peak-shape-assessment
License: noncommercial — confirm your use is a permitted (noncommercial) purpose before applying; commercial use requires a separate license (see metadata.tool_license).
Summary
Evaluate the morphological quality of detected LC-MS peaks by extracting and analyzing peak shape attributes (e.g., symmetry, width, definition) as part of comprehensive feature quality scoring. This skill surfaces problematic peaks that may indicate instrumental artifacts, co-elution, or poor signal quality before downstream annotation.
When to use
Apply this skill after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain features have inconsistent intensity or poor annotation confidence. Peak shape assessment is essential before committing features to MS/MS annotation or statistical analysis.
When NOT to use
- Input is already a validated, publication-ready feature table with pre-computed quality scores.
- Analysis is targeted and peaks are known a priori (e.g., selected reaction monitoring); peak shape assessment is most valuable in nontargeted discovery workflows.
- Peak detection algorithm has not yet been run; this skill requires detected features, not raw LC-MS data alone.
Inputs
- feature table from peak detection (pandas DataFrame or equivalent with retention time, m/z, intensity, chromatographic metrics)
- raw LC-MS data or extracted ion chromatogram data for peak profile analysis
Outputs
- quality-annotated feature table with per-feature quality scores
- peak shape quality flags (pass/fail/warning)
- per-feature diagnostic metrics (peak definition, chromatographic resolution scores)
How to apply
Extract peak morphology attributes (retention time, m/z, peak width, symmetry metrics, signal-to-noise ratio) from the detected feature table using MassCube's quality evaluation module. Compute individual peak shape quality dimensions, such as peak definition and chromatographic resolution, which quantify how well-defined and baseline-separated the peak is. Aggregate shape metrics with other quality dimensions (intensity consistency, isotope/adduct coherence) into a per-feature quality score and assign quality flags (pass/fail/warning). Features with poor peak shape scores should be flagged for review or exclusion, as they indicate potential instrumental noise, co-elution, or chemical artifacts rather than true molecular signals.
Related tools
- masscube (Provides the integrated quality evaluation module that extracts peak shape attributes and computes per-feature quality scores including peak definition and chromatographic resolution) — https://github.com/huaxuyu/masscube/
- Python (Programming environment for loading feature tables with pandas, extracting peak attributes, and scripting the quality evaluation workflow)
Evaluation signals
- Output feature table schema matches input but includes new columns for per-feature quality scores and peak shape flags; all rows retain original feature identifiers.
- Quality scores are numeric, bounded (e.g., 0–1 or 0–100), and correlate inversely with peak width, asymmetry, and baseline noise.
- Features flagged as 'fail' or 'warning' exhibit visually poor peak morphology (broad, asymmetric, low signal-to-noise ratio) when inspected in the extracted ion chromatogram.
- Aggregate quality distribution shows expected bimodality or right-skew toward high-quality features in a typical nontargeted dataset; absence of such distribution suggests algorithm failure or misconfiguration.
- Per-feature diagnostics (peak definition and resolution scores) are internally consistent and inversely correlated with intensity variance across replicates.
Limitations
- Peak shape assessment depends on accurate peak detection upstream; misdetected or partially detected peaks will receive misleading quality scores.
- Quality thresholds and aggregation weights are not explicitly tuned in the README; practitioners may need to validate cutoffs against their own instrumental and sample contexts.
- No changelog is available; feature definitions and quality metrics may change across MassCube versions without warning.
Evidence
- [other] Extract feature attributes including retention time, m/z, peak shape, signal-to-noise ratio, and chromatographic metrics.: "Extract feature attributes including retention time, m/z, peak shape, signal-to-noise ratio, and chromatographic metrics."
- [other] Compute individual quality dimensions (peak definition, chromatographic resolution, intensity consistency, isotope/adduct coherence) using masscube's quality evaluation module.: "Compute individual quality dimensions (peak definition, chromatographic resolution, intensity consistency, isotope/adduct coherence) using masscube's quality evaluation module."
- [intro] Comprehensive feature quality evaluation.: "Comprehensive feature quality evaluation."
- [other] Aggregate per-feature quality scores into a single comprehensive metric and assign quality flags (pass/fail/warning).: "Aggregate per-feature quality scores into a single comprehensive metric and assign quality flags (pass/fail/warning)."
- [readme] masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing.: "masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing."
1---2name: peak-shape-assessment-23description: Use when after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain features have inconsistent intensity or poor annotation confidence.4license: CC-BY-4.05---67# peak-shape-assessment89> **License: noncommercial** — confirm your use is a permitted (noncommercial) purpose before applying; commercial use requires a separate license (see `metadata.tool_license`). <!-- asb-license-banner -->10## Summary1112Evaluate the morphological quality of detected LC-MS peaks by extracting and analyzing peak shape attributes (e.g., symmetry, width, definition) as part of comprehensive feature quality scoring. This skill surfaces problematic peaks that may indicate instrumental artifacts, co-elution, or poor signal quality before downstream annotation.1314## When to use1516Apply this skill after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain features have inconsistent intensity or poor annotation confidence. Peak shape assessment is essential before committing features to MS/MS annotation or statistical analysis.1718## When NOT to use1920- Input is already a validated, publication-ready feature table with pre-computed quality scores.21- Analysis is targeted and peaks are known a priori (e.g., selected reaction monitoring); peak shape assessment is most valuable in nontargeted discovery workflows.22- Peak detection algorithm has not yet been run; this skill requires detected features, not raw LC-MS data alone.2324## Inputs2526- feature table from peak detection (pandas DataFrame or equivalent with retention time, m/z, intensity, chromatographic metrics)27- raw LC-MS data or extracted ion chromatogram data for peak profile analysis2829## Outputs3031- quality-annotated feature table with per-feature quality scores32- peak shape quality flags (pass/fail/warning)33- per-feature diagnostic metrics (peak definition, chromatographic resolution scores)3435## How to apply3637Extract peak morphology attributes (retention time, m/z, peak width, symmetry metrics, signal-to-noise ratio) from the detected feature table using MassCube's quality evaluation module. Compute individual peak shape quality dimensions, such as peak definition and chromatographic resolution, which quantify how well-defined and baseline-separated the peak is. Aggregate shape metrics with other quality dimensions (intensity consistency, isotope/adduct coherence) into a per-feature quality score and assign quality flags (pass/fail/warning). Features with poor peak shape scores should be flagged for review or exclusion, as they indicate potential instrumental noise, co-elution, or chemical artifacts rather than true molecular signals.3839## Related tools4041- **masscube** (Provides the integrated quality evaluation module that extracts peak shape attributes and computes per-feature quality scores including peak definition and chromatographic resolution) — https://github.com/huaxuyu/masscube/42- **Python** (Programming environment for loading feature tables with pandas, extracting peak attributes, and scripting the quality evaluation workflow)4344## Evaluation signals4546- Output feature table schema matches input but includes new columns for per-feature quality scores and peak shape flags; all rows retain original feature identifiers.47- Quality scores are numeric, bounded (e.g., 0–1 or 0–100), and correlate inversely with peak width, asymmetry, and baseline noise.48- Features flagged as 'fail' or 'warning' exhibit visually poor peak morphology (broad, asymmetric, low signal-to-noise ratio) when inspected in the extracted ion chromatogram.49- Aggregate quality distribution shows expected bimodality or right-skew toward high-quality features in a typical nontargeted dataset; absence of such distribution suggests algorithm failure or misconfiguration.50- Per-feature diagnostics (peak definition and resolution scores) are internally consistent and inversely correlated with intensity variance across replicates.5152## Limitations5354- Peak shape assessment depends on accurate peak detection upstream; misdetected or partially detected peaks will receive misleading quality scores.55- Quality thresholds and aggregation weights are not explicitly tuned in the README; practitioners may need to validate cutoffs against their own instrumental and sample contexts.56- No changelog is available; feature definitions and quality metrics may change across MassCube versions without warning.5758## Evidence5960- [other] Extract feature attributes including retention time, m/z, peak shape, signal-to-noise ratio, and chromatographic metrics.: "Extract feature attributes including retention time, m/z, peak shape, signal-to-noise ratio, and chromatographic metrics."61- [other] Compute individual quality dimensions (peak definition, chromatographic resolution, intensity consistency, isotope/adduct coherence) using masscube's quality evaluation module.: "Compute individual quality dimensions (peak definition, chromatographic resolution, intensity consistency, isotope/adduct coherence) using masscube's quality evaluation module."62- [intro] Comprehensive feature quality evaluation.: "Comprehensive feature quality evaluation."63- [other] Aggregate per-feature quality scores into a single comprehensive metric and assign quality flags (pass/fail/warning).: "Aggregate per-feature quality scores into a single comprehensive metric and assign quality flags (pass/fail/warning)."64- [readme] masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing.: "masscube is an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing."
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