/metabolomics-statistics

name: metabolomics-statistics
description: 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 (use metabolomics-xcms-preprocessing); two-group DE with
  default `ctrl` / `treat` prefixes (use metabolomics-de).
trigger: metabolomics statistics, multivariate, PCA, clustering
tags:
- metabolomics
- statistics
- ttest
- wilcoxon
- anova
- kruskal
- bh-fdr

metabolomics-statistics

When to use

Test two explicit sample groups using Welch, ranksums, ANOVA or Kruskal. Use metabolomics-de for the ctrl/treat contrast and PCA CLI.

Use from a step

import pandas as pd
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("metabolomics-statistics")
data = read_input('features.csv', reader=lambda path: pd.read_csv(path, index_col=0))
result = library.test_groups(data, group1_prefix='ctrl', group2_prefix='treat')
write_output(result, 'tables/result.csv')

[examples/example_step.py](examples/example_step.py) runs a seeded synthetic example through the step runner and writes a table and Figure. Computations return new DataFrames, leave the input unchanged and expose diagnostics through `run_info(result)`. Plotting functions write no files.

API

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`test_groups(data, *, method='ttest', alpha=0.05, group1_prefix=None, group2_prefix=None, group1_cols=None, group2_cols=None)`

Return two-group test statistics and BH-adjusted p values.

:param data: Numeric feature-by-sample DataFrame with feature IDs as its index. :param method: CLI default ttest; wilcoxon (ranksums), anova or kruskal also work. :param alpha: CLI default .05; diagnostic significance threshold. :param group1_prefix: CLI default None; prefix selecting the reference samples. :param group2_prefix: CLI default None; prefix selecting the comparison samples. :param group1_cols: Explicit reference columns; supply together with group2_cols. :param group2_cols: Explicit comparison columns; overrides prefix selection. :returns: A new DataFrame with grouping and significance diagnostics. :raises ValueError: A group is empty, overlaps another or is only partly specified.

`run_info(data, *, keep=True)`

Read diagnostics attached to a returned table.

:param data: DataFrame returned by this library. :param keep: Default True; use False in the CLI to remove diagnostics. :returns: An independent dictionary describing the run. :raises ValueError: The table carries no run_info.

`volcano_figure(data)`

Plot group2/group1 log2 fold change against BH-adjusted significance.

:param data: Results from test_groups. :returns: A matplotlib Figure. :raises KeyError: log2fc or fdr is absent.

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Methods and parameters

The wilcoxon label calls scipy.stats.ranksums, not paired Wilcoxon or Mann-Whitney U. ANOVA and Kruskal accept exactly two groups here. BH FDR covers every returned feature.

Gotchas

• `test_groups` falls back to midpoint grouping with a warning when both prefixes are not supplied; run_info records the columns. `tables/significant.csv` uses fdr < alpha. log2fc is group2/group1.

Inputs and outputs

CSV input; `tables/statistics.csv`, `report.md` and `result.json`. The CLI also writes `tables/significant.csv`. The CLI writes `reproducibility/commands.sh`. The function library returns objects; the CLI and step own file writes.

CLI

python skills/metabolomics/metabolomics-statistics/metabolomics_statistics.py --demo --output /tmp/metabolomics_statistics

See also

• [Parameters](references/parameters.md)
• [Methodology](references/methodology.md)
• [Output contract](references/output_contract.md)

Dependencies

`numpy`, `pandas`, `scipy`, `matplotlib`