# Quality Metrics Summarization

> Use when after running QC analysis on NMR or MS metabolomic data and obtaining per-feature CV values, use this skill to validate that the dataset meets FDA thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification) and to report the proportion of features meeting each threshold.

- Skill: `holobiomicslab/quality-metrics-summarization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/quality-metrics-summarization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/quality-metrics-summarization/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/quality-metrics-summarization

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# quality-metrics-summarization

## Summary

Compute and report cumulative distributions of quality control metrics (e.g., coefficient of variation) across metabolomic features to validate dataset reproducibility against regulatory thresholds. This skill aggregates pre-computed per-feature QC values into interpretable summary statistics and visualizations that confirm fitness for downstream analysis.

## When to use

After running QC analysis on NMR or MS metabolomic data and obtaining per-feature CV values, use this skill to validate that the dataset meets FDA thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification) and to report the proportion of features meeting each threshold. This is a validation gate before proceeding to association modeling.

## When NOT to use

- Input is raw NMR or MS spectra (not yet processed through QC analysis)
- Per-feature CV values have not yet been computed or are missing
- The analysis goal is to identify individual features requiring reanalysis rather than to summarize overall dataset reproducibility

## Inputs

- Pre-computed per-feature CV values (numeric vector or table, output from MWASTools QC analysis)
- FDA regulatory thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification)

## Outputs

- Summary table: feature counts and proportions meeting each CV threshold
- Distribution plot: histogram or empirical CDF of CV values with threshold lines at 0.15 and 0.30
- Validation report: confirmation that observed percentages match expected thresholds

## How to apply

Load pre-computed per-feature CV values from QC output. Calculate the empirical cumulative distribution function (CDF) of CV and determine what proportion of features fall below each FDA threshold (0.30 and 0.15). Generate a summary table reporting feature counts and proportions in each CV category. Produce a distribution plot (histogram or empirical CDF) overlaid with vertical threshold lines at CV = 0.15 and 0.30. Validate that computed percentages match reported expectations (e.g., 99% < 0.30, 92% < 0.15) to confirm reproducibility and quality before advancing to metabolite-phenotype association models.

## Related tools

- **MWASTools** (R package that performs QC analysis and outputs per-feature CV values; used to load and aggregate QC metrics for threshold-based validation) — github.com/AndreaRMICL/MWASTools
- **R** (Statistical environment used to compute empirical CDF, calculate proportions, and generate summary tables and distribution plots)

## Examples

```
# In R using MWASTools output: cv_data <- read.csv('cv_values.csv'); prop_030 <- sum(cv_data$CV < 0.30) / nrow(cv_data); prop_015 <- sum(cv_data$CV < 0.15) / nrow(cv_data); cat('Proportion < 0.30:', prop_030, '\nProportion < 0.15:', prop_015, '\n'); hist(cv_data$CV, breaks=50, main='CV Distribution', xlab='Coefficient of Variation'); abline(v=c(0.15, 0.30), col=c('blue', 'red'), lty=2)
```

## Evaluation signals

- Computed percentages at CV thresholds (0.30 and 0.15) match reported expectations (99% and 92%, respectively)
- Sum of feature counts across all CV categories equals total number of features in the dataset
- Distribution plot displays both threshold lines (0.15 and 0.30) and reflects the empirical shape of the CV distribution
- Summary table contains non-negative integers for feature counts and proportions that sum to 100% (or 1.0 as decimal)
- No missing or undefined CV values in the input data; all features have valid numeric CV measurements

## Limitations

- Summary is descriptive only; does not identify which specific features are outliers or fail thresholds — separate investigation needed
- Thresholds (0.30, 0.15) are FDA recommendations; applicability may vary by regulatory context or assay platform (NMR vs. MS)
- Does not account for potential batch effects or platform-specific CV distributions that might differ across cohorts or experimental runs

## Evidence

- [intro] FDA threshold interpretation and task objective: "What proportion of NMR metabolic features meet the FDA coefficient of variation (CV) thresholds of <0.30 for biomarker discovery and <0.15 for quantification?"
- [other] Core workflow for metrics summarization: "Calculate the cumulative distribution of CV values and determine the percentage of features meeting each FDA threshold (CV < 0.30 and CV < 0.15). Generate a summary table reporting the count and"
- [other] Visualization and validation requirement: "Produce a distribution plot (histogram or empirical CDF) showing CV values with threshold lines marked at 0.15 and 0.30. Validate that reported percentages (99% at 0.30 threshold, 92% at 0.15"
- [other] Input data type: "Load the pre-computed per-feature CV values (output from quality control analysis)."
- [abstract] MWASTools QC capability: "Key functionalities of the package include: quality control (QC) analysis; metabolite-phenotype association models"