metabolomics-statistics

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Load when running univariate two-group testing (t-test / Wilcoxon / ANOVA / Kruskal-Wallis) on a feature × sample metabolomics CSV with `--group1-prefix` / `--group2-prefix` column matching, BH-FDR adjusted. Skip when working with raw spectra (run `metabolomics-xcms-preprocessing`) or for two-group DE with default `ctrl` / `treat` prefixes (use `metabolomics-de`).

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

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metabolomics-statistics

When to use

The user has a wide feature × sample CSV (rows = features as index, columns = samples) and wants univariate two-group testing. Four backends:

ttest (default) — Welch's two-sample t-test.
wilcoxon — Mann-Whitney U (non-parametric).
anova — one-way ANOVA (two-group case ≡ equal-variance t-test).
kruskal — Kruskal-Wallis (non-parametric ANOVA).

--group1-prefix / --group2-prefix select sample columns by prefix; without them the script splits at column-midpoint with a warning. Significance threshold via --alpha (default 0.05); BH-FDR adjusted.

For metabolomics-DE with default ctrl / treat column prefixes use metabolomics-de. For raw spectra use metabolomics-xcms-preprocessing.

Inputs & Outputs

Input	Format	Required
Feature × sample table	`.csv` with feature index in column 0 + sample columns	yes (unless `--demo`)
Method	`--method {ttest,anova,wilcoxon,kruskal}` (default `ttest`)	no
Significance	`--alpha <float>` (default 0.05)	no
Group columns	`--group1-prefix <str>` and `--group2-prefix <str>` (else midpoint split)	no

Output	Path	Notes
Full results	`tables/statistics.csv`	per-feature columns: `feature`, `group1_mean`, `group2_mean`, `fold_change`, `log2fc`, `statistic`, `pvalue`, `fdr` (BH-adjusted)
Significant subset	`tables/significant.csv`	filtered by `--alpha`
Report	`report.md` + `result.json`	`summary["method"]`, `summary["n_significant"]`

Flow

Load CSV with pd.read_csv(args.input_path, index_col=0) (metabolomics_statistics.py:325).
If both --group1-prefix and --group2-prefix are set, filter columns by c.startswith(prefix) (metabolomics_statistics.py:330-331); else fall back to midpoint split with a warning (:333-340).
If either group is empty, raise ValueError("Could not determine group columns. ...") at :344.
Dispatch on --method (:209 rejects unknown with ValueError); per-feature test → pvalue + BH-adjusted fdr.
Filter fdr < args.alpha → tables/significant.csv (:363).
Write tables/statistics.csv (metabolomics_statistics.py:360) + report + result.json.

Gotchas

Group prefixes are OPTIONAL with midpoint fallback. metabolomics_statistics.py:329-340 only honours --group1-prefix / --group2-prefix when BOTH are passed; missing one or both falls back to midpoint split (first half / second half) with a warning. Always pass BOTH for explicit group control.
Empty group ⇒ ValueError. metabolomics_statistics.py:344 raises if either group's column list is empty (e.g. typo in prefix). Sanity-check --group1-prefix / --group2-prefix against your column names.
Index column 0 is the feature ID. pd.read_csv(args.input_path, index_col=0) (:325) is unconditional — make sure your feature-ID column is the FIRST column in the CSV.
anova = equal-variance t-test in the two-group case (metabolomics_statistics.py:138-140). For more than two groups, this skill silently assumes two — extend group_cols lists or use a different tool for true multi-group ANOVA.
wilcoxon here is Mann-Whitney U (independent samples), NOT paired Wilcoxon signed-rank. Don't use it for paired designs.
--input REQUIRED unless --demo. metabolomics_statistics.py:324 raises ValueError("--input required when not using --demo").
log2FC direction depends on group order. group2_mean - group1_mean convention; pass groups in the right order.

Key CLI

# Demo
python omicsclaw.py run metabolomics-statistics --demo --output /tmp/stats_demo

# Real CSV with explicit group prefixes
python omicsclaw.py run metabolomics-statistics \
  --input features_quant.csv --output results/ \
  --method ttest --alpha 0.05 \
  --group1-prefix control_ --group2-prefix treated_

# Wilcoxon (non-parametric)
python omicsclaw.py run metabolomics-statistics \
  --input features_quant.csv --output results/ \
  --method wilcoxon --group1-prefix WT --group2-prefix KO

See also

references/parameters.md — every CLI flag
references/methodology.md — per-method assumptions, BH FDR
references/output_contract.md — tables/statistics.csv schema
Adjacent skills: metabolomics-de (parallel — pre-set ctrl / treat prefixes), metabolomics-quantification (upstream — impute + normalise), metabolomics-normalization (upstream — normalisation only), metabolomics-pathway-enrichment (downstream — pathway analysis on significant features)