Dailey Allyson L
Department of Chemistry and Biochemistry, George Mason University, 10920 George Mason Circle, MS1A9, Manassas, VA, 20110, USA.
Methods Mol Biol. 2017;1606:341-352. doi: 10.1007/978-1-4939-6990-6_22.
Metabolomics allows for the investigation of the small molecules found within living systems. Based on the design of the experiments, it is not uncommon for these analyses to include matrices of thousands of variables. In order to handle such large datasets, many have turned to multivariate statistical analyses to analyze and understand their data. Herein, we present protocols for using R to analyze metabolomic data using some of the more common multivariate statistical techniques.
代谢组学有助于研究生物系统中发现的小分子。根据实验设计,这些分析中包含数千个变量的矩阵并不罕见。为了处理如此庞大的数据集,许多人已转向多变量统计分析来分析和理解他们的数据。在此,我们介绍使用R运用一些更常见的多变量统计技术来分析代谢组学数据的方案。