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A Bayesian feature allocation model (FAM) is presented for identifying cellsubpopulations based on multiple samples of cell surface or intracellularmarker expression level data obtained by cytometry by time of flight (CyTOF).Cell subpopulations are characterized by differences in expression patterns ofmakers, and individual cells are clustered into the subpopulations based on thepatterns of their observed expression levels. A finite Indian buffet process isused to model subpopulations as latent features, and a model-based method basedon these latent feature subpopulations is used to construct cell clusterswithin each sample. Non-ignorable missing data due to technical artifacts inmass cytometry instruments are accounted for by defining a static missing datamechanism. In contrast to conventional cell clustering methods based onobserved marker expression levels that are applied separately to differentsamples, the FAM based method can be applied simultaneously to multiplesamples, and can identify important cell subpopulations likely to be missed byconventional clustering. The proposed FAM based method is applied to jointlyanalyze three datasets, generated by CyTOF, to study natural killer (NK) cells.Because the subpopulations identified by the FAM may define novel NK cellsubsets, this statistical analysis may provide useful information about thebiology of NK cells and their potential role in cancer immunotherapy which maylead, in turn, to development of improved cellular therapies. Simulationstudies of the proposed method's behavior under two cases of knownsubpopulations also are presented, followed by analysis of the CyTOF NK cellsurface marker data.

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