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

Motivation: Recent flow and mass cytometers generate 1,000,000 single cell datasets of dimensions 20 to 40. Many tools facilitate the discovery of new cell populations associated with diseases or physiology. These discoveries require the identification of new gating strategies, but gating strategies become exponentially harder 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 datasets. We use it to revisit some established gating strategies for the identification of Innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergate9s outputs are consistent with fields9 knowledge and sparser than those from a competing method. Availability and Implementation: Hypergate is implemented in R and available at http://github.com/ebecht/hypergate under an Open Source Initiative-compliant licence.