metabolomics-normalization

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Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Skip when also imputing (use `metabolomics-quantification`) or for raw spectra (run `metabolomics-xcms-preprocessing` first).

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/metabolomics/metabolomics-normalization/SKILL.md

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metabolomics-normalization

When to use

The user has a feature × sample metabolomics intensity table and wants normalisation only (no imputation). Five methods:

median (default) — divide each sample by its median.
quantile — quantile normalisation across samples.
total — divide by per-sample total (TIC).
pqn — Probabilistic Quotient Normalisation (Dieterle 2006).
log — log2(x+1) per-cell.

For combined imputation + normalisation use metabolomics-quantification.

Inputs & Outputs

Input	Format	Required
Feature × intensity table	`.csv` (wide form: rows = features, columns = samples)	yes (unless `--demo`)
Method	`--method {median,quantile,total,pqn,log}` (default `median`)	no

Output	Path	Notes
Normalised table	`tables/normalized.csv`	wide form, same shape as input
Report	`report.md` + `result.json`	`n_features`, `n_samples`, `method`

Flow

Load CSV (--input <features.csv>) or generate a demo (--demo).
Dispatch on --method; reject unknown via ValueError("Unknown method: {method}. Choose from {SUPPORTED_METHODS}") at metabolomics_normalization.py:151.
Apply the chosen normalisation; write tables/normalized.csv (metabolomics_normalization.py:258) + report.md + result.json.

Gotchas

--method choices are exact: median / quantile / total / pqn / log. metabolomics_normalization.py:36 defines SUPPORTED_METHODS. Aliases like tic (= total) are NOT accepted — pass total explicitly. (Note: sibling metabolomics-quantification accepts tic as a normalize choice; the two skills' vocabularies differ.)
--input REQUIRED unless --demo. metabolomics_normalization.py:248 raises ValueError("--input required when not using --demo").
pqn requires non-zero reference values. Probabilistic Quotient Normalisation divides by per-feature reference (median sample); features with all zeros yield NaN quotients. Pre-filter zero-prevalent features.
log is log2(x+1). Negative values raise / propagate NaN. Pre-clip upstream.
No imputation is performed. NaN values pass through normalisation untouched (most methods skipna; quantile may NaN-propagate). Pre-impute with metabolomics-quantification if NaNs are problematic.
Method-specific behaviour with NaN may differ. median / total use np.nanmedian / np.nansum; quantile may collapse rows with NaN; pqn expects all-numeric.

Key CLI

# Demo (median normalize)
python omicsclaw.py run metabolomics-normalization --demo --output /tmp/norm_demo

# PQN
python omicsclaw.py run metabolomics-normalization \
  --input features.csv --output results/ --method pqn

# Total (TIC)
python omicsclaw.py run metabolomics-normalization \
  --input features.csv --output results/ --method total

# log2(x+1)
python omicsclaw.py run metabolomics-normalization \
  --input features.csv --output results/ --method log

See also

references/parameters.md — every CLI flag
references/methodology.md — per-method semantics, when each wins
references/output_contract.md — tables/normalized.csv schema
Adjacent skills: metabolomics-quantification (parallel — combined impute + normalise), metabolomics-xcms-preprocessing (upstream), metabolomics-peak-detection (upstream), metabolomics-statistics (downstream — multi-group testing), metabolomics-de (downstream — two-group DE)