Auto clustering method study of flow cytometry data based on skew t-mixture models

A major component of flow cytometry data analysis involves gating, which is the process of identifying homogeneous groups of cells. As manual gating is error-prone, non-reproducible, nonstandardized, and time-consuming, we propose a flexible statistical model-based clustering approach to identifying cell populations in flow cytometry data based on skew t-mixture models. This approach, which employs a finite mixture model with the density function of skew t-distribution, estimates parameters via an expectation maximization algorithm. Data analysis from two different experiments prove that the model-based clustering methods give better results in terms of robustness against outliers than non model-based clustering methods. Compared to the Gaussian mixture models, skew normal mixture models and t-mixture models, the skew t-mixture models have more flexibility in clustering symmetric data and leads to lower misclassification rates when handling highly asymmetric data. ©, 2014, Chinese Institute of Electronics. All right reserved.