Change Point Detection for Cell Populations Measured via Flow Cytometry

Fuente: arXiv
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Autori principali: Kei, Yik Lun, Wang, Qi, Parker, Paul, Ribalet, Francois, Hyun, Sangwon
Natura: Preprint
Pubblicazione: 2026
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author Kei, Yik Lun
Wang, Qi
Parker, Paul
Ribalet, Francois
Hyun, Sangwon
author_facet Kei, Yik Lun
Wang, Qi
Parker, Paul
Ribalet, Francois
Hyun, Sangwon
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_05700
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Change Point Detection for Cell Populations Measured via Flow Cytometry
Kei, Yik Lun
Wang, Qi
Parker, Paul
Ribalet, Francois
Hyun, Sangwon
Methodology
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.
title Change Point Detection for Cell Populations Measured via Flow Cytometry
topic Methodology
url https://arxiv.org/abs/2603.05700