Analyzing and presenting data from multiple groups are much more informative than that from two groups. However, common tools such as S plot and volcano plot are only available for identifying the significant features between two groups and are restricted to multiple-group comparisons. This study proposed novel visualization plots which not only overcame the restrictions of the above methods but also utilized the p values of multiple tests as the x-axis. The novel visualization plots included a parametric method and a nonparametric method. The parametric method was a combination of an analysis of variance and Welch's analysis of variance; the nonparametric method used the Kruskal-Wallis test. During the selection of significant features, machine learning algorithms were used to determine the cutting points of the x-axis. As a proof of concept, the real data from the experiments of 4-MeO-α-PVP metabolites and fish spoilage metabolomics were illustrated via our visualization method. The results showed that the novel visualization plots were much efficiently presented to identify significant metabolites in multiple-group comparisons. Especially, the positive predicted values of the nonparametric method and the cutting points determined by logistic regression were higher than those of other machine learning algorithms in determining the cutting points for multiple groups.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry
- Process Chemistry and Technology
- Computer Science Applications