Automated Gating for Flow Cytometry Data Using a Kernel-Smoothed EM Algorithm

Phytoplankton are microscopic algae responsible for roughly half of the world's photosynthesis that play a critical role in global carbon cycles and oxygen production, and measuring the abundance of their subtypes across a wide range of spatiotemporal scales is of great relevance to oceanography. High-frequency flow cytometry is a powerful technique in which oceanographers at sea can rapidly record the optical properties of tens of thousands of individual phytoplankton cells every few minutes. Identifying distinct subpopulations within these vast datasets (a process known as "gating") remains a major challenge and has largely been performed manually so far. In this paper, we introduce a fast, automated gating method, which accurately identifies phytoplankton populations by fitting a time-evolving mixture of Gaussians model using an expectation-maximization-like algorithm with kernel smoothing. We use simulated data to demonstrate the validity and robustness of this approach, and use oceanographic cruise data to highlight the method's ability to not only replicate but surpass expert manual gating. Finally, we provide the flowkernel R package, written in literate programming, that implements the algorithm efficiently.