XQuestScores#

class pyopenms.XQuestScores#

Bases: object

Cython implementation of _XQuestScores

Original C++ documentation is available here

__init__()#

Overload:

__init__(self) None

Overload:

__init__(self, in_0: XQuestScores) None

Methods

__init__

Overload:

logOccupancyProb(self, theoretical_spec, ...)

Compute the logOccupancyProb score, similar to the match_odds, a score based on the probability of getting the given number of matched peaks by chance

matchOddsScore(self, theoretical_spec, ...)

Compute the match-odds score, a score based on the probability of getting the given number of matched peaks by chance

matchedCurrentChain(self, ...)

preScore

Overload:

totalMatchedCurrent(self, ...)

weightedTICScore(self, alpha_size, ...)

weightedTICScoreXQuest(self, alpha_size, ...)

xCorrelation(self, spec1, spec2, maxshift, ...)

xCorrelationPrescore(self, spec1, spec2, ...)

logOccupancyProb(self, theoretical_spec: MSSpectrum, matched_size: int, fragment_mass_tolerance: float, fragment_mass_tolerance_unit_ppm: bool) float#

Compute the logOccupancyProb score, similar to the match_odds, a score based on the probability of getting the given number of matched peaks by chance

Parameters:
  • theoretical_spec – Theoretical spectrum, sorted by position

  • matched_size – Number of matched peaks between experimental and theoretical spectra

  • fragment_mass_tolerance – The tolerance of the alignment

  • fragment_mass_tolerance_unit – The tolerance unit of the alignment, true = ppm, false = Da

matchOddsScore(self, theoretical_spec: MSSpectrum, fragment_mass_tolerance: float, fragment_mass_tolerance_unit_ppm: bool, is_xlink_spectrum: bool, n_charges: int) float#

Compute the match-odds score, a score based on the probability of getting the given number of matched peaks by chance

Parameters:
  • theoretical_spec – Theoretical spectrum, sorted by position

  • matched_size – Alignment between the theoretical and the experimental spectra

  • fragment_mass_tolerance – Fragment mass tolerance of the alignment

  • fragment_mass_tolerance_unit_ppm – Fragment mass tolerance unit of the alignment, true = ppm, false = Da

  • is_xlink_spectrum – Type of cross-link, true = cross-link, false = mono-link

  • n_charges – Number of considered charges in the theoretical spectrum

matchedCurrentChain(self, matched_spec_common: List[List[int, int]], matched_spec_xlinks: List[List[int, int]], spectrum_common_peaks: MSSpectrum, spectrum_xlink_peaks: MSSpectrum) float#
preScore()#

Overload:

preScore(self, matched_alpha: int, ions_alpha: int, matched_beta: int, ions_beta: int) float

Compute a simple and fast to compute pre-score for a cross-link spectrum match

Parameters:
  • matched_alpha – Number of experimental peaks matched to theoretical linear ions from the alpha peptide

  • ions_alpha – Number of theoretical ions from the alpha peptide

  • matched_beta – Number of experimental peaks matched to theoretical linear ions from the beta peptide

  • ions_beta – Number of theoretical ions from the beta peptide

Overload:

preScore(self, matched_alpha: int, ions_alpha: int) float

Compute a simple and fast to compute pre-score for a mono-link spectrum match

Parameters:
  • matched_alpha – Number of experimental peaks matched to theoretical linear ions from the alpha peptide

  • ions_alpha – Number of theoretical ions from the alpha peptide

totalMatchedCurrent(self, matched_spec_common_alpha: List[List[int, int]], matched_spec_common_beta: List[List[int, int]], matched_spec_xlinks_alpha: List[List[int, int]], matched_spec_xlinks_beta: List[List[int, int]], spectrum_common_peaks: MSSpectrum, spectrum_xlink_peaks: MSSpectrum) float#
weightedTICScore(self, alpha_size: int, beta_size: int, intsum_alpha: float, intsum_beta: float, total_current: float, type_is_cross_link: bool) float#
weightedTICScoreXQuest(self, alpha_size: int, beta_size: int, intsum_alpha: float, intsum_beta: float, total_current: float, type_is_cross_link: bool) float#
xCorrelation(self, spec1: MSSpectrum, spec2: MSSpectrum, maxshift: int, tolerance: float) List[float]#
xCorrelationPrescore(self, spec1: MSSpectrum, spec2: MSSpectrum, tolerance: float) float#