PrecursorPurity

class pyopenms.PrecursorPurity

Bases: object

Cython implementation of _PrecursorPurity

Original C++ documentation is available here

Precursor purity or noise estimation

This class computes metrics for precursor isolation window purity (or noise) The function extracts the peaks from an isolation window targeted for fragmentation and determines which peaks are isotopes of the target and which come from other sources The intensities of the assumed target peaks are summed up as the target intensity Using this information it calculates an intensity ratio for the relative intensity of the target compared to other sources These metrics are combined over the previous and the next MS1 spectrum

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: PrecursorPurity) → None

Methods

__init__	Overload:
computePrecursorPurity(self, ms1, pre, ...)	Compute precursor purity metrics for one MS2 precursor

computePrecursorPurity(self, ms1: MSSpectrum, pre: Precursor, precursor_mass_tolerance: float, precursor_mass_tolerance_unit_ppm: bool) → PurityScores

Compute precursor purity metrics for one MS2 precursor

Note: This function is implemented in a general way and can also be used for e.g. MS3 precursor isolation windows in MS2 spectra Spectra annotated with charge 0 will be treated as charge 1.

Parameters:

• ms1 – The Spectrum containing the isolation window

• pre – The precursor containing the definition the isolation window

• precursor_mass_tolerance – The precursor tolerance. Is used for determining the targeted peak and deisotoping

• precursor_mass_tolerance_unit_ppm – The unit of the precursor tolerance