Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.
复制标题
通过混合建模对斑马鱼脑钙图像的时间序列数据进行全体积聚类。
DOI：
10.1002/sam.11366
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发表时间：
2018
影响因子：
1.3
通讯作者：
Janke,AndrewL
中科院分区：
文献类型：
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作者：
Nguyen,HienD;Ullmann,JeremyFP;McLachlan,GeoffreyJ;Voleti,Venkatakaushik;Li,Wenze;Hillman,ElizabethMC;Reutens,DavidC;Janke,AndrewL
钙是神经信号事件中普遍存在的信使。越来越多的技术通过与钙离子结合的发光蛋白来可视化动物模型中的神经活动。这些技术产生了大量空间相关的时间序列。本文提出了一种基于模型的基于高斯混合的函数数据分析方法，用于对这类可视化数据进行聚类。该方法在理论上是合理的，并提出了一种计算上有效的估计方法。给出了斑马鱼成像实验的实例分析。
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model‐based functional data analysis methodology via Gaussian mixtures for clustering of data from such visualizations is proposed in this paper. The methodology is theoretically justified, and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.