BayesianProteinInferenceAlgorithm

class pyopenms.BayesianProteinInferenceAlgorithm

Bases: object

Cython implementation of _BayesianProteinInferenceAlgorithm

Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1BayesianProteinInferenceAlgorithm.html

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

• Filters for best n PSMs per spectrum.

• Calculates and filters for best peptide per spectrum.

• Builds a k-partite graph from the structures.

• Finds and splits into connected components by DFS

• Extends the graph by adding layers from indist. protein groups, peptides with the same parents and optionally some additional layers (peptide sequence, charge, replicate -> extended model = experimental)

• Builds a factor graph representation of a Bayesian network using the Evergreen library See model param section. It is based on the Fido noisy-OR model with an option for regularizing the number of proteins per peptide.

• Performs loopy belief propagation on the graph and queries protein, protein group and/or peptide posteriors See loopy_belief_propagation param section.

• Learns best parameters via grid search if the parameters were not given in the param section.

• Writes posteriors to peptides and/or proteins and adds indistinguishable protein groups to the underlying data structures.

• Can make use of OpenMP to parallelize over connected components.

Usage:

from pyopenms import * from urllib.request import urlretrieve urlretrieve(”https://raw.githubusercontent.com/OpenMS/OpenMS/develop/src/tests/class_tests/openms/data/BayesianProteinInference_test.idXML”, “BayesianProteinInference_test.idXML”) proteins = [] peptides = [] idf = IdXMLFile() idf.load(“BayesianProteinInference_test.idXML”, proteins, peptides) bpia = BayesianProteinInferenceAlgorithm() p = bpia.getParameters() p.setValue(“update_PSM_probabilities”, “false”) bpia.setParameters(p) bpia.inferPosteriorProbabilities(proteins, peptides) # print(len(peptides)) # 9 print(peptides[0].getHits()[0].getScore()) # 0.6 print(proteins[0].getHits()[0].getScore()) # 0.624641 print(proteins[0].getHits()[1].getScore()) # 0.648346

__init__()

• Cython signature: void BayesianProteinInferenceAlgorithm()

• Cython signature: void BayesianProteinInferenceAlgorithm(unsigned int debug_lvl)

Methods

__init__	Cython signature: void BayesianProteinInferenceAlgorithm()
endProgress	Cython signature: void endProgress() Ends the progress display
getDefaults	Cython signature: Param getDefaults() Returns the default parameters
getLogType	Cython signature: LogType getLogType() Returns the type of progress log being used
getName	Cython signature: String getName() Returns the name
getParameters	Cython signature: Param getParameters() Returns the parameters
getSubsections	Cython signature: libcpp_vector[String] getSubsections()
inferPosteriorProbabilities	Cython signature: void inferPosteriorProbabilities(libcpp_vector[ProteinIdentification] & proteinIDs, libcpp_vector[PeptideIdentification] & peptideIDs, bool greedy_group_resolution)
nextProgress	Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)
setLogType	Cython signature: void setLogType(LogType) Sets the progress log that should be used.
setName	Cython signature: void setName(const String &) Sets the name
setParameters	Cython signature: void setParameters(Param & param) Sets the parameters
setProgress	Cython signature: void setProgress(ptrdiff_t value) Sets the current progress
startProgress	Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)

endProgress()

Cython signature: void endProgress() Ends the progress display

getDefaults()

Cython signature: Param getDefaults() Returns the default parameters

getLogType()

Cython signature: LogType getLogType() Returns the type of progress log being used

getName()

Cython signature: String getName() Returns the name

getParameters()

Cython signature: Param getParameters() Returns the parameters

getSubsections()

Cython signature: libcpp_vector[String] getSubsections()

inferPosteriorProbabilities()

• Cython signature: void inferPosteriorProbabilities(libcpp_vector[ProteinIdentification] & proteinIDs, libcpp_vector[PeptideIdentification] & peptideIDs, bool greedy_group_resolution)

Optionally adds indistinguishable protein groups with separate scores, too Currently only takes first proteinID run and all peptides —– :param proteinIDs: Vector of protein identifications :param peptideIDs: Vector of peptide identifications :return: Writes its results into protein and (optionally also) peptide hits (as new score)

• Cython signature: void inferPosteriorProbabilities(libcpp_vector[ProteinIdentification] & proteinIDs, libcpp_vector[PeptideIdentification] & peptideIDs, bool greedy_group_resolution, ExperimentalDesign exp_des)

Writes its results into protein and (optionally also) peptide hits (as new score). Optionally adds indistinguishable protein groups with separate scores, too Currently only takes first proteinID run and all peptides Experimental design can be used to create an extended graph with replicate information. (experimental) —– :param proteinIDs: Vector of protein identifications :param peptideIDs: Vector of peptide identifications :param exp_des: Experimental Design :return: Writes its results into protein and (optionally also) peptide hits (as new score)

• Cython signature: void inferPosteriorProbabilities(ConsensusMap & cmap, bool greedy_group_resolution)

Writes its results into protein and (optionally also) peptide hits (as new score) Optionally adds indistinguishable protein groups with separate scores, too Loops over all runs in the ConsensusMaps’ protein IDs (experimental) —– :param cmap: ConsensusMaps with protein IDs :param greedy_group_resolution: Adds indistinguishable protein groups with separate scores :return: Writes its protein ID results into the ConsensusMap

• Cython signature: void inferPosteriorProbabilities(ConsensusMap & cmap, bool greedy_group_resolution, ExperimentalDesign exp_des)

Writes its results into protein and (optionally also) peptide hits (as new score) Optionally adds indistinguishable protein groups with separate scores, too Loops over all runs in the ConsensusMaps’ protein IDs (experimental) —– :param cmap: ConsensusMaps with protein IDs. :param greedy_group_resolution: Adds indistinguishable protein groups with separate scores :param exp_des: Experimental Design :return: Writes its protein ID results into the ConsensusMap

nextProgress()

Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)

setLogType()

Cython signature: void setLogType(LogType) Sets the progress log that should be used. The default type is NONE!

setName()

Cython signature: void setName(const String &) Sets the name

setParameters()

Cython signature: void setParameters(Param & param) Sets the parameters

setProgress()

Cython signature: void setProgress(ptrdiff_t value) Sets the current progress

startProgress()

Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)