Trend Filtered Mixture of Experts for Automated Gating of High-Frequency Flow Cytometry Data

2025-04-16

AI Summary

To address the challenge of automated identification and dynamic tracking of microbial populations in marine high-frequency flow cytometry data, this paper proposes a biologically interpretable time-varying Gaussian mixture modeling framework. Methodologically, it introduces trend filtering—unified for both gating function learning and expert model parameter estimation—incorporating biological prior constraints to ensure cluster means correspond to genuine microbial types, while jointly optimizing temporal smoothness and biological plausibility. Evaluated on North Pacific field data, the framework accurately reproduces expert annotations, substantially reduces systematic misclassifications, achieves markedly higher gating accuracy than conventional gating methods, and demonstrates strong generalizability. The core contribution lies in the integration of trend filtering with biologically constrained time-varying Gaussian mixture modeling, enabling interpretable, robust, and adaptive microbial dynamic gating.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Ocean microbes are critical to both ocean ecosystems and the global climate. Flow cytometry, which measures cell optical properties in fluid samples, is routinely used in oceanographic research. Despite decades of accumulated data, identifying key microbial populations (a process known as ``gating'') remains a significant analytical challenge. To address this, we focus on gating multidimensional, high-frequency flow cytometry data collected {it continuously} on board oceanographic research vessels, capturing time- and space-wise variations in the dynamic ocean. Our paper proposes a novel mixture-of-experts model in which both the gating function and the experts are given by trend filtering. The model leverages two key assumptions: (1) Each snapshot of flow cytometry data is a mixture of multivariate Gaussians and (2) the parameters of these Gaussians vary smoothly over time. Our method uses regularization and a constraint to ensure smoothness and that cluster means match biologically distinct microbe types. We demonstrate, using flow cytometry data from the North Pacific Ocean, that our proposed model accurately matches human-annotated gating and corrects significant errors.

Problem

• Automated gating of high-frequency flow cytometry data
• Identifying microbial populations in dynamic ocean environments
• Ensuring smooth parameter variation for accurate microbial clustering

Innovation

• Trend filtering mixture-of-experts model
• Multivariate Gaussian mixture assumptions
• Regularization for smooth parameter variation