PeptideAndProteinQuant#

class pyopenms.PeptideAndProteinQuant#

Bases: object

Cython implementation of _PeptideAndProteinQuant

Original C++ documentation is available here: – Inherits from [‘DefaultParamHandler’]

__init__()#

Overload:

__init__(self) → None

Helper class for peptide and protein quantification based on feature data annotated with IDs

Overload:

__init__(self, in_0: PeptideAndProteinQuant) → None

Methods

`__init__`	Overload:
`getDefaults`(self)	Returns the default parameters
`getName`(self)	Returns the name
`getParameters`(self)	Returns the parameters
`getStatistics`(self)
`getSubsections`(self)
`quantifyPeptides`(self, peptides)	Compute peptide abundances
`quantifyProteins`(self, proteins)	Compute protein abundances
`readQuantData`	Overload:
`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

getStatistics(self) → PeptideAndProteinQuant_Statistics#

getSubsections(self) → List[bytes]#

quantifyPeptides(self, peptides: List[PeptideIdentification]) → None#

Compute peptide abundances

Based on quantitative data for individual charge states (in member pep_quant_), overall abundances for peptides are computed (and stored again in pep_quant_) Quantitative data must first be read via readQuantData() Optional (peptide-level) protein inference information (e.g. from Fido or ProteinProphet) can be supplied via peptides. In that case, peptide-to-protein associations - the basis for protein-level quantification - will also be read from peptides!

quantifyProteins(self, proteins: ProteinIdentification) → None#

Compute protein abundances

Peptide abundances must be computed first with quantifyPeptides(). Optional protein inference information (e.g. from Fido or ProteinProphet) can be supplied via proteins

readQuantData()#

Overload:

readQuantData(self, map_in: FeatureMap, ed: ExperimentalDesign) → None

Read quantitative data from a feature map

Parameters should be set before using this method, as setting parameters will clear all results

Overload:

readQuantData(self, map_in: ConsensusMap, ed: ExperimentalDesign) → None

Read quantitative data from a consensus map

Parameters should be set before using this method, as setting parameters will clear all results

Overload:

readQuantData(self, proteins: List[ProteinIdentification], peptides: List[PeptideIdentification], ed: ExperimentalDesign) → None

Read quantitative data from identification results (for quantification via spectral counting)

Parameters should be set before using this method, as setting parameters will clear all results

setName(self, in_0: bytes | str | String) → None#: Sets the name

setParameters(self, param: Param) → None#: Sets the parameters