗研究中的应用
Mixture Models for Single Cell Assays with Applications to Vaccine Studies
摘要：
在免疫学研究中，来自血液和组织的小的、功能不同的细胞亚群的表征对于解读系统水平的生物学变化至关重要。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。分析此类数据的常见问题是鉴定在两种生物条件（例如，接种前/接种后），其中表达被定义为在感兴趣的细胞亚群中表达生物标志物或组合的细胞的比例。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异性的，这是在访问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：一个基于期望最大化算法的贝叶斯方法和一个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单细胞测定，包括Fisher精确检验，似然比检验，和基本的对数倍变化的频率论方法。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数据，我们表明，我们的方法具有更高的灵敏度和特异性比替代方法。额外的模拟表明，我们的框架也是强大的模型误指定。最后，我们还展示了我们的方法如何扩展到使用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多变量方法。
英文摘要：
In immunological studies, the characterization of small, functionally distinct cell subsets from blood and tissue is crucial to decipher system level biological changes. 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 thereof) that are differentially expressed between two biological conditions (e.g., before/after vaccination), where expression is defined as the proportion of cells expressing the biomarker or combination in the cell subset 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 inference to be subject specific, as is typically required when accessing 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 frequentist 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 the single-cell level and simulated data, 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 also demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this multivariate approach using single-cell gene expression data and simulations.
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