The Probabilities Mixture Model for Clustering Flow-Cytometric Data: An Application to Gating Lymphocytes in Peripheral Blood

Abstract

Data clustering is a major data mining technique and has been shown to be useful in a wide variety of domains, including medical and biological statistical data analysis. A non trivial application of cluster analysis occurs in the identification of different subpopulations of particles in large-sized heterogeneous flow-cytometric data. Mixture-model based clustering has been several times applied in the past to medical and biological data analysis; to our knowledge, however, non of these applications was involved with flow-cytometric data. We claim, that utilizing the probabilities mixture model offers several advantages compared to other proposed flow-cytometric data clustering approaches. We apply this model in order to gate lymphocytes in peripheral blood, which is a necessary first-step procedure when dealing with various hematological diseases diagnoses, such as lymphocytic leukemias and lymphoma.KeywordsMixture ModelBayesian NetworkData ClusterSupervise ClusterGating LymphocyteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.