Metabolomics Quantification

Workflow

1. Calculate: Assess inherent missing value distributions per feature.
2. Execute: Impute empty values using the user-defined algorithm (KNN, Min, Median).
3. Assess: Apply normalization logic (TIC, MAD) to align global gradients.
4. Generate: Output structural completed data matrices.
5. Report: Produce imputation QC boxplots before and after correction.

Example Queries

• "Impute missing values using KNN"
• "Normalize this feature table with TIC"

Output Structure

output_directory/
├── report.md
├── result.json
├── quantified.csv
├── figures/
│   └── imputation_boxplot.png
├── tables/
│   └── imputed_matrix.csv
└── reproducibility/
    ├── commands.sh
    ├── requirements.txt
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• peak-detection — Upstream raw data matrix creation
• met-diff — Downstream univariate/multivariate testing

Citations

• NOREVA — normalization evaluation