Metabolomic Feature Matching

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"