OpenMS

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 ycoordinates). 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 ycoordinates). 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: 11111121.

static 
This function computes Chauvenet's criterion for a vector and a value whose position is submitted.

static 
This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted.

static 
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 

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 ycoordinates).
Exception::UnableToFit  is thrown if fitting cannot be performed 

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) 

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) 

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 ycoordinates).
Exception::UnableToFit  is thrown if fitting cannot be performed 