A Multidimensional Flow Cytometry Data Classification

Flow has been widely used for the diagnosis of various diseases. We proposed an automated gating approach to analyze and cluster flow data and provide researchers and physicians a 3-D way of viewing flow data. This automated approach was designed to give reproducible results, which avoid the subjective and time-consuming human manipulation. Doublets problem will be eliminated by our tool, and the result were compared with the manual gating. In addition, a 3D visualization will not only give a user-friendly representation but also avert misclassification would occur in lower dimension. We demonstrated how the 3D approach can be used to augment the K-means method in classifying the data. To validate the feasibility of our proposed automatic approach, our result is compared with the result done at the Methodist Hospital in Houston.