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A Bayesian Feature Allocation Model for Identification of Cell Subpopulations Using Cytometry Data

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.