Clustering Based on Periodicity in High-Throughput Time Course Data.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Statistical Analysis and Data Mining Pub Date : 2011-12-01 DOI:10.1002/sam.10137

Anna J Blackstock, Amita K Manatunga, Youngja Park, Dean P Jones, Tianwei Yu

引用次数: 2

Abstract

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-hour 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.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于周期性的高通量时间课程数据聚类。

传统上用于分析化学的核磁共振(NMR)光谱学最近被引入到生物液体和组织的代谢物组成的研究中。代谢物水平随着时间的推移而变化，提供一种工具来更好地提取显示周期性行为的NMR峰是有意义的。我们提出了一种基于周期行为的核磁共振峰聚类方法。周期回归是用来获得参数的估计对应于周期的各个核磁共振峰。然后，考虑到回归参数估计的可变性，使用混合模型来开发峰簇。方法应用于24小时内从人血浆中收集的核磁共振数据。仿真研究表明，在聚类过程中应考虑到由于参数估计估计而产生的额外方差分量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.