proteobench.modules.quant.quant_lfq_ion_DIA_AIF module#

DIA Quantification Module for precursor level Quantification.

class proteobench.modules.quant.quant_lfq_ion_DIA_AIF.DIAQuantIonModuleAIF(token: str, proteobot_repo_name: str = 'Proteobot/Results_quant_ion_DIA', proteobench_repo_name: str = 'Proteobench/Results_quant_ion_DIA')[source]#

Bases: QuantModule

DIA Quantification Module for precursor level Quantification.

Parameters:

  • token (str) – GitHub token for the user.

  • proteobot_repo_name (str) – Name of the repository for pull requests and where new points are added.

  • proteobench_repo_name (str) – Name of the repository where the benchmarking results will be stored.

module_id#

Module identifier for configuration.

Type:

str

precursor_column_name#

Level of quantification.

Type:

str

benchmarking(input_file: str, input_format: str, user_input: dict, all_datapoints: DataFrame | None, default_cutoff_min_prec: int = 3, input_file_secondary: str = None) → Tuple[DataFrame, DataFrame, DataFrame][source]#

Main workflow of the module for benchmarking workflow results.

Parameters:

  • input_file (str) – Path to the workflow output file.

  • input_format (str) – Format of the workflow output file.

  • user_input (str) – User-provided parameters for plotting.

  • all_datapoints (Optional[pd.DataFrame])) – DataFrame containing all data points from the repo.

  • default_cutoff_min_prec (int, optional) – Minimum number of runs an precursor must be identified in. Defaults to 3.

  • input_file_secondary (str, optional) – Path to a secondary input file (used for some formats like AlphaDIA).

Returns:

A tuple containing the intermediate data structure, all data points, and the input DataFrame.

Return type:

Tuple[DataFrame, DataFrame, DataFrame]

is_implemented() → bool[source]#

Return whether the module is fully implemented.

Returns:

Whether the module is fully implemented.

Return type:

bool

module_id: str = 'quant_lfq_DIA_ion_AIF'#