metabolomics-de

metabolomics-de

When to use

Run Welch tests and treatment/control fold changes using ctrl/treat sample prefixes. Use metabolomics-statistics for another test backend.

Use from a step

import pandas as pd
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("metabolomics-de")
data = read_input('features.csv', reader=pd.read_csv)
result = library.differential_expression(data)
write_output(result, 'tables/result.csv')

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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differential_expression(data, *, group_a_prefix='ctrl', group_b_prefix='treat')

Return Welch tests, treatment/control log2 fold changes and BH FDR.

:param data: Feature table whose first column identifies features; other columns contain intensities. :param group_a_prefix: CLI default ctrl; prefix selecting control samples. :param group_b_prefix: CLI default treat; prefix selecting treatment samples. :returns: A new differential table with group sizes in attrs['run_info']. :raises ValueError: A group is absent or groups overlap.

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.

pca_figure(data, *, group_a_prefix='ctrl', group_b_prefix='treat', random_state=0)

Plot sample PCA from untransformed intensities; NaNs become zero.

:param data: Feature table with the same sample columns as differential_expression. :param group_a_prefix: CLI default ctrl; control prefix. :param group_b_prefix: CLI default treat; treatment prefix. :param random_state: Default 0; seed forwarded to sklearn PCA. :returns: A matplotlib Figure with sample labels and explained variance axes. :raises ImportError: Install scikit-learn with install_skill_deps if unavailable. :raises ValueError: Groups are absent, overlap or the matrix is invalid.

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Methods and parameters

Welch tests use raw supplied intensities, with BH FDR and a fixed CLI significance threshold of 0.05. PCA uses untransformed intensities and converts NaNs to zero.

Gotchas

• differential_expression treats the first column as feature IDs. Both groups must be nonempty and disjoint. tables/significant_features.csv uses fdr < 0.05. The CLI logs optional PCA failures.

Inputs and outputs

CSV input; tables/differential_features.csv, report.md and result.json. The CLI also writes tables/significant_features.csv and, when PCA succeeds, figures/pca_scores.png. Demo mode also writes its synthetic input CSV at the output root. The function library returns objects; the CLI and step own file writes.

CLI

python skills/metabolomics/metabolomics-de/met_diff.py --demo --output /tmp/metabolomics_de

See also

• Parameters
• Methodology
• Output contract

Dependencies

numpy, pandas, scipy, scikit-learn, matplotlib