proteobench.score.quantscores module#

Module containing quantification score calculators.

class proteobench.score.quantscores.QuantScoresHYE(precursor_column_name: str, species_expected_ratio, species_dict: Dict[str, str])[source]#

Bases: ScoreBase

Class for computing quantification scores for LFQ benchmarking.

This class implements the ScoreBase interface to compute quantification-specific metrics including condition statistics, fold changes, and epsilon (difference) values.

Parameters:

  • precursor_column_name (str) – Name of the precursor column.

  • species_expected_ratio (dict) – Dictionary containing the expected ratios for each species.

  • species_dict (dict) – Dictionary containing the species names and their column mappings.

static compute_condition_stats(relevant_columns_df: DataFrame, min_intensity=0, precursor='precursor ion') → DataFrame[source]#

Method used to precursor statistics, such as number of observations, CV, mean per condition etc.

Parameters:

  • relevant_columns_df (pd.DataFrame) – DataFrame containing the relevant columns for the statistics.

  • min_intensity (int, optional) – Minimum intensity value to filter for. Defaults to 0.

  • precursor (str, optional) – Name of the precursor column. Defaults to “precursor ion.

Returns:

DataFrame containing the precursor statistics.

Return type:

pd.DataFrame

static compute_epsilon(withspecies, species_expected_ratio) → DataFrame[source]#

Compute epsilon for each species in species_expected_ratio.

Parameters:

  • withspecies (pd.DataFrame) – DataFrame containing the species columns and the log2_A_vs_B column.

  • species_expected_ratio (dict) – Dictionary containing the expected ratios for each species.

Returns:

DataFrame containing the epsilon values.

Return type:

pd.DataFrame

static convert_replicate_to_raw(replicate_to_raw: dict) → DataFrame[source]#

Convert replicate_to_raw dictionary into a dataframe.

Parameters:

replicate_to_raw (dict) – Dictionary containing the replicate to raw mapping.

Returns:

DataFrame containing the replicate to raw mapping.

Return type:

pd.DataFrame

generate_intermediate(filtered_df: DataFrame, replicate_to_raw: dict) → DataFrame[source]#

Generate intermediate data structure for quantification scores.

Parameters:

  • filtered_df (pd.DataFrame) – DataFrame containing the filtered data.

  • replicate_to_raw (dict) – Dictionary containing the replicate to raw mapping.

Returns:

DataFrame containing the intermediate data structure.

Return type:

pd.DataFrame