alphabase.pg_reader.fragpipe_pg_reader

FragPipe protein group reader.

Classes:

FragPipePGReader(*[, column_mapping, ...])

Reader for protein.tsv reports from FragPipe.

class alphabase.pg_reader.fragpipe_pg_reader.FragPipePGReader(*, column_mapping: dict[str, str] | None = None, measurement_regex: Literal['raw', 'razor', 'unique', 'total', 'lfq', 'lfq_unique', 'lfq_total'] | None = 'razor')[source][source]

Bases: PGReaderBase

Reader for protein.tsv reports from FragPipe.

Example:

Per default, the reader will return the raw intensities from the razor method. Additional protein features are stored in the dataframe index, samples are stored as columns.

# Get raw intensities
reader = FragPipePGReader()
results = reader.import_file(download_path)

References:

Methods:

__init__(*[, column_mapping, measurement_regex])

Read protein group (PG) matrices into the standardized alphabase format.

__init__(*, column_mapping: dict[str, str] | None = None, measurement_regex: Literal['raw', 'razor', 'unique', 'total', 'lfq', 'lfq_unique', 'lfq_total'] | None = 'razor')[source][source]

Read protein group (PG) matrices into the standardized alphabase format.

Parameters:
  • column_mapping – A dictionary of mapping alphabase columns (keys) to the corresponding columns in the other search engine (values). If None will be loaded from the column_mapping key of the respective search engine in pg_reader.yaml

  • measurement_regex – Regular expression that identifies correct measurement type. Only relevant if PG matrix contains multiple measurement types. For example, alphapept returns the raw protein intensity per sample in column A and the LFQ corrected value in A_LFQ. If None uses all columns.

column_mapping

Dictionary structure mapping alphabase columns (keys) to the corresponding columns in the other search engine (values), see parameters.

measurement_regex

Regular expression that matches quantity of interest for all samples

Notes

Standardizes protein group reports to a protein group dataframe (features x samples) in wide format. Contains at least

Additional feature-level metadata might be available in the index.