# Metabolomic Feature Matching

> Use when you have two LC-MS feature tables (each with m/z, retention time, and intensity columns) from separate metabolomic experiments or replicates, and you need to establish which features in dataset A correspond to which features in dataset B to enable comparative or longitudinal analysis.

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

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# metabolomic-feature-matching

## Summary

M2S is a Matlab package that matches untargeted metabolomic features across two LC-MS datasets by computing similarity scores on m/z and retention time dimensions and resolving one-to-one feature correspondences. Use this skill when you have two independent LC-MS metabolomic experiments and need to establish which detected features represent the same underlying metabolite across datasets.

## When to use

You have two LC-MS feature tables (each with m/z, retention time, and intensity columns) from separate metabolomic experiments or replicates, and you need to establish which features in dataset A correspond to which features in dataset B to enable comparative or longitudinal analysis.

## When NOT to use

- Input data are already aligned or from targeted metabolomic methods with pre-defined feature lists
- You have only a single LC-MS dataset (no second dataset to match against)
- Features have already been matched using an alternative method and you are validating results

## Inputs

- LC-MS feature table 1 (m/z, retention time, intensity columns)
- LC-MS feature table 2 (m/z, retention time, intensity columns)

## Outputs

- Matched feature pairs table with original identifiers, m/z values, retention times, and confidence scores

## How to apply

Load both LC-MS feature tables into Matlab, each containing m/z, retention time, and intensity columns. Apply the M2S matching algorithm to compute pairwise similarity scores across the m/z and retention time dimensions. Filter candidate matches using mass-to-charge and temporal tolerances to eliminate false positives. Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair. Compile the matched pairs into a structured output table preserving original feature identifiers, m/z values, retention times, and match confidence scores.

## Related tools

- **Matlab** (Execution environment for M2S matching algorithm and feature table I/O)
- **M2S** (Core package implementing pairwise feature matching via m/z and retention time similarity scoring) — https://github.com/rjdossan/M2S

## Evaluation signals

- Output table contains exactly one match per feature (one-to-one correspondence enforced)
- All matched feature pairs fall within specified m/z and retention time tolerance windows
- Match confidence scores are ranked and the highest-scoring match is selected for each feature
- Output preserves and correctly maps original feature identifiers from both input tables
- No unmatched features are discarded without documentation of confidence threshold rationale

## Limitations

- M2S is designed for untargeted metabolomics; targeted methods with predefined feature lists may not benefit
- Matching quality depends critically on m/z and retention time tolerance parameters, which must be set appropriately for the LC-MS instrument and experimental conditions
- One-to-one resolution may lose true biological one-to-many relationships (e.g., isobaric metabolites or in-source fragments)
- No changelog is documented for the package, limiting reproducibility across versions

## Evidence

- [readme] Matlab package designed to match untargeted metabolomic features of two LC-MS datasets: "Matlab package to match untargeted metabolomic features of two LC-MS datasets"
- [other] Load two LC-MS feature tables and apply pairwise feature matching: "Load two LC-MS feature tables (each with m/z, retention time, and intensity columns) into Matlab. 2. Apply pairwise feature matching by computing similarity scores"
- [other] Filter using tolerances and resolve one-to-one correspondences: "Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives. 4. Resolve one-to-one feature correspondences by selecting the highest-confidence match"
- [other] Compile output with identifiers and confidence scores: "Compile matched pairs into a structured output table with original feature identifiers, m/z values, retention times, and match confidence scores"