# Peak Similarity Assessment

> Use when you have a set of picked peaks from INADEQUATE NMR spectra and need to group them into networks to identify which peaks co-originate from the same metabolite compound.

- Skill: `holobiomicslab/peak-similarity-assessment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/peak-similarity-assessment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/peak-similarity-assessment/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/peak-similarity-assessment

---


# peak-similarity-assessment

## Summary

Assess spectral similarity and co-occurrence patterns among picked peaks in INADEQUATE NMR spectra to group peaks originating from the same compound. This skill operates as a clustering step after peak picking and before database matching, using spectral similarity metrics to identify peak networks.

## When to use

Apply this skill when you have a set of picked peaks from INADEQUATE NMR spectra and need to group them into networks to identify which peaks co-originate from the same metabolite compound. Use it as a downstream filtering step after peak picking and before attempting to match peak networks to a metabolite database.

## When NOT to use

- Peak picking has not yet been performed; apply peak picking first
- Input peaks are already grouped or pre-clustered by an external method incompatible with PyINETA's clustering module
- Spectral data does not contain co-occurrence patterns (e.g., single isolated spectra with no replicate or multi-dimensional data)

## Inputs

- Picked peaks from PyINETA peak-picking module output
- Spectral data (INADEQUATE NMR spectra in PyINETA-compatible format)
- Peak coordinates and intensity values

## Outputs

- Peak cluster assignments (peak-to-cluster mapping file)
- Clustered peak networks with cluster identifiers
- Network metadata documenting co-occurrence and similarity patterns
- Clustered peak data compatible with downstream matching module

## How to apply

Load picked peaks from the peak-picking module output using PyINETA's picking interface. Apply the PyINETA clustering algorithm to group peaks based on spectral similarity and co-occurrence patterns—peaks that appear together across spectra and exhibit similar spectral features are clustered together. Assign each peak a unique cluster identifier and generate a mapping file documenting the peak-to-cluster assignments. Export the clustered peak data in a format compatible with downstream matching and finding modules. The clustering acts as a dimensionality reduction step that simplifies the search space for metabolite database matching by reducing noise and grouping related signals.

## Related tools

- **PyINETA** (Implements the clustering module (pyineta.clustering) that groups peaks by spectral similarity and co-occurrence patterns) — https://github.com/edisonomics/PyINETA
- **Python** (Runtime environment and language for PyINETA execution)

## Examples

```
python <path_to_pyineta_repo>/run_pyineta.py -c config.ini -s cluster -o output_dir
```

## Evaluation signals

- Peak-to-cluster mapping file is generated and contains a valid cluster identifier for every input peak
- Cluster size distribution is reasonable and peaks within each cluster exhibit high spectral similarity scores (exact threshold depends on algorithm configuration)
- Clusters preserve co-occurrence relationships observed in raw spectra (e.g., peaks that co-appear in spectra remain in the same cluster)
- Clustered output can be successfully loaded and processed by the downstream matching module without format errors
- Manual inspection of representative clusters confirms that grouped peaks correspond to expected multiplet patterns or correlated signals from the same compound structure

## Limitations

- Clustering performance depends on quality of upstream peak picking; poor peak picking produces unreliable clusters
- Algorithm assumes co-occurrence patterns are present in the spectral data; spectra with limited replication or single-scan acquisitions may produce uninformative clusters
- No changelog is publicly available for PyINETA, making it difficult to track changes in clustering algorithm behavior across versions
- Clustering operates on spectral similarity metrics without chemical structure validation; chemically unrelated peaks may cluster if they share spectral patterns

## Evidence

- [intro] pyINETA implements a clustering module that filters picked peaks to identify networks of peaks from the same compound: "It is designed to filter these picked peaks to identify networks of peaks (ideally) coming from the same compound"
- [methods] Clustering is a core workflow step in the PyINETA pipeline: "Clustering
----------
.. automodule:: pyineta.clustering"
- [other] Clustering operates on spectral similarity and co-occurrence patterns: "Apply the PyINETA clustering algorithm to group peaks into networks based on spectral similarity and co-occurrence patterns."
- [other] Clustering precedes matching to the metabolite database: "operating as a downstream step after peak picking and before matching to metabolite databases"
- [readme] PyINETA reads and processes INADEQUATE spectra with peak picking and clustering: "pyINETA can perform basic tasks such as reading and referencing (using basic shifting) the INADEQUATE spectra and peak picking"