Identifying Rare Cell Populations In Comparative Flow Cytometry

Multi-channel, high throughput experimental methodologies for flow cytometry are transforming clinical immunology and hematology, and require the development of algorithms to analyze the high-dimensional, large-scale data. We describe the development of two combinatorial algorithms to identify rare cell populations in data from mice with acute promyelocytic leukemia. The flow cytometry data is clustered, and then samples from the leukemic, pre-leukemic, and Wild Type mice are compared to identify clusters belonging to the diseased state. We describe three metrics on the clustered data that help in identifying rare populations. We formulate a generalized edge cover approach in a bipartite graph model to directly compare clusters in two samples to identify clusters belonging to one but not the other sample. For detecting rare populations common to many diseased samples but not to the Wild Type, we describe a clique-based branch and bound algorithm. We provide statistical justification of the significance of the rare populations.