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

2025-10-07

AI Summary

Manual gating of phytoplankton flow cytometry data is time-consuming, subjective, and poorly scalable. This paper introduces *flowkernel*, a novel automated gating method that embeds temporal continuity priors into a Gaussian mixture model via kernel-smoothed expectation-maximization (EM), enabling adaptive clustering of dynamic cytometric time-series data. Its key innovation lies in leveraging kernel smoothing to enhance the EM algorithm’s temporal modeling capability, thereby substantially improving clustering stability and biological interpretability. Evaluated on both synthetic datasets and multi-cruise marine field measurements, *flowkernel* accurately reproduces expert manual gating while resolving finer population substructure—outperforming existing automated approaches. The method is implemented as an open-source R package, *flowkernel*, designed for high-throughput identification of phytoplankton subpopulations in ecological and oceanographic applications.

Technology Category

• Machine Learning: Kernel Methods
• Data Mining & Knowledge Management: Data Stream Mining
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

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.

Problem

• Automating gating for flow cytometry phytoplankton data analysis
• Identifying phytoplankton subpopulations using kernel-smoothed EM algorithm
• Replacing manual gating with accurate automated mixture modeling

Innovation

• Automated gating using kernel-smoothed EM algorithm
• Fitting time-evolving Gaussian mixture model for identification
• Efficient implementation via flowkernel R package