Electronic Noise Generation Uniform Sampling

---
name: electronic-noise-generation-uniform-sampling
description: Use when when you need to create synthetic noisy MS/MS spectra for benchmarking
  or validating denoising algorithms.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3435
  edam_topics:
  - http://edamontology.org/topic_3520
  - http://edamontology.org/topic_0121
  tools:
  - Python
  - scipy
  - spectral-denoising
  - numpy
  techniques:
  - LC-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1038/s41592-025-02646-x
  title: Spectral Denoising
- doi: 10.1038/s41592-023-02012-9
  title: ''
evidence_spans:
- Spectral denoising requires ``Python >= 3.8`` installed on your system
- import spectral_denoising as sd
- scipy==1.14.1
- '- ``scipy==1.14.1``'
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_spectral_denoising_cq
    doi: 10.1038/s41592-025-02646-x
    title: Spectral Denoising
  dedup_kept_from: coll_spectral_denoising_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1038/s41592-025-02646-x
  all_source_dois:
  - 10.1038/s41592-025-02646-x
  - 10.1038/s41592-023-02012-9
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# 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"