Bayesian clustering of flow cytometry data for the diagnosis of B-chronic lymphocytic leukemia.

In the rapidly advancing field of flow cytometry, methodologies facilitating automated clinical decision support are increasingly needed. In the case of B-chronic lymphocytic leukemia (B-CLL), discrimination of the various subpopulations of blood cells is an important task. In this work, our objective is to provide a useful paradigm of computer-based assistance in the domain of flow-cytometric data analysis by proposing a Bayesian methodology for flow cytometry clustering. Using Bayesian clustering, we replicate a series of (unsupervised) data clustering tasks, usually performed manually by the expert. The proposed methodology is able to incorporate the expert's knowledge, as prior information to data-driven statistical learning methods, in a simple and efficient way. We observe almost optimal clustering results, with respect to the expert's gold standard. The model is flexible enough to identify correctly non canonical clustering structures, despite the presence of various abnormalities and heterogeneities in data; it offers an advantage over other types of approaches that apply hierarchical or distance-based concepts.