proteobench.datapoint.quant_datapoint module

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

class proteobench.datapoint.quant_datapoint.QuantDatapoint(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, median_abs_epsilon: float = 0, mean_abs_epsilon: float = 0, nr_prec: int = 0, comments: str = '', proteobench_version: str = '')

Bases: object

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

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

median_abs_epsilon

Median absolute epsilon value for the benchmark.

Type:

float

mean_abs_epsilon

Mean absolute epsilon value for the benchmark.

Type:

float

nr_prec

Number of precursors identified.

Type:

int

comments

Any additional comments.

Type:

str

proteobench_version

Version of the Proteobench tool used.

Type:

str

allowed_miscleavages: int = 0

comments: str = ''

enable_match_between_runs: bool = False

enzyme: str = None

fragment_mass_tolerance: str = None

static generate_datapoint(intermediate: DataFrame, input_format: str, user_input: dict, default_cutoff_min_prec: int = 3) → 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.

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.

get_metrics(min_nr_observed: int = 1) → dict[int, dict[str, float]]

Compute various statistical metrics from the provided DataFrame for the benchmark, but optimized to do fewer passes over the data.

static get_metrics_old(df: DataFrame, min_nr_observed: int = 1) → Dict[int, Dict[str, float]]

Compute various statistical metrics from the provided DataFrame for the benchmark.

Parameters:

• df (pd.DataFrame) – The DataFrame containing the benchmark results.

• min_nr_observed (int, optional) – The minimum number of observed values for a valid computation. Defaults to 1.

Returns:

A dictionary containing computed metrics such as ‘median_abs_epsilon’, ‘variance_epsilon’, etc.

Return type:

dict

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

max_peptide_length: int = 0

mean_abs_epsilon: float = 0

median_abs_epsilon: float = 0

min_peptide_length: int = 0

nr_prec: int = 0

precursor_mass_tolerance: str = None

proteobench_version: str = ''

results: dict = None

search_engine: str = None

search_engine_version: int = 0

software_name: str = None

software_version: int = 0

proteobench.datapoint.quant_datapoint.filter_df_numquant_epsilon(row: Dict[str, Any], min_quant: int = 3, metric: str = 'median') → float | None

Extract the ‘median_abs_epsilon’ value from a row (assumed to be a dictionary).

Parameters:

• row (dict) – The row from which to extract the value. Expected to be a dictionary.

• min_quant (int or str, optional) – The key for the desired value. Defaults to 3.

• metric (str) – The metric to be calculated. Should be either median or mean, defaults to median.

Returns:

The ‘median_abs_epsilon’ value if found, otherwise None.

Return type:

float or None

proteobench.datapoint.quant_datapoint.filter_df_numquant_nr_prec(row: Series, min_quant: int = 3) → int | None

Extract the ‘nr_prec’ value from a row (assumed to be a dictionary).

Parameters:

• row (pd.Series) – The row from which to extract the value. Expected to be a dictionary or Series.

• min_quant (int or str, optional) – The key for the desired value. Defaults to 3.

Returns:

The ‘nr_prec’ value if found, otherwise None.

Return type:

int, None