标题： 混合模型在单细胞分析中的应用：以疫苗研究为例 显示英文标题

标题： Mixture Models for Single Cell Assays with Applications to Vaccine Studies

摘要： 在免疫学研究中，从血液和组织中表征功能上不同的小细胞亚群对于解析系统级的生物学变化至关重要。越来越多的研究依赖于能够从大量细胞样本中提供多个基因和蛋白质单细胞测量值的分析方法。此类数据的一个常见问题是识别在两种生物条件下（例如，接种疫苗前/后）差异表达的生物标志物（或其组合），其中表达被定义为感兴趣细胞亚群中表达该生物标志物或组合的比例。在这里，我们提出了一种基于贝叶斯层次模型的框架，用于使用单细胞测定法检测差异生物标志物表达。我们的模型允许推断具有特定性，这通常是评估疫苗反应时所要求的，同时通过共同先验分布跨受试者借用信息。我们提出了两种参数估计方法：一种是利用期望最大化算法的经验贝叶斯方法，另一种是基于马尔可夫链蒙特卡洛算法的完全贝叶斯方法。我们将我们的方法与针对单细胞测定法的频率派方法进行比较，包括费舍尔精确检验、似然比检验和基本对数倍数变化。通过几个实验测定法（测量单细胞水平上的蛋白质或基因）和模拟数据，我们展示了我们的方法具有更高的灵敏度和特异性。额外的模拟表明，我们的框架对模型误设也具有鲁棒性。最后，我们还演示了如何通过狄利克雷-多项式模型扩展我们的方法以测试多种生物标志物组合的多变量差异表达，并用单细胞基因表达数据和模拟数据说明了这种方法。 显示英文摘要

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.