A two-stage clustering technique for automatic biaxial gating of flow cytometry data

Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is approximation of the number of cellular populations which heavily affects the accuracy of results. In this work, we propose a novel technique to estimate the number of dominant subtypes and identify them in flow cytometry datasets. Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91%) in identifying the main cellular populations.