基于细胞计数数据的细胞亚群识别的贝叶斯特征分配模型
A Bayesian Feature Allocation Model for Identification of Cell Subpopulations Using Cytometry Data
摘要：
提出了一种贝叶斯特征分配模型（FAM），用于基于通过飞行时间流式细胞术（CyTOF）获得的细胞表面或细胞内标志物表达水平数据的多个样品来鉴定细胞亚群。细胞亚群的特征在于标记物的表达模式的差异，并且基于其观察到的表达水平的模式将单个细胞聚类到亚群中。一个有限的印度自助餐过程是用来模拟亚群作为潜在的功能，和基于模型的方法，这些潜在的功能亚群的基础上，用于构建每个样本内的细胞簇。通过定义静态缺失数据机制来解释由于质谱细胞仪中的技术伪影而导致的不可解释的缺失数据。与单独应用于不同样品的基于观察到的标志物表达水平的常规细胞聚类方法相比，基于FAM的方法可以同时应用于多个样品，并且可以鉴定可能被常规聚类遗漏的重要细胞亚群。提出的基于FAM的方法被应用于联合分析三个数据集，产生的CyTOF，研究自然杀伤（NK）细胞。因为FAM鉴定的亚群可以定义新的NK细胞亚群，所以这种统计分析可以提供关于NK细胞的生物学及其在癌症免疫治疗中的潜在作用的有用信息，这反过来可以导致改进的细胞疗法的开发。模拟研究所提出的方法的行为下的两种情况下的已知亚群也提出了，然后通过分析的CyTOF NK细胞表面标志物的数据。
英文摘要：
A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in expression patterns of makers, and individual cells are clustered into the subpopulations based on the patterns of their observed expression levels. A finite Indian buffet process is used to model subpopulations as latent features, and a model-based method based on these latent feature subpopulations is used to construct cell clusters within each sample. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missing data mechanism. In contrast to conventional cell clustering methods based on observed marker expression levels that are applied separately to different samples, the FAM based method can be applied simultaneously to multiple samples, and can identify important cell subpopulations likely to be missed by conventional clustering. The proposed FAM based method is applied to jointly analyze three datasets, generated by CyTOF, to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved cellular therapies. Simulation studies of the proposed method's behavior under two cases of known subpopulations also are presented, followed by analysis of the CyTOF NK cell surface marker data.
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