Select the local CSV file at the top of the page and click Analysis.

Then the page will generate 6 types of diagrams:

t-test plot: The t-test plot assesses the significance of differences in metabolite levels between two groups of samples, giving a visual comparison of whether those differences are significant, and shows the significance of change for each metabolite.

PCA Plot: Principal component analysis (PCA) is a common multivariate method for dimensionality reduction and data visualization. The plot is a Scores Plot of the PCA results: the horizontal and vertical axes represent the first and second principal components (PC1 and PC2), and the bracketed annotations indicate the percentage of variance accounted for by each component; this shows the relative position and variability of samples in the principal component space. The 95% confidence region is drawn in the same color as each group.

ANOVA plots: Analysis of variance (ANOVA) tests whether differences in metabolite levels are significant across three or more groups. The graph is a one-way ANOVA plot that identifies which peaks differ significantly between samples and can serve as metabolite markers; the log transformation compresses the data into a narrower range for easier comparison and analysis.

Heat Map: A heatmap shows the relative intensity and distribution of metabolite abundance differences between groups. It helps researchers identify interactions among metabolites in the metabolic pathway. The color of each cell in the heatmap indicates the expression level of the corresponding metabolite in that sample, with darker colors indicating higher expression.

Volcano: A volcano plot shows the expression levels and variability of metabolites between two groups of samples. Each dot represents a metabolite, and its position reflects its expression level and significance. The darker the color, the greater the variability; the position on the horizontal axis indicates the direction of change, with the right side representing up-regulation and the left side down-regulation.

PLS-DA: Partial least squares discriminant analysis (PLS-DA) is a common multivariate statistical method used to compare metabolite variability across groups, helping researchers discover metabolite interactions and reveal the biological mechanisms of metabolic pathways. The plot shows the variability and similarity of samples along the first component, helping to distinguish metabolite differences between groups. The 95% confidence region is drawn in the same color as each group.

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