Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting

Abstract

Motivation Recent flow and mass cytometers generate datasets of dimensions 20 to 40 and a million single cells. From these, many tools facilitate the discovery of new cell populations associated with diseases or physiology. These new cell populations require the identification of new gating strategies, but gating strategies become exponentially more difficult to optimize when dimensionality increases. To facilitate this step, we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity. Results Hypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public d..