retention-time-mass-correspondence-resolution
Summary
Match untargeted metabolomic features across two LC-MS datasets by resolving one-to-one correspondences based on m/z and retention time similarity. This skill enables cross-dataset feature alignment by computing pairwise similarity scores and selecting highest-confidence matches within specified mass and temporal tolerances.
When to use
You have two LC-MS untargeted metabolomic feature tables (each containing m/z, retention time, and intensity columns) and need to establish which features in dataset A correspond to which features in dataset B, typically for comparative metabolomics, batch effect correction, or multi-cohort feature integration.
When NOT to use
- Input datasets are targeted metabolomics (with known metabolite identities) rather than untargeted feature discovery.
- Features are already aligned (e.g., from a single LC-MS run or pre-processed common feature space).
- One or both input tables lack retention time information or have poor m/z calibration.
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 pair table (original identifiers, m/z, retention times, confidence scores)
- Feature correspondence matrix
How to apply
Load both LC-MS feature tables into Matlab, then apply the M2S matching algorithm to compute pairwise similarity scores across m/z and retention time dimensions. Define mass-to-charge and temporal tolerances to filter candidate matches and eliminate false positives. Resolve the bipartite matching problem by selecting the highest-confidence match for each feature pair, ensuring a one-to-one correspondence. Output a structured table mapping original feature identifiers, m/z values, retention times, and match confidence scores for downstream validation and reuse.
Related tools
- M2S (Matlab package implementing pairwise feature matching and one-to-one correspondence resolution via m/z and retention time similarity scoring) — https://github.com/rjdossan/M2S
Evaluation signals
- All matched pairs have m/z and retention time differences within user-specified tolerances (e.g., ±5 ppm for mass, ±0.5 min for retention time).
- Each feature in the smaller dataset is matched to at most one feature in the larger dataset (one-to-one correspondence enforced).
- Match confidence scores are monotonically ranked; the highest-scoring match is selected for each feature pair.
- Output table schema is complete: every matched pair includes original identifiers, m/z values, retention times, and confidence metrics.
- No matched pairs violate the pairwise similarity metric (e.g., Euclidean distance in normalized m/z–RT space).
Limitations
- M2S is designed for untargeted LC-MS feature matching; performance may degrade if features are poorly resolved or if m/z calibration differs significantly between datasets.
- One-to-one matching assumes no feature duplication or isobaric co-elution; overlapping or ambiguous matches may lead to arbitrary resolution.
- Sensitivity to mass and temporal tolerance thresholds; overly strict tolerances risk missing true matches, while loose tolerances risk false positives.
- No changelog or version control information was found, limiting reproducibility and compatibility tracking.
Evidence
- [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"
- [other] Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm.: "Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm"
- [other] Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives.: "Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives"
- [other] Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair.: "Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair"
- [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"
1---2name: retention-time-mass-correspondence-resolution3description: Use when you have two LC-MS untargeted metabolomic feature tables (each containing m/z, retention time, and intensity columns) and need to establish which features in dataset A correspond to which features in dataset B, typically for comparative metabolomics, batch effect correction, or.4license: CC-BY-4.05---67# retention-time-mass-correspondence-resolution89## Summary1011Match untargeted metabolomic features across two LC-MS datasets by resolving one-to-one correspondences based on m/z and retention time similarity. This skill enables cross-dataset feature alignment by computing pairwise similarity scores and selecting highest-confidence matches within specified mass and temporal tolerances.1213## When to use1415You have two LC-MS untargeted metabolomic feature tables (each containing m/z, retention time, and intensity columns) and need to establish which features in dataset A correspond to which features in dataset B, typically for comparative metabolomics, batch effect correction, or multi-cohort feature integration.1617## When NOT to use1819- Input datasets are targeted metabolomics (with known metabolite identities) rather than untargeted feature discovery.20- Features are already aligned (e.g., from a single LC-MS run or pre-processed common feature space).21- One or both input tables lack retention time information or have poor m/z calibration.2223## Inputs2425- LC-MS feature table 1 (with m/z, retention time, intensity columns)26- LC-MS feature table 2 (with m/z, retention time, intensity columns)2728## Outputs2930- Matched feature pair table (original identifiers, m/z, retention times, confidence scores)31- Feature correspondence matrix3233## How to apply3435Load both LC-MS feature tables into Matlab, then apply the M2S matching algorithm to compute pairwise similarity scores across m/z and retention time dimensions. Define mass-to-charge and temporal tolerances to filter candidate matches and eliminate false positives. Resolve the bipartite matching problem by selecting the highest-confidence match for each feature pair, ensuring a one-to-one correspondence. Output a structured table mapping original feature identifiers, m/z values, retention times, and match confidence scores for downstream validation and reuse.3637## Related tools3839- **M2S** (Matlab package implementing pairwise feature matching and one-to-one correspondence resolution via m/z and retention time similarity scoring) — https://github.com/rjdossan/M2S4041## Evaluation signals4243- All matched pairs have m/z and retention time differences within user-specified tolerances (e.g., ±5 ppm for mass, ±0.5 min for retention time).44- Each feature in the smaller dataset is matched to at most one feature in the larger dataset (one-to-one correspondence enforced).45- Match confidence scores are monotonically ranked; the highest-scoring match is selected for each feature pair.46- Output table schema is complete: every matched pair includes original identifiers, m/z values, retention times, and confidence metrics.47- No matched pairs violate the pairwise similarity metric (e.g., Euclidean distance in normalized m/z–RT space).4849## Limitations5051- M2S is designed for untargeted LC-MS feature matching; performance may degrade if features are poorly resolved or if m/z calibration differs significantly between datasets.52- One-to-one matching assumes no feature duplication or isobaric co-elution; overlapping or ambiguous matches may lead to arbitrary resolution.53- Sensitivity to mass and temporal tolerance thresholds; overly strict tolerances risk missing true matches, while loose tolerances risk false positives.54- No changelog or version control information was found, limiting reproducibility and compatibility tracking.5556## Evidence5758- [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"59- [other] Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm.: "Apply pairwise feature matching by computing similarity scores across m/z and retention time dimensions using the M2S matching algorithm"60- [other] Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives.: "Filter candidate matches based on mass-to-charge and temporal tolerances to eliminate false positives"61- [other] Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair.: "Resolve one-to-one feature correspondences by selecting the highest-confidence match for each feature pair"62- [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"
