# Peak Shape Assessment > 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. - Skill: `holobiomicslab/peak-shape-assessment` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/peak-shape-assessment` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/peak-shape-assessment/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - License: CC-BY-4.0 - Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/holobiomicslab/peak-shape-assessment --- # peak-shape-assessment ## 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."