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OpenMS
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Shared OpenSwath analysis context inference for peptide, protein, and gene rows. More...
#include <OpenMS/ANALYSIS/OPENSWATH/LevelContextInference.h>
Static Public Member Functions | |
| static std::vector< LevelContextResultRow > | infer (const std::vector< LevelContextInputRow > &input, const LevelContextInferenceConfig &config) |
| Estimate p-values, q-values, and PEPs for the given input rows. | |
Shared OpenSwath analysis context inference for peptide, protein, and gene rows.
This class is file-format agnostic and operates only on compact typed rows.
This is adapted from PyProphet's context inference methods. See the original publication for details: Rosenberger, G., Bludau, I., Schmitt, U. et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods 14, 921–927 (2017). https://doi.org/10.1038/nmeth.4398
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Estimate p-values, q-values, and PEPs for the given input rows.
The input rows are expected to contain one best-scoring row per entity and context. Their decoy flag defines the target/decoy split used to build score distributions for p-value, q-value, and local-FDR/PEP estimation.
For run-specific inference, target/decoy error statistics are estimated independently per RUN_ID. For global and experiment-wide inference, one shared score distribution is used for all rows in the call.
Internally this wraps OpenMS multiple-testing utilities for:
| input | Compact input rows selected for one inference level |
| config | Inference and error-estimation configuration |
1.9.8 