Nuclear magnetic resonance (NMR) spectroscopy, traditionally used in analytical chemistry, has recently been introduced to studies of metabolite composition of biological fluids and tissues. Metabolite levels change over time, and providing a tool for better extraction of NMR peaks exhibiting periodic behavior is of interest. We propose a method in which NMR peaks are clustered based on periodic behavior. Periodic regression is used to obtain estimates of the parameter corresponding to period for individual NMR peaks. A mixture model is then used to develop clusters of peaks, taking into account the variability of the regression parameter estimates. Methods are applied to NMR data collected from human blood plasma over a 24‐h period. Simulation studies show that the extra variance component due to the estimation of the parameter estimate should be accounted for in the clustering procedure. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 579–589, 2011

中文翻译：

---

基于高吞吐量时间过程数据中周期性的聚类。

核磁共振 (NMR) 光谱，传统上用于分析化学，最近已被引入生物体液和组织的代谢物组成的研究。代谢物水平随时间变化，提供一种工具来更好地提取表现出周期性行为的 NMR 峰是令人感兴趣的。我们提出了一种基于周期性行为对 NMR 峰进行聚类的方法。周期回归用于获得对应于各个 NMR 峰周期的参数估计值。然后使用混合模型来开发峰值簇，同时考虑到回归参数估计的可变性。方法适用于在 24 小时内从人血浆中收集的 NMR 数据。模拟研究表明，在聚类过程中应该考虑由于参数估计的估计而产生的额外方差分量。© 2011 Wiley Periodicals, Inc. 统计分析和数据挖掘 4: 579–589, 2011