# Mass Spectrometry Data Alignment

> Use when you have two LC-MS feature tables (each containing m/z, retention time, and intensity columns) from the same or related biological samples and need to identify which features in dataset A correspond to which features in dataset B.

- Skill: `holobiomicslab/mass-spectrometry-data-alignment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/mass-spectrometry-data-alignment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/mass-spectrometry-data-alignment/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/mass-spectrometry-data-alignment

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# mass-spectrometry-data-alignment

## Summary

Align untargeted metabolomic features across two LC-MS datasets by computing pairwise similarity scores on m/z and retention time dimensions, then resolving one-to-one feature correspondences. This skill enables integration of metabolomic datasets for comparative analysis.

## When to use

You have two LC-MS feature tables (each containing m/z, retention time, and intensity columns) from the same or related biological samples and need to identify which features in dataset A correspond to which features in dataset B. This is essential when combining or comparing untargeted metabolomic experiments that were run separately.

## When NOT to use

- Input datasets have already been aligned or preprocessed with a prior feature-matching step
- You are working with targeted metabolomics (where features are predefined) rather than untargeted discovery
- The two datasets come from fundamentally different analytical platforms (e.g., one GC-MS and one LC-MS)

## Inputs

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

## Outputs

- Matched feature pairs table (original identifiers, m/z, retention times, confidence scores)
- Match confidence scores or similarity metrics

## How to apply

Load both LC-MS feature tables into Matlab. Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm. Filter candidate matches using mass-to-charge and temporal tolerances to eliminate false positives—these thresholds control specificity and should reflect your instrument's typical measurement error and expected metabolite stability. Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair, ensuring each feature in dataset A maps to at most one feature in dataset B and vice versa. Compile the final matched pairs into a structured output table preserving original feature identifiers, m/z values, retention times, and match confidence scores for downstream validation.

## Related tools

- **M2S** (Matlab package that implements pairwise feature matching and one-to-one correspondence resolution for LC-MS metabolomic datasets) — https://github.com/rjdossan/M2S

## Evaluation signals

- All features in the output table have corresponding entries in both input datasets (referential integrity check)
- Match confidence scores are within the expected range (e.g., 0–1 or 0–100%) and show a clear separation between high-confidence matches and rejected pairs
- Each feature from dataset A appears in at most one matched pair (one-to-one correspondence enforced)
- Matched m/z values between datasets differ by less than the specified mass-to-charge tolerance
- Matched retention times between datasets differ by less than the specified temporal tolerance

## Limitations

- M2S requires manual setting of mass-to-charge and temporal tolerances; inappropriate thresholds can cause false positives or false negatives
- The algorithm is designed for pairwise matching only and cannot directly align >2 datasets simultaneously
- Performance and parameter sensitivity depend on the complexity of metabolomic profiles and the overlap between datasets

## Evidence

- [other] Load two LC-MS feature tables (each with m/z, retention time, and intensity columns) into Matlab. Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm.: "Load two LC-MS feature tables (each with m/z, retention time, and intensity columns) into Matlab. Apply pairwise feature matching by computing similarity scores across m/z and retention time"
- [other] Filter candidate matches based on 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.: "Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives. Resolve one-to-one feature correspondences by selecting the highest-confidence match for each"
- [other] Compile matched pairs into a structured output table with original feature identifiers, m/z values, retention times, and match confidence scores.: "Compile matched pairs into a structured output table with original feature identifiers, m/z values, retention times, and match confidence scores."
- [readme] Matlab package to match untargeted metabolomic features of two LC-MS datasets: "Matlab package to match untargeted metabolomic features of two LC-MS datasets"