MRMFeatureFinderScoring

class pyopenms.MRMFeatureFinderScoring

Bases: object

Cython implementation of _MRMFeatureFinderScoring

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’, ‘ProgressLogger’]

__init__(self) → None

Methods

__init__(self)
endProgress(self)	Ends the progress display
getDefaults(self)	Returns the default parameters
getLogType(self)	Returns the type of progress log being used
getName(self)	Returns the name
getParameters(self)	Returns the parameters
getSubsections(self)
nextProgress(self)	Increment progress by 1 (according to range begin-end)
pickExperiment(self, chromatograms, output, ...)	Pick features in one experiment containing chromatogram
prepareProteinPeptideMaps_(self, transition_exp)	Prepares the internal mappings of peptides and proteins
scorePeakgroups(self, transition_group, ...)	Score all peak groups of a transition group
setLogType(self, in_0)	Sets the progress log that should be used.
setMS1Map	Overload:
setName(self, in_0)	Sets the name
setParameters(self, param)	Sets the parameters
setProgress(self, value)	Sets the current progress
setStrictFlag(self, flag)
startProgress(self, begin, end, label)

endProgress(self) → None

Ends the progress display

getDefaults(self) → Param

Returns the default parameters

getLogType(self) → int

Returns the type of progress log being used

getName(self) → bytes | str | String

Returns the name

getParameters(self) → Param

Returns the parameters

getSubsections(self) → List[bytes]

nextProgress(self) → None

Increment progress by 1 (according to range begin-end)

pickExperiment(self, chromatograms: MSExperiment, output: FeatureMap, transition_exp_: TargetedExperiment, trafo: TransformationDescription, swath_map: MSExperiment) → None

Pick features in one experiment containing chromatogram

Function for for wrapping in Python, only uses OpenMS datastructures and does not return the map

Parameters:

• chromatograms – The input chromatograms

• output – The output features with corresponding scores

• transition_exp – The transition list describing the experiment

• trafo – Optional transformation of the experimental retention time to the normalized retention time space used in the transition list

• swath_map – Optional SWATH-MS (DIA) map corresponding from which the chromatograms were extracted

prepareProteinPeptideMaps_(self, transition_exp: LightTargetedExperiment) → None

Prepares the internal mappings of peptides and proteins

Calling this method _is_ required before calling scorePeakgroups

Parameters:

transition_exp – The transition list describing the experiment

scorePeakgroups(self, transition_group: LightMRMTransitionGroupCP, trafo: TransformationDescription, swath_maps: List[SwathMap], output: FeatureMap, ms1only: bool) → None

Score all peak groups of a transition group

Iterate through all features found along the chromatograms of the transition group and score each one individually

Parameters:

• transition_group – The MRMTransitionGroup to be scored (input)

• trafo – Optional transformation of the experimental retention time to the normalized retention time space used in thetransition list

• swath_maps – Optional SWATH-MS (DIA) map corresponding from which the chromatograms were extracted. Use empty map if no data is available

• output – The output features with corresponding scores (the found features will be added to this FeatureMap)

• ms1only – Whether to only do MS1 scoring and skip all MS2 scoring

setLogType(self, in_0: int) → None

Sets the progress log that should be used. The default type is NONE!

setMS1Map()

Overload:

setMS1Map(self, ms1_map: SpectrumAccessOpenMS) → None

Overload:

setMS1Map(self, ms1_map: SpectrumAccessOpenMSCached) → None

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

Sets the name

setParameters(self, param: Param) → None

Sets the parameters

setProgress(self, value: int) → None

Sets the current progress

setStrictFlag(self, flag: bool) → None

startProgress(self, begin: int, end: int, label: bytes | str | String) → None