An Approach for Data Analysis of Multi-Group Metabonomics Base on Hierarchical-PCA

Abstract:

Datasets of metabolomics of multi-group are becoming increasingly complex, hard to summarize and visualize. Hierarchical Modeling makes the data dimensionality reduction and interpretation much easier by principal component analysis (PCA). Dose-response curve is drawed with the principal component score values. As an example, dataset from Ma Xin Shi Gan Tang (MXSGT) water extract administrated rats plasma collected by LC/MS/MS was used to demonstrate this method. As a result, Hierarchical Modeling based on PCA was proved to be an effective, time saving method for data purification.

• Periodical: Applied Mechanics and Materials (Volumes 236-237)
• Pages: 779-782
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.236-237.779
• Online since: November 2012
• Authors: Li Xin Huang, Yang Li, Qi Yun Zhang, Bing Tao Li, Guang Bin Shang, Yi Zhao, Guo Liang Xu, Bin Nie
• Keywords: Hierarchical, Metabonomics, Principal Component Analysis (PCA)

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