Electronic Noise Generation Uniform Sampling

electronic-noise-generation-uniform-sampling

Summary

Synthesize realistic electronic noise for MS/MS spectra by uniformly sampling m/z values between 50 and precursor m/z, then drawing ion intensities from a Poisson distribution. This generates benchmark test datasets with known noise characteristics for validating denoising algorithms.

When to use

When you need to create synthetic noisy MS/MS spectra for benchmarking or validating denoising algorithms. Use this specifically when your goal is to introduce electronic noise (random detector artifacts) rather than chemical noise, and when you have a clean baseline spectrum and know its precursor m/z value. Triggers: you are building a test dataset with controlled noise levels, or you want to measure how well a denoising method recovers signal from spectra with known noise injection.

When NOT to use

• Spectrum already contains chemical noise (formula-dependent artifacts); use formula_denoising or generate_chemical_noise instead.
• Input is a mass spectrum with m/z < 50 as minimum baseline; uniform sampling from 50 may miss lower mass artifacts.
• You need to simulate isotope patterns or multiply-charged ions; this method generates random noise, not plausible chemical fragments.

Inputs

• clean mass spectrum (m/z–intensity peak pairs as numpy array, dtype=float32)
• precursor m/z (float, upper bound for noise sampling)
• number of noise ions to generate (integer, typically 5–20)
• Poisson lambda parameter (float, intensity scale; typical range 50–500)

Outputs

• noisy spectrum (m/z–intensity peak pairs as numpy array, dtype=float32)
• electronic noise ion list (m/z–intensity pairs, shape [num_noise, 2])

How to apply

Define a generate_noise function that samples m/z values uniformly at random between 50 and the precursor m/z value. For each sampled m/z, draw an intensity from a Poisson distribution with a specified lambda parameter (controlling noise magnitude). Generate a specified number of noise ion pairs (typically 5–20 per spectrum based on empirical MS/MS characteristics). Merge the noise peak array with the clean spectrum using an add_noise function that concatenates peaks and removes duplicate m/z entries by keeping the highest intensity. Validate the output by confirming the resulting ion count and m/z range match expectations (e.g., all noise m/z values fall within [50, precursor_mz], intensities are positive integers).

Related tools

• spectral-denoising (Host package for generate_noise and add_noise functions; provides spectrum I/O, validation, and integration with electronic and chemical denoising workflows.) — https://github.com/FanzhouKong/spectral_denoising
• numpy (Uniform and Poisson random sampling, peak array manipulation and merging.)
• scipy (Statistical distributions (Poisson) for intensity generation.)

Examples

from spectral_denoising.noise import *; import numpy as np; peak_clean = np.array([[79.02, 521.0], [81.01, 659.0]], dtype=np.float32); peak_noisy = sd.add_noise(peak_clean, num_noise=10, lambda_=100, precursor_mz=195.5)

Evaluation signals

• All generated noise m/z values fall strictly within [50, precursor_mz] range.
• Noise intensity values are non-negative integers (Poisson-sampled).
• After add_noise merges peaks, duplicate m/z entries are removed and the highest intensity is retained for each unique m/z.
• Output spectrum contains exactly num_noise additional ions beyond the clean spectrum baseline (or num_noise peaks total if clean spectrum was empty).
• When comparing denoised output (after electronic_denoising) to the input noisy spectrum, the denoised spectrum recovers m/z and intensity values closer to the original clean baseline; entropy similarity score increases.

Limitations

• Uniform sampling between 50–precursor_mz does not reproduce observed electronic noise distribution in real instruments; actual noise tends to cluster near precursor or common artifact m/z values.
• Poisson distribution assumes independent shot noise; real electronics introduce correlated, systematic artifacts.
• No mechanism to generate m/z measurement error or peak splitting; assumes perfect mass calibration.
• Lambda parameter must be tuned empirically; no automated selection based on precursor_mz or spectrum dynamic range is provided.
• Does not account for ion suppression or detector saturation effects; purely additive model.

Evidence

• [other] 1. 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.: "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"
• [other] 2. Define add_noise function to combine clean spectrum with generated noise by merging peak arrays and removing duplicate m/z entries (keeping highest intensity).: "combine clean spectrum with generated noise by merging peak arrays and removing duplicate m/z entries (keeping highest intensity)"
• [other] 3. Validate both functions by creating test spectra with known baseline, adding noise, and confirming output arrays contain expected ion counts and m/z ranges.: "creating test spectra with known baseline, adding noise, and confirming output arrays contain expected ion counts and m/z ranges"
• [intro] 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"
• [other] The electronic_denoising function removes obvious electronic noise ions in MS/MS spectra: "The electronic_denoising function removes obvious electronic noise ions in MS/MS spectra"