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Title: Model-based tools for the analysis of flow and mass cytometric data Authors:  Sharon Lee - University of Adelaide (Australia) [presenting]
Abstract: Cytometry plays an important role in clinical diagnosis and monitoring of lymphoma and leukaemia. However, analysis of modern cytometric data is challenging due to their high dimensionality, large number of observations, as well as complex distributional features such as multimodality, asymmetry, and other non-normal characteristics. Firstly, a mixture model-based tool is presented to automatically segment and perform dimension reduction of high-dimensional cytometry data. Secondly, the tasks of unsupervised clustering and supervised classification of multiple heterogeneous cytometric samples are presented. We adopt a linear mixed model approach to handle inter-sample variations, and flexible component densities to cater for non-normal cluster shapes. The usefulness and effectiveness of these model-based tools are demonstrated using a number of real data from flow and mass cytometry experiments.