PrecursorIonSelection

class pyopenms.PrecursorIonSelection

Bases: object

Cython implementation of _PrecursorIonSelection

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: PrecursorIonSelection) → None

Methods

__init__	Overload:
getDefaults(self)	Returns the default parameters
getLPSolver(self)
getMaxScore(self)
getName(self)	Returns the name
getNextPrecursors	Overload:
getParameters(self)	Returns the parameters
getSubsections(self)
rescore(self, features, new_pep_ids, ...)	Change scoring of features using peptide identifications from all spectra
reset(self)
setLPSolver(self, solver)
setMaxScore(self, max_score)
setName(self, in_0)	Sets the name
setParameters(self, param)	Sets the parameters
simulateRun(self, features, pep_ids, ...)	Simulate the iterative precursor ion selection
sortByTotalScore(self, features)	Sort features by total score

PrecursorIonSelection_Type

alias of __PrecursorIonSelection_Type

getDefaults(self) → Param

Returns the default parameters

getLPSolver(self) → int

getMaxScore(self) → float

getName(self) → bytes | str | String

Returns the name

getNextPrecursors()

Overload:

getNextPrecursors(self, features: FeatureMap, next_features: FeatureMap, number: int) → None

Returns features with highest score for MS/MS

Parameters:

• features – FeatureMap with all possible precursors

• next_features – FeatureMap with next precursors

• number – Number of features to be reported

Overload:

getNextPrecursors(self, solution_indices: List[int], variable_indices: List[IndexTriple], measured_variables: Set[int], features: FeatureMap, new_features: FeatureMap, step_size: int, ilp: PSLPFormulation) → None

getParameters(self) → Param

Returns the parameters

getSubsections(self) → List[bytes]

rescore(self, features: FeatureMap, new_pep_ids: List[PeptideIdentification], prot_ids: List[ProteinIdentification], preprocessed_db: PrecursorIonSelectionPreprocessing, check_meta_values: bool) → None

Change scoring of features using peptide identifications from all spectra

Parameters:

• features – FeatureMap with all possible precursors

• new_pep_ids – Peptide identifications

• prot_ids – Protein identifications

• preprocessed_db – Information from preprocessed database

• check_meta_values – True if the FeatureMap should be checked for the presence of required meta values

reset(self) → None

setLPSolver(self, solver: int) → None

setMaxScore(self, max_score: float) → None

setName(self, in_0: bytes | str | String) → None

Sets the name

setParameters(self, param: Param) → None

Sets the parameters

simulateRun(self, features: FeatureMap, pep_ids: List[PeptideIdentification], prot_ids: List[ProteinIdentification], preprocessed_db: PrecursorIonSelectionPreprocessing, path: bytes | str | String, experiment: MSExperiment, precursor_path: bytes | str | String) → None

Simulate the iterative precursor ion selection

Parameters:

• features – FeatureMap with all possible precursors

• new_pep_ids – Peptide identifications

• prot_ids – Protein identifications

• preprocessed_db – Information from preprocessed database

• step_size – Number of MS/MS spectra considered per iteration

• path – Path to output file

sortByTotalScore(self, features: FeatureMap) → None

Sort features by total score