  Analysis of flow cytometry data is an essential tool for clinical diagnosis
of hematological and immunological conditions. Current clinical workflows rely
on a manual process called gating to classify cells into their canonical types.
This dependence on human annotation limits the rate, reproducibility, and
complexity of flow cytometry analysis. In this paper, we propose using Mondrian
processes to perform automated gating by incorporating prior information of the
kind used by gating technicians. The method segments cells into types via
Bayesian nonparametric trees. Examining the posterior over trees allows for
interpretable visualizations and uncertainty quantification - two vital
qualities for implementation in clinical practice.
