multi-criterion-scoring-integration
Summary
Integrates multiple orthogonal MS1 chemical predictors (m/z, retention time, isotopic pattern, neutral loss) into a unified proximity score to rank feature-suspect pairs for LC-HRMS suspect screening. This skill enables rapid, automated prioritization of candidate compounds by combining weak individual signals into a robust ensemble score.
When to use
Apply this skill when you have an LC-HRMS feature table (m/z, retention time, isotope ratios, fragmentation patterns) and a suspect compound database with reference properties (m/z, expected RT windows, theoretical isotope ratios, characteristic neutral losses), and you need to rank which features most likely correspond to which suspects without running MS2 fragmentation on all candidates.
When NOT to use
- Feature table is already assigned to confirmed standards with authentic MS2 spectra — use MS2 library matching instead.
- Suspect database lacks theoretical retention time or isotope ratio predictions — scoring will be incomplete and reliability reduced.
- Sample contains only a single dominant compound or is already pre-filtered to high confidence candidates — overhead of multi-criterion integration is not justified.
Inputs
- LC-HRMS feature table (m/z values, retention times, observed isotopic pattern abundances, observed fragmentation neutral losses)
- Suspect compound database (reference m/z, expected retention time windows, theoretical isotope ratios, known neutral loss fragments for each suspect)
Outputs
- Ranked feature-suspect proximity scores (scalar score per pair)
- Prioritized compound candidate list (features ranked by proximity to suspects, filtered above decision threshold)
- Scoring component breakdown (m/z distance, RT proximity, isotope match, neutral loss match per pair for interpretability)
How to apply
Load the observed feature table and suspect compound database. For each feature-suspect pair, compute four independent proximity metrics: (1) m/z distance as absolute or relative mass error within your tolerance window (e.g. 5 ppm); (2) retention time proximity as deviation from predicted RT within an acceptable window; (3) isotopic pattern match as correlation or χ² between observed and theoretical isotope abundance ratios; (4) neutral loss pattern match as presence of common loss fragments (e.g., H₂O, CO₂, CH₄) in the observed feature profile. Combine the four component scores via weighted sum, normalized ensemble, or other aggregation method—Scannotation's implementation uses a unified scoring mechanism but the exact weighting scheme should be tuned to your analytical platform and compound class. Rank all feature-suspect pairs by final score and apply a decision threshold to produce a prioritized candidate list for confirmatory MS2 analysis or targeted acquisition.
Related tools
Evaluation signals
- All four component scores (m/z, RT, isotope, neutral loss) are computed and present in output for each feature-suspect pair; no missing or NaN values where predictors are defined.
- Final proximity scores are bounded in a consistent range (e.g., 0–1 or 0–100) and monotonically rank candidates such that higher-scoring pairs receive higher priority.
- Feature-suspect pairs known to be true positives (from standards or confirmed annotations) rank significantly higher than decoy or negative-control pairs in the same feature set.
- Threshold-filtered candidate list is smaller than the full feature table by a meaningful margin (e.g., 5–50% of original features retained), indicating effective prioritization.
- Component score contributions are independently interpretable: e.g., a high-scoring pair can be traced to strong m/z and isotope matches even if RT or neutral loss scores are weak, demonstrating that the ensemble does not mask individual signals.
Limitations
- Accuracy depends on accuracy and completeness of suspect database predictions (reference m/z, RT windows, isotope ratios, neutral losses); missing or incorrect reference data will produce misleading proximity scores.
- Relative weighting of the four component scores (m/z, RT, isotope, neutral loss) is not data-driven in the article; manual tuning may be required for optimal performance on different compound classes or LC-MS platforms.
- Retention time prediction model is not detailed; if RT windows are too broad or narrow, the RT component will either add noise or provide false confidence.
- Neutral loss component requires detailed knowledge of suspect chemistry; for complex or unfamiliar compounds, predicted neutral loss patterns may be incomplete or incorrect.
- Does not address cases where multiple features map to the same suspect or multiple suspects map to the same feature; post-processing or conflict resolution rules are not specified.
Evidence
- [readme] Scannotation combines MS1 chemical predictors and scores proximity between features and suspects: "This software combines several MS1 chemical predictors: m/z, retention times, isotopic patterns and neutral loss patterns, to score the proximity between features and suspects"
- [intro] Four component scores are integrated into a unified scoring mechanism: "Scannotation implements a scoring module that integrates four MS1 chemical predictors—m/z, retention times, isotopic patterns, and neutral loss patterns—to compute a proximity score between detected"
- [other] Workflow combines component scores via weighted sum or ensemble method: "Combine the four component scores (m/z, RT, isotope, neutral loss) via weighted sum or other ensemble method to produce final feature-suspect proximity score"
- [readme] Scoring enables rapid pre-annotation and compound prioritization: "Scannotation is an automated and user-friendly suspect screening tool for the rapid pre-annotation of LC-HRMS datasets."
- [other] Final ranking and threshold filtering produce prioritized candidate list: "Rank candidates by proximity score and filter by threshold to produce prioritized compound list"
1---2name: multi-criterion-scoring-integration3description: Use when you have an LC-HRMS feature table (m/z, retention time, isotope ratios, fragmentation patterns) and a suspect compound database with reference properties (m/z, expected RT windows, theoretical isotope ratios, characteristic neutral losses), and you need to rank which features most likely.4license: CC-BY-4.05---67# multi-criterion-scoring-integration89## Summary1011Integrates multiple orthogonal MS1 chemical predictors (m/z, retention time, isotopic pattern, neutral loss) into a unified proximity score to rank feature-suspect pairs for LC-HRMS suspect screening. This skill enables rapid, automated prioritization of candidate compounds by combining weak individual signals into a robust ensemble score.1213## When to use1415Apply this skill when you have an LC-HRMS feature table (m/z, retention time, isotope ratios, fragmentation patterns) and a suspect compound database with reference properties (m/z, expected RT windows, theoretical isotope ratios, characteristic neutral losses), and you need to rank which features most likely correspond to which suspects without running MS2 fragmentation on all candidates.1617## When NOT to use1819- Feature table is already assigned to confirmed standards with authentic MS2 spectra — use MS2 library matching instead.20- Suspect database lacks theoretical retention time or isotope ratio predictions — scoring will be incomplete and reliability reduced.21- Sample contains only a single dominant compound or is already pre-filtered to high confidence candidates — overhead of multi-criterion integration is not justified.2223## Inputs2425- LC-HRMS feature table (m/z values, retention times, observed isotopic pattern abundances, observed fragmentation neutral losses)26- Suspect compound database (reference m/z, expected retention time windows, theoretical isotope ratios, known neutral loss fragments for each suspect)2728## Outputs2930- Ranked feature-suspect proximity scores (scalar score per pair)31- Prioritized compound candidate list (features ranked by proximity to suspects, filtered above decision threshold)32- Scoring component breakdown (m/z distance, RT proximity, isotope match, neutral loss match per pair for interpretability)3334## How to apply3536Load the observed feature table and suspect compound database. For each feature-suspect pair, compute four independent proximity metrics: (1) m/z distance as absolute or relative mass error within your tolerance window (e.g. 5 ppm); (2) retention time proximity as deviation from predicted RT within an acceptable window; (3) isotopic pattern match as correlation or χ² between observed and theoretical isotope abundance ratios; (4) neutral loss pattern match as presence of common loss fragments (e.g., H₂O, CO₂, CH₄) in the observed feature profile. Combine the four component scores via weighted sum, normalized ensemble, or other aggregation method—Scannotation's implementation uses a unified scoring mechanism but the exact weighting scheme should be tuned to your analytical platform and compound class. Rank all feature-suspect pairs by final score and apply a decision threshold to produce a prioritized candidate list for confirmatory MS2 analysis or targeted acquisition.3738## Related tools3940- **Scannotation** (implements multi-criterion scoring module integrating m/z, retention time, isotopic pattern, and neutral loss predictors for automated suspect screening and feature-suspect proximity ranking) — https://github.com/scannotation/Scannotation_software4142## Evaluation signals4344- All four component scores (m/z, RT, isotope, neutral loss) are computed and present in output for each feature-suspect pair; no missing or NaN values where predictors are defined.45- Final proximity scores are bounded in a consistent range (e.g., 0–1 or 0–100) and monotonically rank candidates such that higher-scoring pairs receive higher priority.46- Feature-suspect pairs known to be true positives (from standards or confirmed annotations) rank significantly higher than decoy or negative-control pairs in the same feature set.47- Threshold-filtered candidate list is smaller than the full feature table by a meaningful margin (e.g., 5–50% of original features retained), indicating effective prioritization.48- Component score contributions are independently interpretable: e.g., a high-scoring pair can be traced to strong m/z and isotope matches even if RT or neutral loss scores are weak, demonstrating that the ensemble does not mask individual signals.4950## Limitations5152- Accuracy depends on accuracy and completeness of suspect database predictions (reference m/z, RT windows, isotope ratios, neutral losses); missing or incorrect reference data will produce misleading proximity scores.53- Relative weighting of the four component scores (m/z, RT, isotope, neutral loss) is not data-driven in the article; manual tuning may be required for optimal performance on different compound classes or LC-MS platforms.54- Retention time prediction model is not detailed; if RT windows are too broad or narrow, the RT component will either add noise or provide false confidence.55- Neutral loss component requires detailed knowledge of suspect chemistry; for complex or unfamiliar compounds, predicted neutral loss patterns may be incomplete or incorrect.56- Does not address cases where multiple features map to the same suspect or multiple suspects map to the same feature; post-processing or conflict resolution rules are not specified.5758## Evidence5960- [readme] Scannotation combines MS1 chemical predictors and scores proximity between features and suspects: "This software combines several MS1 chemical predictors: m/z, retention times, isotopic patterns and neutral loss patterns, to score the proximity between features and suspects"61- [intro] Four component scores are integrated into a unified scoring mechanism: "Scannotation implements a scoring module that integrates four MS1 chemical predictors—m/z, retention times, isotopic patterns, and neutral loss patterns—to compute a proximity score between detected"62- [other] Workflow combines component scores via weighted sum or ensemble method: "Combine the four component scores (m/z, RT, isotope, neutral loss) via weighted sum or other ensemble method to produce final feature-suspect proximity score"63- [readme] Scoring enables rapid pre-annotation and compound prioritization: "Scannotation is an automated and user-friendly suspect screening tool for the rapid pre-annotation of LC-HRMS datasets."64- [other] Final ranking and threshold filtering produce prioritized candidate list: "Rank candidates by proximity score and filter by threshold to produce prioritized compound list"
HolobiomicsLab