📐 Metabolomics Normalization

Data normalization, scaling, and transformation for metabolomics feature tables.

CLI Reference

python omicsclaw.py run met-normalize --demo

Why This Exists

• Without it: Run-order effects and instrument drift heavily skew analytical variance
• With it: Mathematical transformations stabilize distributions and correct intrabatch variations
• Why OmicsClaw: Rapid integration of classic techniques (TIC, Median, Pareto) to prepare matrices for statistics

Workflow

1. Calculate: Analyze missing value distribution.
2. Execute: Impute missing entries via localized techniques (kNN, RF).
3. Assess: Apply transformation (Log, Generalized Log) and scaling (Pareto, Auto).
4. Generate: Output structural normalized numerical matrices.
5. Report: Synthesize before/after boxplots of sample variance.

Example Queries

• "Normalize this metabolomics table using QC-RLSC"
• "Log transform and Pareto scale this feature matrix"

Output Structure

output_directory/
├── report.md
├── result.json
├── normalized.csv
├── figures/
│   └── normalization_boxplot.png
├── tables/
│   └── normalization_metrics.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── 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 mapping

• met-diff

  — Downstream statistical execution

Citations

• NOREVA — normalization evaluation