Automated identification of stratifying signatures in cellular subpopulations

Significance Single-cell measurement technologies such as flow cytometry permit the investigation of specific cellular subpopulations. Mass cytometry currently measures >40 parameters per cell and produces phenotypically rich datasets that may be retrospectively interrogated for relevant biological signal. There are few methods that identify experimentally relevant subpopulations within these datasets, and most do not scale well to higher-dimensional measurements. To address this bottleneck, we present a data-driven method termed Citrus that identifies cell subsets associated with an experimental endpoint of interest. Citrus can test diverse experimental hypotheses and is demonstrated through the systematic identification of ( i ) blood cells that signal in response to experimental stimuli and ( ii ) T-cell subsets whose abundance is predictive of AIDS-free survival risk in patients with HIV.