# Mass Spectrum Peak Manipulation Merging

> Use when after generating electronic noise (uniformly sampled m/z with Poisson-distributed intensities) and chemical noise (formula database-sampled m/z with Poisson intensities) and you need to combine both noise types with a clean baseline spectrum into a single unified peak array.

- Skill: `holobiomicslab/mass-spectrum-peak-manipulation-merging` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/mass-spectrum-peak-manipulation-merging`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/mass-spectrum-peak-manipulation-merging/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/mass-spectrum-peak-manipulation-merging

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# mass-spectrum-peak-manipulation-merging

## Summary

Combine electronic and chemical noise peaks with clean spectrum peaks by merging m/z arrays and resolving duplicate m/z entries to produce a consolidated peak array for benchmarking MS/MS spectra analysis. This skill is essential for creating synthetic noisy test datasets where peak intensities at identical m/z values must be reconciled by selecting the highest intensity.

## When to use

Apply this skill after generating electronic noise (uniformly sampled m/z with Poisson-distributed intensities) and chemical noise (formula database-sampled m/z with Poisson intensities) and you need to combine both noise types with a clean baseline spectrum into a single unified peak array. Use it during synthetic spectra generation for benchmarking denoising algorithms, when you must simulate realistic MS/MS noise contamination while preserving the highest-intensity peak at each m/z location.

## When NOT to use

- When input spectra are already denoised or do not contain duplicates—use this skill only when intentionally merging separate noise streams with a clean baseline.
- When m/z precision and rounding behavior differ between peak sources—inconsistent floating-point comparison may cause false negatives in duplicate detection; standardize m/z precision before merging.
- When you need to preserve all peak intensities for later statistical analysis—this merging operation discards sub-maximal intensity values, which may be required for uncertainty quantification or alternative noise models.

## Inputs

- clean baseline spectrum (numpy array of shape [n, 2] with m/z and intensity columns)
- electronic noise peaks (numpy array of shape [m, 2])
- chemical noise peaks (numpy array of shape [k, 2])

## Outputs

- merged and deduplicated peak array (numpy array of shape [p, 2] where p ≤ n+m+k, with duplicate m/z entries resolved to maximum intensity)

## How to apply

First, obtain three separate peak arrays: the clean baseline spectrum (m/z × intensity pairs), the generated electronic noise peaks, and the generated chemical noise peaks. Merge all three arrays into a single concatenated list of [m/z, intensity] pairs. Iterate through the merged array and identify duplicate m/z entries (treating m/z values as keys). For each duplicate m/z location, retain only the peak pair with the highest intensity value, discarding lower-intensity duplicates at that m/z. The rationale is that in real MS/MS spectra, a single m/z value produces one measured ion; by keeping the maximum intensity, the function preserves the strongest signal while eliminating redundant noise artifacts. Sort the deduplicated result by m/z to maintain standard spectral format.

## Related tools

- **spectral_denoising** (Python package providing add_noise and peak merging utilities; used to integrate generated electronic and chemical noise into clean spectra for creating test datasets) — https://github.com/FanzhouKong/spectral_denoising
- **numpy** (Array manipulation and duplicate detection; enables efficient m/z grouping and intensity comparison operations)
- **scipy** (Statistical distributions (Poisson); used to generate noise intensities before merging)

## Examples

```
import numpy as np; from spectral_denoising.noise import add_noise; clean_peaks = np.array([[100.0, 500.0], [200.0, 300.0]], dtype=np.float32); electronic_noise = np.array([[150.0, 100.0]], dtype=np.float32); chemical_noise = np.array([[100.0, 50.0]], dtype=np.float32); merged_peaks = add_noise(clean_peaks, electronic_noise, chemical_noise)
```

## Evaluation signals

- Output array contains no duplicate m/z entries—verify by checking that all m/z values in the merged result are unique (no two rows share the same m/z within floating-point precision).
- Number of peaks in output does not exceed sum of input peaks: len(merged) ≤ len(clean) + len(electronic_noise) + len(chemical_noise).
- At each m/z location where duplicates existed in input, the retained intensity equals the maximum of all input intensities at that m/z.
- Output array is sorted by increasing m/z values—required for downstream spectral operations (comparison, visualization, database matching).
- Ion count and m/z range of output align with expected test dataset characteristics—verify against known baseline and noise parameters (Poisson lambda, m/z sampling bounds).

## Limitations

- Floating-point m/z precision: duplicate detection depends on exact or near-exact m/z matching; small measurement variations (e.g., rounding errors from different instruments or calculation pipelines) may cause false negatives in merging, leaving spurious duplicates in output.
- Loss of intensity diversity: by keeping only the maximum intensity at each m/z, the function discards information about competing noise sources; this simplification may not reflect complex multi-source noise in real spectra.
- No mass calibration or alignment: assumes all input peak arrays are already calibrated to the same m/z reference frame; if inputs use different mass calibrations or instrument resolutions, merging will create false duplicates or miss true ones.
- Scale dependence on Poisson parameter lambda: noise intensity distribution depends critically on lambda choice in generate_noise and generate_chemical_noise; inappropriately tuned lambda values produce unrealistic noise and may fail to create challenging test cases for denoising validation.

## Evidence

- [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] Validate both functions by creating test spectra with known baseline, adding noise, and confirming output arrays contain expected ion counts and m/z ranges.: "Validate both functions by creating test spectra with known baseline, adding noise, and confirming output arrays contain expected ion counts and m/z ranges."
- [other] This project also provides useful tools to read, write, visualize and compare spectra.: "This project also provides useful tools to read, write, visualize and compare spectra."
- [readme] 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."