proteomics-data-import

proteomics-data-import

When to use

maxquant is the default; fragpipe, diann and generic normalize their own column names. read_table detects CSV/TSV separation. 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-data-import')
data = library.demo_data(random_state=42)
result = library.standardize(data)
write_output(result, 'tables/proteins.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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read_table(path: str | Path) -> pd.DataFrame

Read a CSV or TSV; pass this function as reader= to read_input.

:param path: Search-engine output file; the first line selects comma or tab separation. :returns: Unmodified input columns as a DataFrame. :raises OSError: The file cannot be read.

standardize(data: pd.DataFrame, *, format: str='maxquant') -> pd.DataFrame

Return a standardized copy of a search-engine protein table.

:param data: Original protein table with engine-specific column names. :param format: CLI default maxquant; fragpipe, diann and generic use their own mappings. :returns: Protein table; MaxQuant reverse, contaminant and site-only flags are filtered. :raises ValueError: The format is unsupported.

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

Read import diagnostics.

:param table: Standardized protein table. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate diagnostic dictionary. :raises TypeError: The input is not a DataFrame.

intensity_figure(table: pd.DataFrame)

Plot numeric column medians for checking an imported table.

:param table: Standardized protein table; numeric metadata is included. :returns: A matplotlib Figure without writing files. :raises TypeError: The input is not a DataFrame.

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

Generate a synthetic MaxQuant table in memory.

:param random_state: CLI seed 42; change for another simulation. :returns: Two hundred protein rows, including five flagged rows. :raises ValueError: The seed is invalid.

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Methods and parameters

maxquant is the default; fragpipe, diann and generic normalize their own column names. read_table detects CSV/TSV separation. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

• standardize filters MaxQuant Reverse, Potential contaminant and Only identified by site flags. Generic import does not infer those flags.
• 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/proteins.csv
• report.md
• result.json
• demo_proteinGroups.txt 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-data-import/proteomics_data_import.py --demo --output /tmp/proteomics_data_import

For real input replace --demo with --input <table>.

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