Spectral denoising: for single spectrum

The spectral_denoising function, which is the core function of the project, removes noise ions in MS/MS spectra.

The function essentially is a warpper function for performing electronic_denoising and formula_denoising functions in sequence. Just like formula_denoising, spectral_denoising also requires molecular information (SMILES and adduct) to remove noise ions. If no valid ion was left after electronic denoising, the function will return nan.

Example usage:

import spectral_denoising as sd
from spectral_denoising.noise import *
from spectral_denoising.spectral_operations import *
from spectral_denoising.chem_utils import *
peak = np.array([[48.992496490478516 ,154.0],
              [63.006099700927734, 265.0],
              [79.02062225341797, 521.0]], dtype = np.float32)
noise = generate_noise(pmz, lamda=10, n = 50)
smiles = 'O=c1nc[nH]c2nc[nH]c12'
adduct = '[M+Na]+'
pmz = calculate_precursormz(adduct,smiles)
peak_with_noise = add_noise(peak, noise)
peak_denoised = sd.spectral_denoising(peak_with_noise, smiles, adduct)
print(f'Entropy similarity of spectra with noise: {sd.entropy_similairty(peak_with_noise,peak, pmz ):.2f}.')
print(f'Entropy similarity of spectra with noise: {sd.entropy_similairty(peak_denoised,peak, pmz ):.2f}.')

The output will be:

Entropy similarity of spectra with noise: 0.47.
Entropy similarity of spectra with noise: 0.99.

References

spectral_denoising.spectral_denoising(msms, smiles, adduct, mass_tolerance=0.005)[source]

Perform spectral denoising on the given mass spectrometry data. The function first performs electronic denoising, followed by formula denoising.

Parameters:

msms (numpy.array): The mass spectrometry data to be denoised. smiles (str): The SMILES representation of the molecule. adduct (str): The adduct type. mass_tolerance (float, optional): The mass tolerance for the denoising process. Default is 0.005.

Returns:

numpy.array: The denoised mass spectrometry data.Returns NaN if the input is invalid or if the denoising process fails.

Notes:

  • The function first checks if any of the inputs are of type np.nan, which is considered invalid.

  • It then performs electronic denoising on the msms data.

  • If electronic denoising resulted in empty spectrum (all ions removed), it will return np.nan.

  • If successful, it proceeds to formula denoising using the electronic denoised data, smiles, adduct, and mass_tolerance.