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 agai

nst 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.