  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.
