Statistics Toolbox for Study: ST003036

Title: Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa - Part 2

Select a dataset:

HILIC POSITIVE ION MODE HILIC NEGATIVE ION MODE

Run analyses on data in Study ST003036 Dataset: HILIC POSITIVE ION MODE

Metabolite structures and classes (all analyses combined)

• Pie chart of metabolite super classes
• Pie chart of metabolite main classes
• Pie chart of metabolite sub classes
• Molecular structure similarity analysis

Normalization and averaging

• Perform sample normalization / Show metabolite averages / Run cluster analysis
• Perform analyte scaling on data
• Create Relative log abundance plots

Univariate analysis

• Perform multi-condition dot plot analysis
• Perform Volcano plot analysis
• Perform ANOVA analysis

Clustering and correlation

• Perform hierarchial or heatmap cluster analysis
• Perform Clustered correlation analysis
• Perform Network analysis on correlated metabolites (mapped to classification)
• Perform Network analysis on correlated metabolites (mapped to fold-change)

Multivariate analysis

• Perform Principal component analysis
• Perform Linear discriminant analysis
• Perform Partial least-squares discriminant analysis (PLS-DA)

Classification and feature analysis

• Perform OPLS-DA and VIP projection
• Random Forest and VIP projection

MetaBatch Omic Browser (MD Anderson Cancer Center)

(Clustered Heat Maps, PCA+, UMAP, box plot, violin plot, and other visualizations)

• Load this study
• Load this analysis (AN004977)

MetENP: Metabolite enrichment and species-specific pathway annotation

• MetENPWeb analysis
• MetENP R package
• MetENP tutorial

Mapping metabolites to human biochemical pathways

• Not applicable (non-mammalian)