proteobench.datapoint.entrapment_datapoint module

This module provides functionality for handling and processing quantitative datapoints in the ProteoBench framework.

class proteobench.datapoint.entrapment_datapoint.EntrapmentDatapoint(id: str = None, software_name: str = None, software_version: int = 0, search_engine: str = None, search_engine_version: int = 0, ident_fdr_psm: int = 0, ident_fdr_peptide: int = 0, ident_fdr_protein: int = 0, enable_match_between_runs: bool = False, precursor_mass_tolerance: str = None, fragment_mass_tolerance: str = None, enzyme: str = None, allowed_miscleavages: int = 0, min_peptide_length: int = 0, max_peptide_length: int = 0, is_temporary: bool = True, intermediate_hash: str = '', results: dict = None, nr_id_features: int = 0, lower_bound_FDP: float = nan, combined_FDP: float = nan, category_combined: str = '', category_paired: str = '', paired_FDP: float = nan, reported_fdr_parsed_from_input: float = nan, fdp_curve: dict = None, comments: str = '', proteobench_version: str = '')

Bases: DatapointBase

A data structure used to store the results of a entrapment benchmark run.

This class extends DatapointBase to implement entrapment-specific metrics and metadata storage for LFQ benchmarking runs.

id

Unique identifier for the benchmark run.

Type:

str

software_name

Name of the software used in the benchmark.

Type:

str

software_version

Version of the software.

Type:

str

search_engine

Name of the search engine used.

Type:

str

search_engine_version

Version of the search engine.

Type:

str

ident_fdr_psm

False discovery rate for PSMs.

Type:

float

ident_fdr_peptide

False discovery rate for peptides.

Type:

float

ident_fdr_protein

False discovery rate for proteins.

Type:

float

enable_match_between_runs

Whether matching between runs is enabled.

Type:

bool

precursor_mass_tolerance

Mass tolerance for precursor ions.

Type:

str

fragment_mass_tolerance

Mass tolerance for fragment ions.

Type:

str

enzyme

Enzyme used for digestion.

Type:

str

allowed_miscleavages

Number of allowed miscleavages.

Type:

int

min_peptide_length

Minimum peptide length.

Type:

int

max_peptide_length

Maximum peptide length.

Type:

int

is_temporary

Whether the data is temporary.

Type:

bool

intermediate_hash

Hash of the intermediate result.

Type:

str

results

A dictionary of metrics for the benchmark run.

Type:

dict

nr_id_features

Number of identified features.

Type:

int

lower_bound_FDP

estimated false discovery proportion based on entrapment IDs.

Type:

float

combined_FDP

estimated False discovery proportion based on entrapment IDs.

Type:

float

paired_FDP

estimated False discovery proportion based on entrapment IDs.

Type:

float

reported_fdr_parsed_from_input

FDR threshold inferred from the input data (max Q-value).

Type:

float

comments

Any additional comments.

Type:

str

proteobench_version

Version of the Proteobench tool used.

Type:

str

allowed_miscleavages: int = 0

category_combined: str = ''

category_paired: str = ''

combined_FDP: float = nan

comments: str = ''

enable_match_between_runs: bool = False

enzyme: str = None

fdp_curve: dict = None

fragment_mass_tolerance: str = None

static generate_datapoint(intermediate: DataFrame, input_format: str, user_input: dict) → Series

Generate a Datapoint object containing metadata and results from the benchmark run.

Parameters:

• intermediate (pd.DataFrame) – The intermediate DataFrame containing benchmark results.

• input_format (str) – The format of the input data (e.g., file format).

• user_input (dict) – User-defined input values for the benchmark.

• default_cutoff_min_prec (int, optional) – The default minimum precursor cutoff value. Defaults to 3.

• max_nr_observed (int, optional) – Maximum nr_observed value to calculate metrics for. If None, defaults to 6.

Returns:

A Pandas Series containing the Datapoint’s attributes as key-value pairs.

Return type:

pd.Series

generate_id() → None

Generate a unique ID for the benchmark run by combining the software name and a timestamp.

This ID is used to uniquely identify each run of the benchmark.

static get_metrics(intermediate: DataFrame) → Dict[str, Any]

Compute statistical metrics from the provided DataFrame.

Parameters:

• df (pd.DataFrame) – DataFrame containing the intermediate results.

• **kwargs (dict) – Additional module-specific parameters.

Returns:

Dictionary mapping quantification cutoffs to their computed metrics.

Return type:

Dict[int, Dict[str, float]]

id: str = None

ident_fdr_peptide: int = 0

ident_fdr_protein: int = 0

ident_fdr_psm: int = 0

intermediate_hash: str = ''

is_temporary: bool = True

lower_bound_FDP: float = nan

max_peptide_length: int = 0

min_peptide_length: int = 0

nr_id_features: int = 0

paired_FDP: float = nan

precursor_mass_tolerance: str = None

proteobench_version: str = ''

reported_fdr_parsed_from_input: float = nan

results: dict = None

search_engine: str = None

search_engine_version: int = 0

software_name: str = None

software_version: int = 0