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, ...)

DerivatizationAgent

alias of __DerivatizationAgent

MassIntensityType

alias of __MassIntensityType

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