tooluniverse-metabolomics-analysis

What it does

Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.

How it works

Follows a phased workflow:

• Phase 1: Data Import & Metabolite Identification
  • Load data in peak_table, mzml, or nmr format; identify metabolites by matching features to HMDB, KEGG, PubChem with confidence scoring; support mass tolerance of 0.005 (5 ppm).
• Phase 2: Quality Control & Filtering
  • Compute QC metrics (CV in QC samples, blank ratios), remove low-quality features, and handle missing values and blank subtraction.
• Phase 3: Normalization
  • Apply TIC, PQN, or internal standard normalization; include batch effect correction and optional log-transform or scaling; provide internal standard normalization method using a defined metabolite column.
• Phase 4: Exploratory Analysis
  • Perform PCA for sample clustering and generate related plots; provide PLS-DA for supervised dimensionality reduction.
• Phase 5: Differential Metabolite Analysis
  • Conduct statistical tests (t-test, ANOVA, Wilcoxon), compute fold changes and log2FC, apply multiple testing correction, and generate volcano plots and heatmaps.
• Phase 6: Pathway Analysis
  • Conduct metabolite set enrichment (MSEA) and pathway mapping to KEGG/Reactome, identify dysregulated pathways; support integration with pathway context.
• Phase 7: Multi-Omics Integration
  • Correlate metabolite data with enzyme expression (RNA/protein) and perform pathway-level integration and metabolic flux inference.
• Phase 8: Generate Report
  • Compile summary statistics, differential metabolites, pathway diagrams, multi-omics plots, and biomarker panels.

When to use it

Used when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.

What it can touch

The workflow includes data import, metabolite identification, QC, normalization, statistical analysis, differential analysis, pathway analysis, multi-omics integration, and report generation as described in the phases.

Caveats

License is NOASSERTION; the approach relies on database matching (HMDB, KEGG, PubChem) and statistical methods that require proper data quality and experimental design. The provided excerpt includes example code and function outlines but does not guarantee results beyond the stated procedures.