proteomics-enrichment

proteomics-enrichment

When to use

ORA uses one-sided Fisher tests and BH correction. Pass an explicit pathway_db and a background_size matching the measured universe. Use existing search-engine tables; this skill does not search raw spectra.

Use from a step

from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-enrichment')
data = library.demo_data(random_state=42)
result = library.enrich(data['protein_id'].tolist(), pathway_db=library.demo_pathways())
write_output(result, 'tables/enrichment_results.csv')

For real data, use read_input and pass any read_table helper as reader=. The executable examples/example_step.py also checks the result and writes a Figure.

API

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enrich(genes: list[str], *, pathway_db: dict | None=None, background_size: int | None=None, method: str='ora') -> pd.DataFrame

Run one-sided Fisher tests with BH correction against an explicit pathway library.

:param genes: Protein or gene identifiers in the same identifier space as pathway_db. :param pathway_db: Required pathway-to-member mapping; no default biological database is assumed. :param background_size: CLI default None uses the input/library union plus at least one background-only member. :param method: CLI default ora is the only implemented method. :returns: Ranked pathway overlaps, odds ratios and adjusted p values. :raises ValueError: The library is absent, the method is invalid, or the background is too small.

run_info(table: pd.DataFrame, *, keep: bool=True) -> dict

Read enrichment diagnostics.

:param table: Output of enrich. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with library provenance and summary. :raises TypeError: The input is not a DataFrame.

enrichment_figure(table: pd.DataFrame, *, top_n: int=10)

Plot leading pathways by enrichment ratio.

:param table: Enrichment results sorted by p value. :param top_n: Display ten pathways by default; change for a larger result table. :returns: A matplotlib Figure without writing files. :raises KeyError: pathway or enrichment_ratio is absent.

demo_data(*, random_state: int=42) -> pd.DataFrame

Generate a synthetic significant-protein list.

:param random_state: CLI seed 42; change for another simulation. :returns: Eighteen example identifiers with simulated statistics. :raises ValueError: The seed is invalid.

demo_pathways() -> dict

Return the eight small illustrative pathway sets used by --demo.

:returns: A separate mapping, not a production pathway database. :raises RuntimeError: No runtime failures are expected.

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Methods and parameters

ORA uses one-sided Fisher tests and BH correction. Pass an explicit pathway_db and a background_size matching the measured universe. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

• enrich requires pathway_db. demo_pathways contains eight illustrative sets and is only for demonstrations. The CLI requires --pathways for non-demo input; --species is recorded only.
• demo_data uses seed 42, matching the CLI; every demo is synthetic.
• run_info lives in DataFrame attrs and is not preserved by CSV serialization.

Inputs and outputs

The CLI reads CSV tables and writes:

• tables/enrichment_results.csv
• report.md
• result.json
• demo_proteins.csv is written only with --demo.

Functions return data and Figures without writing files. Steps own their outputs. Demo mode also writes its synthetic input when the original CLI used a file.

CLI

python skills/proteomics/proteomics-enrichment/prot_enrichment.py --demo --output /tmp/proteomics_enrichment

For real input replace --demo with --input <table>. Non-demo enrichment also requires --pathways <pathways.json>, a pathway-to-members object.

See also

• references/methodology.md
• references/parameters.md
• references/output_contract.md
• proteomics-data-import for protein-table normalization; proteomics-de for comparisons.

Dependencies

numpy, pandas, matplotlib, scipy