IsotopeLabelingMDVs#

class pyopenms.IsotopeLabelingMDVs#

Bases: object

Cython implementation of _IsotopeLabelingMDVs

Original C++ documentation is available here

__init__()#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: IsotopeLabelingMDVs) → None

Methods

__init__

Overload:

calculateIsotopicPurity(self, ...)

This function calculates the isotopic purity of the MDV using the following formula: isotopic purity of tracer (atom % 13C) = n / [n + (M + n-1)/(M + n)], where n in M+n is represented as the index of the result The formula is extracted from "High-resolution 13C metabolic flux analysis", Long et al, doi:10.1038/s41596-019-0204-0

calculateMDV(self, measured_feature, ...)

calculateMDVAccuracies(self, ...)

This function calculates the accuracy of the MDV as compared to the theoretical MDV (only for 12C quality control experiments) using average deviation to the mean

calculateMDVAccuracy(self, ...)

This function calculates the accuracy of the MDV as compared to the theoretical MDV (only for 12C quality control experiments) using average deviation to the mean.

calculateMDVs(self, measured_featureMap, ...)

isotopicCorrection(self, normalized_feature, ...)

This function performs an isotopic correction to account for unlabeled abundances coming from the derivatization agent (e.g., tBDMS) using correction matrix method and is calculated as follows:

isotopicCorrections(self, ...)

This function performs an isotopic correction to account for unlabeled abundances coming from the derivatization agent (e.g., tBDMS) using correction matrix method and is calculated as follows:
calculateIsotopicPurity(self, normalized_feature: Feature, experiment_data: List[float], isotopic_purity_name: bytes | str | String) → None#

This function calculates the isotopic purity of the MDV using the following formula: isotopic purity of tracer (atom % 13C) = n / [n + (M + n-1)/(M + n)], where n in M+n is represented as the index of the result The formula is extracted from “High-resolution 13C metabolic flux analysis”, Long et al, doi:10.1038/s41596-019-0204-0

Parameters:

  • normalized_feature – Feature with normalized values for each component and the number of heavy labeled e.g., carbons. Out is a Feature with the calculated isotopic purity for the component group

  • experiment_data – Vector of experiment data in percent

  • isotopic_purity_name – Name of the isotopic purity tracer to be saved as a meta value

calculateMDV(self, measured_feature: Feature, normalized_feature: Feature, mass_intensity_type: int, feature_name: bytes | str | String) → None#
calculateMDVAccuracies(self, normalized_featureMap: FeatureMap, feature_name: bytes | str | String, fragment_isotopomer_theoretical_formulas: Dict[bytes | str, bytes | str]) → None#

This function calculates the accuracy of the MDV as compared to the theoretical MDV (only for 12C quality control experiments) using average deviation to the mean

param normalized_featuremap: FeatureMap with normalized values for each component and the chemical formula of the component group. Out is a FeatureMap with the component group accuracy and accuracy for the error for each component param fragment_isotopomer_measured: Measured scan values param fragment_isotopomer_theoretical_formula: A map of ProteinName/peptideRef to Empirical formula from which the theoretical values will be generated

calculateMDVAccuracy(self, normalized_feature: Feature, feature_name: bytes | str | String, fragment_isotopomer_theoretical_formula: bytes | str | String) → None#

This function calculates the accuracy of the MDV as compared to the theoretical MDV (only for 12C quality control experiments) using average deviation to the mean. The result is mapped to the meta value “average_accuracy” in the updated feature

Parameters:

  • normalized_feature – Feature with normalized values for each component and the chemical formula of the component group. Out is a Feature with the component group accuracy and accuracy for the error for each component

  • fragment_isotopomer_measured – Measured scan values

  • fragment_isotopomer_theoretical_formula – Empirical formula from which the theoretical values will be generated

calculateMDVs(self, measured_featureMap: FeatureMap, normalized_featureMap: FeatureMap, mass_intensity_type: int, feature_name: bytes | str | String) → None#
isotopicCorrection(self, normalized_feature: Feature, corrected_feature: Feature, correction_matrix: MatrixDouble, correction_matrix_agent: int) → None#

This function performs an isotopic correction to account for unlabeled abundances coming from the derivatization agent (e.g., tBDMS) using correction matrix method and is calculated as follows:

Parameters:

  • normalized_feature – Feature with normalized values for each component and unlabeled chemical formula for each component group

  • correction_matrix – Square matrix holding correction factors derived either experimentally or theoretically which describe how spectral peaks of naturally abundant 13C contribute to spectral peaks that overlap (or convolve) the spectral peaks of the corrected MDV of the derivatization agent

  • correction_matrix_agent – Name of the derivatization agent, the internally stored correction matrix if the name of the agent is supplied, only “TBDMS” is supported for now

Returns:

corrected_feature: Feature with corrected values for each component

isotopicCorrections(self, normalized_featureMap: FeatureMap, corrected_featureMap: FeatureMap, correction_matrix: MatrixDouble, correction_matrix_agent: int) → None#

This function performs an isotopic correction to account for unlabeled abundances coming from the derivatization agent (e.g., tBDMS) using correction matrix method and is calculated as follows:

Parameters:

  • normalized_featuremap – FeatureMap with normalized values for each component and unlabeled chemical formula for each component group

  • correction_matrix – Square matrix holding correction factors derived either experimentally or theoretically which describe how spectral peaks of naturally abundant 13C contribute to spectral peaks that overlap (or convolve) the spectral peaks of the corrected MDV of the derivatization agent

  • correction_matrix_agent – Name of the derivatization agent, the internally stored correction matrix if the name of the agent is supplied, only “TBDMS” is supported for now

Return corrected_featuremap:

FeatureMap with corrected values for each component

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