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| BayesianProteinInferenceAlgorithm (unsigned int debug_lvl=0) |
| Constructor. More...
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| ~BayesianProteinInferenceAlgorithm () override=default |
| Destructor. More...
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void | updateMembers_ () override |
| This method is used to update extra member variables at the end of the setParameters() method. More...
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void | inferPosteriorProbabilities (std::vector< ProteinIdentification > &proteinIDs, std::vector< PeptideIdentification > &peptideIDs, bool greedy_group_resolution, std::optional< const ExperimentalDesign > exp_des=std::optional< const ExperimentalDesign >()) |
| Perform inference. Filter, build graph, run the private inferPosteriorProbabilities_ function. Writes its results into protein and (optionally also) peptide hits (as new score). Optionally adds indistinguishable protein groups with separate scores, too. Output scores are always posterior probabilities. Input can be posterior or error probabilities. See Param object defaults_ within the BayesianProteinInferenceAlgorithm for more settings. Currently only takes first proteinID run and all peptides (irrespective of getIdentifier()). More...
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void | inferPosteriorProbabilities (ConsensusMap &cmap, bool greedy_group_resolution, std::optional< const ExperimentalDesign > exp_des=std::optional< const ExperimentalDesign >()) |
| Perform inference. Filter, build graph, run the private inferPosteriorProbabilities_ function. Writes its results into protein and (optionally also) peptide hits (as new score). Optionally adds indistinguishable protein groups with separate scores, too. Output scores are always posterior probabilities. Input can be posterior or error probabilities. See Param object defaults_ within the BayesianProteinInferenceAlgorithm for more settings. Currently only takes first proteinID run and all peptides (irrespective of getIdentifier()). More...
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Public Member Functions inherited from DefaultParamHandler |
| DefaultParamHandler (const String &name) |
| Constructor with name that is displayed in error messages. More...
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| DefaultParamHandler (const DefaultParamHandler &rhs) |
| Copy constructor. More...
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virtual | ~DefaultParamHandler () |
| Destructor. More...
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DefaultParamHandler & | operator= (const DefaultParamHandler &rhs) |
| Assignment operator. More...
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virtual bool | operator== (const DefaultParamHandler &rhs) const |
| Equality operator. More...
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void | setParameters (const Param ¶m) |
| Sets the parameters. More...
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const Param & | getParameters () const |
| Non-mutable access to the parameters. More...
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const Param & | getDefaults () const |
| Non-mutable access to the default parameters. More...
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const String & | getName () const |
| Non-mutable access to the name. More...
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void | setName (const String &name) |
| Mutable access to the name. More...
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const std::vector< String > & | getSubsections () const |
| Non-mutable access to the registered subsections. More...
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| ProgressLogger () |
| Constructor. More...
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virtual | ~ProgressLogger () |
| Destructor. More...
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| ProgressLogger (const ProgressLogger &other) |
| Copy constructor. More...
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ProgressLogger & | operator= (const ProgressLogger &other) |
| Assignment Operator. More...
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void | setLogType (LogType type) const |
| Sets the progress log that should be used. The default type is NONE! More...
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LogType | getLogType () const |
| Returns the type of progress log being used. More...
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void | startProgress (SignedSize begin, SignedSize end, const String &label) const |
| Initializes the progress display. More...
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void | setProgress (SignedSize value) const |
| Sets the current progress. More...
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void | endProgress (UInt64 bytes_processed=0) const |
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void | nextProgress () const |
| increment progress by 1 (according to range begin-end) More...
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Performs a Bayesian protein inference on Protein/Peptide identifications or ConsensusMap (experimental).
- 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.