Intensity distribution simulation

Intensity-Distribution Simulation

Summary

Synthesize realistic electronic and chemical noise ions by sampling m/z values and drawing intensities from Poisson distributions, enabling creation of benchmarking datasets with controlled noise characteristics for MS/MS spectrum denoising validation.

When to use

When you need to create synthetic noisy MS/MS spectra from clean baseline spectra to validate denoising algorithms, compare denoising performance across noise levels, or generate ground-truth test datasets where the true signal and noise composition are known and controllable.

When NOT to use

• When you need to denoise real experimental spectra—simulation adds synthetic noise but does not remove actual noise from measured data.
• When your goal is to assess denoising performance on authentic noise distributions—simulated Poisson-drawn intensities may not capture real electronic or chemical noise characteristics.
• When the input spectrum is already contaminated with real noise and you want to validate denoising on ground truth—use synthetic clean spectra instead.

Inputs

• clean spectrum (m/z, intensity pairs as numpy array)
• precursor m/z value
• formula database (sorted by mass)
• lambda parameter for Poisson distribution
• desired number of electronic noise ions
• desired number of chemical noise ions

Outputs

• noisy spectrum (m/z, intensity pairs with synthetic noise added)
• noise ion array (electronic noise component)
• chemical noise ion array (chemical noise component)

How to apply

Define separate noise generation pipelines for electronic and chemical noise. For electronic noise: uniformly sample m/z values between 50 and the precursor m/z, then draw intensities from a Poisson distribution with a specified lambda parameter, creating a defined number of noise ion pairs. For chemical noise: randomly sample m/z values from a pre-sorted formula database (formula_db), generate corresponding intensities from the same Poisson distribution, and create the desired number of chemical noise ions. Merge the generated noise peaks with the clean spectrum's peak array, removing duplicate m/z entries by retaining the highest intensity at each m/z. Validate the synthetic spectra by confirming the output arrays contain the expected ion counts and m/z ranges matching your simulation parameters.

Related tools

• numpy (Array operations for m/z and intensity sampling and merging)
• scipy (Poisson distribution sampling for intensity generation)
• spectral_denoising (spectral-denoising package) (Implementation of generate_noise, generate_chemical_noise, and add_noise functions; spectrum validation and comparison) — https://github.com/FanzhouKong/spectral_denoising

Examples

from spectral_denoising.noise import *; import numpy as np; peak_with_noise = add_noise(peak, precursor_mz=200.0, lambda_elec=0.5, num_elec=5, lambda_chem=0.8, num_chem=8); print(peak_with_noise.shape)

Evaluation signals

• Output noise spectrum contains the sum of original peaks plus generated electronic and chemical noise ions, with no duplicate m/z entries.
• Generated electronic noise m/z values fall within [50, precursor_mz] range.
• Generated chemical noise m/z values exist in the formula_db and are chemically plausible fragment losses.
• Intensity values from both noise sources follow Poisson distribution with specified lambda; mean intensity ≈ lambda.
• Ion counts in output match expected total: original peak count + electronic noise count + chemical noise count (minus any m/z collisions retained as maximum intensity).

Limitations

• Poisson-sampled intensities may not accurately represent the statistical properties of real electronic and chemical noise in actual MS/MS instruments.
• The approach assumes uniform m/z sampling for electronic noise, which may not reflect true instrument noise patterns across different mass ranges.
• Chemical noise generation relies on the completeness and accuracy of the pre-sorted formula_db; missing formulas will not be represented in synthetic noise.
• Noise intensity distribution is independent of m/z; real noise often exhibits m/z-dependent behavior not captured by this method.
• The method does not account for instrument-specific noise characteristics (e.g., detector saturation, baseline drift) and is best used for controlled benchmarking rather than realistic noise simulation.

Evidence

• [other] Define generate_noise function to synthesize electronic noise: sample m/z values uniformly between 50 and precursor m/z, generate intensities from Poisson distribution with parameter lambda, and create specified number of noise ion pairs.: "Define generate_noise function to synthesize electronic noise: sample m/z values uniformly between 50 and precursor m/z, generate intensities from Poisson distribution with parameter lambda, and"
• [other] Define generate_chemical_noise function to synthesize chemical noise: randomly sample m/z values from a pre-sorted formula database (formula_db), generate corresponding intensities from Poisson distribution with given lambda parameter, and create specified number of chemical noise ions.: "Define generate_chemical_noise function to synthesize chemical noise: randomly sample m/z values from a pre-sorted formula database (formula_db), generate corresponding intensities from Poisson"
• [other] Define add_noise function to combine clean spectrum with generated noise by merging peak arrays and removing duplicate m/z entries (keeping highest intensity).: "Define add_noise function to combine clean spectrum with generated noise by merging peak arrays and removing duplicate m/z entries (keeping highest intensity)."
• [other] Noise ions in MS/MS spectra are largely categorized as 1. electronic noises and 2. chemical noises.: "Noise ions in MS/MS spectra are largely categorized as 1. electronic noises and 2. chemical noises."
• [readme] generate some noise ions and add it to the peaks: "# generate some noise ions and add it to the peaks"