January 2008
Paper to appear in Cytometry, A
Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometry data. The configuration of cells as represented by multiple markers simultaneously can be arbitrarily well modeled as a mixture of Gaussian distributions in the dimension of the number of markers, and fitting such models is enabled using standard Bayesian statistical approaches and Markov chain Monte Carlo computations. Cellular subtypes may be related to one or multiple components of such mixtures, and fitted mixture models can be evaluated in the full set of markers as an alternative, or adjunct, to traditional subjective gating methods that relies on choosing one or two dimensions. This paper describes studies of statistical mixture modeling of flow cytometry data, and demonstrates their utility in examples with data from human peripheral blood samples using four markers.