OPL公司
代谢组学
可视化
化学计量学
主成分分析
绘图(图形)
化学
偏最小二乘回归
多元统计
普鲁克分析
色谱法
人工智能
模式识别(心理学)
数据挖掘
生物系统
计算机科学
机器学习
统计
数学
计算化学
分子动力学
水模型
生物
作者
Susanne Wiklund,Erik Johansson,Lina Sjöström,Ewa J. Mellerowicz,Ulf Edlund,John P. Shockcor,Johan Gottfries,Thomas Möritz,Johan Trygg
摘要
Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.
科研通智能强力驱动
Strongly Powered by AbleSci AI