MRMMapping

class pyopenms.MRMMapping

Bases: object

Cython implementation of _MRMMapping

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

__init__(self) → None

Methods

__init__(self)
getDefaults(self)	Returns the default parameters
getName(self)	Returns the name
getParameters(self)	Returns the parameters
getSubsections(self)
mapExperiment(self, input_chromatograms, ...)	Maps input chromatograms to assays in a targeted experiment
setName(self, in_0)	Sets the name
setParameters(self, param)	Sets the parameters

getDefaults(self) → Param

Returns the default parameters

getName(self) → bytes | str | String

Returns the name

getParameters(self) → Param

Returns the parameters

getSubsections(self) → List[bytes]

mapExperiment(self, input_chromatograms: MSExperiment, targeted_exp: TargetedExperiment, output: MSExperiment) → None

Maps input chromatograms to assays in a targeted experiment

The output chromatograms are an annotated copy of the input chromatograms with native id, precursor information and peptide sequence (if available) annotated in the chromatogram files

The algorithm tries to match a given set of chromatograms and targeted assays. It iterates through all the chromatograms retrieves one or more matching targeted assay for the chromatogram. By default, the algorithm assumes that a 1:1 mapping exists. If a chromatogram cannot be mapped (does not have a corresponding assay) the algorithm issues a warning, the user can specify that the program should abort in such a case (see error_on_unmapped)

:note If multiple mapping is enabled (see map_multiple_assays parameter) then each mapped assay will get its own chromatogram that contains the same raw data but different meta-annotation. This can be useful if the same transition is used to monitor multiple analytes but may also indicate a problem with too wide mapping tolerances

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

Sets the name

setParameters(self, param: Param) → None

Sets the parameters