📏 Metabolomics Quantification

Feature quantification with missing value imputation (min/median/KNN) and normalization (TIC/median/log).

CLI Reference

python omicsclaw.py run met-quantify --demo
python omicsclaw.py run met-quantify --input <features.csv> --output <dir>

Parameters

--impute

min

--normalize

tic

Why This Exists

• Without it: Downstream models crash when encountering missing LC/MS peak values
• With it: Recovers matrix completeness via K-Nearest Neighbors (KNN) or Median Imputation
• Why OmicsClaw: Centralized, reproducible preprocessing steps tailored for sparse metabolomic data

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
    ├── 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 matrix creation

• met-diff

  — Downstream univariate/multivariate testing

Citations

• NOREVA — normalization evaluation