proteobench.modules.quant.quant_lfq_ion_DDA_Astral module#

DDA Quantification Module for Ion level Quantification.

class proteobench.modules.quant.quant_lfq_ion_DDA_Astral.DDAQuantIonAstralModule(token: str, proteobot_repo_name: str = 'Proteobot/Results_quant_ion_DDA_Astral', proteobench_repo_name: str = 'Proteobench/Results_quant_ion_DDA_Astral')[source]#

Bases: QuantModule

DDA Quantification Module for Ion level Quantification.

Parameters:

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

  • proteobot_repo_name (str, optional) – Name of the repository for pull requests and where new points are added, by default “Proteobot/Results_quant_ion_DDA”.

  • proteobench_repo_name (str, optional) – Name of the repository where the benchmarking results will be stored, by default “Proteobench/Results_quant_ion_DDA”.

module_id#

Module identifier for configuration.

Type:

str

precursor_column_name#

Level of quantification.

Type:

str

benchmarking(input_file_loc: any, input_format: str, user_input: dict, all_datapoints: DataFrame, default_cutoff_min_prec: int = 3, input_file_secondary: str = None) → tuple[DataFrame, DataFrame, DataFrame][source]#

Main workflow of the module. Used to benchmark workflow results.

Parameters:

  • input_file_loc (any) – Path to the workflow output file.

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

  • user_input (dict) – User provided parameters for plotting.

  • all_datapoints (pd.DataFrame) – DataFrame containing all datapoints from the proteobench repo.

  • default_cutoff_min_prec (int) – Minimum number of runs an ion has to be identified in.

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

Returns:

Tuple containing the intermediate data structure, all datapoints, and the input DataFrame.

Return type:

tuple[DataFrame, DataFrame, DataFrame]

benchmarking_2(input_file_loc: str, input_format: str, user_input: dict[str, object], all_datapoints: DataFrame, default_cutoff_min_prec: int = 3) → tuple[DataFrame, DataFrame, DataFrame, dict[str, float]][source]#

Main workflow of the module with timing information. Used to benchmark workflow results.

Parameters:

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

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

  • user_input (dict[str, object]) – User provided parameters for plotting.

  • all_datapoints (pd.DataFrame) – DataFrame containing all datapoints from the proteobench repo.

  • default_cutoff_min_prec (int, optional) – Minimum number of runs an ion has to be identified in (default is 3).

Returns:

A 4-tuple containing:

  • intermediate_metric_structure (pd.DataFrame)

  • all_datapoints (pd.DataFrame)

  • input_df (pd.DataFrame)

  • timings (dict of step names to elapsed seconds)

Return type:

tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, dict[str, float]]

get_plot_generator()[source]#

Get the plot generator for LFQ Ion plots.

Returns:

The plot generator instance.

Return type:

PlotGeneratorBase

is_implemented() → bool[source]#

Return whether the module is fully implemented.

Returns:

True if the module is fully implemented, False otherwise.

Return type:

bool

module_id = 'quant_lfq_DDA_ion_Astral'#