Abstract
Flow cytometry technique produces large, multi-dimensional datasets of properties of individual cells that are helpful for biomedical science and clinical research. This paper explores an approach for comparing and clustering flow cytometry data. To overcome challenges posed by the irregularities and the high dimensions of the data, we develop a set of data preprocessing techniques to facilitate effective clustering of flow cytometry data files. We present a set of experiments using real data from the Protective Immunity Project (PIP) showing the effectiveness of the approach.