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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.
Objective Despite principal components analysis profiles greatly facilitate the visualization and interpretation of the multivariate data, the quantitative concepts in both scores and loading columns are rather obscure. This article introduces 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 shows the discriminatory metabolites between the two groups, while the statistic profile showed the potential markers of statistic significance. Confluence The three types of profiles minimal apt our understanding of the metabolomic data and the identification of the potential markers.