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OpenMS
2.5.0
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The MRMRTNormalizer will find retention time peptides in data. More...
#include <OpenMS/ANALYSIS/OPENSWATH/MRMRTNormalizer.h>
Static Public Member Functions | |
| static std::vector< std::pair< double, double > > | removeOutliersRANSAC (const std::vector< std::pair< double, double > > &pairs, double rsq_limit, double coverage_limit, size_t max_iterations, double max_rt_threshold, size_t sampling_size) |
| This function removes potential outliers in a linear regression dataset. More... | |
| static std::vector< std::pair< double, double > > | removeOutliersIterative (const std::vector< std::pair< double, double > > &pairs, double rsq_limit, double coverage_limit, bool use_chauvenet, const std::string &method) |
| This function removes potential outliers in a linear regression dataset. More... | |
| static double | chauvenet_probability (const std::vector< double > &residuals, int pos) |
| This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted. More... | |
| static bool | chauvenet (const std::vector< double > &residuals, int pos) |
| This function computes Chauvenet's criterion for a vector and a value whose position is submitted. More... | |
| static bool | computeBinnedCoverage (const std::pair< double, double > &rtRange, const std::vector< std::pair< double, double > > &pairs, int nrBins, int minPeptidesPerBin, int minBinsFilled) |
| Computes coverage of the RT normalization peptides over the whole RT range, ensuring that each bin has enough peptides. More... | |
Static Protected Member Functions | |
| static int | jackknifeOutlierCandidate_ (const std::vector< double > &x, const std::vector< double > &y) |
| This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The data points are submitted as two vectors of doubles (x- and y-coordinates). More... | |
| static int | residualOutlierCandidate_ (const std::vector< double > &x, const std::vector< double > &y) |
| This function computes a candidate outlier peptide by computing the residuals of all points to the linear fit and selecting the one with the largest deviation. The data points are submitted as two vectors of doubles (x- and y-coordinates). More... | |
The MRMRTNormalizer will find retention time peptides in data.
This tool will take a description of RT peptides and their normalized retention time to write out a transformation file on how to transform the RT space into the normalized space.
The principle is adapted from the following publication: Escher, C. et al. (2012), Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics, 12: 1111-1121.
This function computes Chauvenet's criterion for a vector and a value whose position is submitted.
This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted.
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Computes coverage of the RT normalization peptides over the whole RT range, ensuring that each bin has enough peptides.
| rtRange | The (estimated) full RT range in iRT space (theoretical RT) |
| pairs | The RT normalization peptide pairs (pair = experimental RT / theoretical RT) |
| nrBins | The number of bins to be used |
| minPeptidesPerBin | The minimal number of peptides per bin to be used to be considered full |
| minBinsFilled | The minimal number of bins needed to be full |
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staticprotected |
This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The data points are submitted as two vectors of doubles (x- and y-coordinates).
| Exception::UnableToFit | is thrown if fitting cannot be performed |
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static |
This function removes potential outliers in a linear regression dataset.
Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides and applies the Chauvenet's criterion on the assumption that the residuals are normal distributed to determine whether the peptides can be removed. This is done iteratively until both limits are reached.
| pairs | Input data (paired data of type <experimental_rt, theoretical_rt>) |
| rsq_limit | Minimal R^2 required |
| coverage_limit | Minimal coverage required (the number of points falls below this fraction, the algorithm aborts) |
| use_chauvenet | Whether to only remove outliers that fulfill Chauvenet's criterion for outliers (otherwise it will remove any outlier candidate regardless of the criterion) |
| method | Outlier detection method ("iter_jackknife" or "iter_residual") |
| Exception::UnableToFit | is thrown if fitting cannot be performed (rsq_limit and coverage_limit cannot be fulfilled) |
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static |
This function removes potential outliers in a linear regression dataset.
Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides using the RANSAC outlier detection algorithm and returns the corrected set of peptides if the two thresholds are satisfied.
| pairs | Input data (paired data of type <experimental_rt, theoretical_rt>) |
| rsq_limit | Minimal R^2 required |
| coverage_limit | Minimal coverage required (if the number of points falls below this fraction, the algorithm aborts) |
| max_iterations | Maximum iterations for the RANSAC algorithm |
| max_rt_threshold | Maximum deviation from fit for the retention time. This must be in the unit of the second dimension (e.g. theoretical_rt). |
| sampling_size | The number of data points to sample for the RANSAC algorithm. |
| Exception::UnableToFit | is thrown if fitting cannot be performed (rsq_limit and coverage_limit cannot be fulfilled) |
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staticprotected |
This function computes a candidate outlier peptide by computing the residuals of all points to the linear fit and selecting the one with the largest deviation. The data points are submitted as two vectors of doubles (x- and y-coordinates).
| Exception::UnableToFit | is thrown if fitting cannot be performed |
1.8.16
