Metabolomics Normalization

---
name: metabolomics-normalization
description: >-
  Metabolomics data normalization, scaling and transformation.
version: 0.1.0
author: OmicsClaw
license: MIT
tags: [metabolomics, normalization, scaling]
metadata:
  omicsclaw:
    domain: metabolomics
    emoji: "📐"
    trigger_keywords: [metabolomics normalization, scaling, NOREVA, TIC normalization]
    allowed_extra_flags: []
    legacy_aliases: [met-normalize]
    saves_h5ad: false
---

# 📐 Metabolomics Normalization

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

## CLI Reference

```bash
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
    ├── 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 mapping
- `met-diff` — Downstream statistical execution

## Citations

- [NOREVA](https://doi.org/10.1093/nar/gkx449) — normalization evaluation