spectral-denoising 0.0.6

Spectral denoising and denoising search

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If you have any questions, feel free to send me E-mails: [e-mail]. If you find this package useful, please consider citing the following papers:

. Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer, Res Sq, 2024). [https://doi.org/10.1038/s41592-023-02012-9]

Project information

Remove noise ions from MS/MS spectra has been tackled for years by mass spectrometrists. Noise ions in MS/MS spectra are largely categorized as 1. electronic noises and 2. chemical noises. In this project, we aim to eliminate both chemical noise and electronic noises for improving high-confidence compound identification. Integrating such process into spectra matching process, we developed denoising search, which psudo-denoise spectra based on molecular information fetched from reference databases. This project also provides useful tools to read, write, visualize and compare spectra.

How to use this package

This repository in Python. A python version >= 3.8 is preferred.

Installation

pip install spectral-denoising

Usage of Classical spectral denoising (electronic denoising and chemical denoising)

import numpy as np
import spectral_denoising as sd
import spectral_denoising.spectral_operations as so

quene_data = sd.read_msp('../sample_data/noisy_spectra.msp').iloc[0] # just use the first spectra
reference_data= sd.read_msp('../sample_data/clean_spectra.msp').iloc[0] 
quene_spectra, quene_smiles, quene_adduct, quene_pmz = quene_data['peaks'], quene_data['smiles'], quene_data['adduct'], quene_data['precursor_mz']
reference_spectra = reference_data['peaks']

# Visualize the head-to-tail plots
sd.head_to_tail_plot(quene_spectra, reference_spectra, pmz = quene_pmz)

# Run spectra denoising will remove electronic noises and chemical noises.
msms_denoised = sd.spectra_denoising(quene_spectra, quene_smiles, quene_adduct) #denoise the spectrum based on the smiles/adduct information

# Calculate spectral entropy before and after denoising.
print(f'the raw spectra has spectral entropy of {so.spctrum_entropy(quene_spectra):.2f}')
print(f'the denoised spectra has spectral entropy of {so.spctrum_entropy(msms_denoised):.2f}')
# Calculate entropy similarity.
sd.head_to_tail_plot(msms_denoised, reference_spectra, pmz = quene_pmz)

Example of usage for batch mode can be found in /notebook/spectral_denoising_demo.ipynb

Usage of Flash Entropy Search

import spectral_denoising as sd
data_dir = '../sample_data/'
quene_spectra= sd.read_msp(os.path.join(data_dir, 'quene_spectra.msp'))
reference_library =sd.read_msp(os.path.join(data_dir, 'sample_library.msp'))
quene_spectrum, quene_pmz = quene_spectra.iloc[0]['peaks'], quene_spectra.iloc[0]['precursor_mz']
sd.denoising_search(quene_spectrum, quene_pmz, reference_library)

Example of usage for batch mode can be found in /notebook/denoising_search_demo.ipynb