Change Point Detection for Cell Populations Measured via Flow Cytometry

2026-03-05

AI Summary

This study addresses the challenge of detecting abrupt shifts—change points—in phytoplankton population distributions within marine flow cytometry data, which arise from environmental responses. The authors propose a latent-space Gaussian mixture of experts model that captures population structure through low-dimensional latent representations. By incorporating group fused LASSO regularization, the method enforces piecewise-constant changes in cluster means, and an ADMM algorithm is employed for efficient optimization. As the first work to integrate mixture-of-experts modeling with change-point detection, this approach effectively handles replicated, clustered single-cell flow cytometry data. Applied to real-world oceanic datasets, the method successfully identifies key change points that align closely with transitions between major ecological provinces, demonstrating both biological relevance and practical utility.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Data transparency and provenance

Abstract

The ocean is filled with phytoplankton that contribute as much photosynthesis as all land plants combined, making them vital to the carbon cycle and climate system. Recent advances in flow cytometry allow oceanographers to measure the optical traits of individual cells along research cruise tracks, generating single-cell resolution microbial data. In marine microbial ecology, detecting locations of abrupt changes in the environmental response of cytometric plankton distributions is an important task. This manuscript proposes a latent space Gaussian mixture-of-experts model, facilitating change point detection in replicated and clustered phytoplankton observations. Change points are identified through shifts in prior means of low-dimensional representations, with piecewise-constant structure enforced by a group-fused LASSO penalty. The optimization problem is then solved via Alternating Direction Method of Multipliers. Applied to flow cytometry data, the proposed method identifies a scientifically important change point that aligns with a transition zone between two marine provinces.

Problem

• Change Point Detection
• Flow Cytometry
• Phytoplankton
• Marine Microbial Ecology
• Environmental Response

Innovation

• change point detection
• Gaussian mixture-of-experts
• latent space
• group-fused LASSO
• flow cytometry