Objective Although principal components analysis profiles greatly facilitate the visualization and interpretation of the multivariate data, the quantitative concepts in both scores plot and loading plot are rather obscure. This article introduced three profiles that assisted the better understanding of metabolomic data. Methods The discriminatory profile, heat map, and statistic profile were developed to visualize the multivariate data obtained from high-throughput GC-TOF-MS analysis. Results The discriminatory profile and heat map obviously showed the discriminatory metabolites between the two groups, while the statistic profile showed the potential markers of statistic significance. Conclusion The three types of profiles greatly facilitate our understanding of the metabolomic data and the identification of the potential markers.
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Project Supported:
the National Key New Drug Creation Special Programs (2009ZX09304-001 and 2009ZX09502-004); National Natural Science Foundation of the People’s Republic of China (81072692); National Key Fundamental Research “973” Projects (2011CB505300 and 2011CB505303)
ZHOU Jun, AA Ji-ye, WANG Guang-ji, ZHANG Feng-yi, GU Rong-rong, WANG Xin-wen, ZHAO Chun-yan, LI Meng-jie, SHI Jian, CAO Bei, ZHENG Tian, LIU Lin-sheng, GUO Sheng, DUAN Jin-ao. Visualization of Multivariate Metabolomic Data[J]. Chinese Herbal Medicines (CHM),2011,3(4):285-289
