Metabolite Feature Quality Control

metabolite-feature-quality-control

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Assess reproducibility of metabolomic features by computing coefficient of variation (CV) across quality control samples and filtering features that fail FDA thresholds for biomarker discovery (CV < 0.30) or quantification (CV < 0.15). This skill ensures only reliable, low-variance metabolites are retained for downstream association analysis.

When to use

Apply this skill when you have a metabolomic SummarizedExperiment object with replicate QC (quality control) samples and need to remove non-reproducible metabolic features before phenotype association modeling. Use it specifically when your workflow requires FDA-compliant reproducibility thresholds (CV < 0.30 for discovery, CV < 0.15 for quantification) or when you need to document what proportion of your feature set meets these standards.

When NOT to use

• Input metabolomic dataset lacks replicate QC samples — CV cannot be computed without within-QC variability
• Features have already been filtered or preprocessed by an external QC pipeline — applying redundant filtering may remove informative signal
• Study design uses single-injection samples without technical replicates — CV estimates will be unreliable

Inputs

• SummarizedExperiment object (metabo_SE) with metabolite abundances and QC sample identifiers
• Assay matrix containing raw metabolite intensity values across samples

Outputs

• Coefficient of variation vector (metabo_CV) with CV values per feature
• Filtered SummarizedExperiment object (metabo_SE) retaining only CV-passing features
• Summary table reporting count and proportion of features in each CV category
• Distribution plot (histogram or empirical CDF) with FDA threshold lines marked

How to apply

First, extract QC sample columns from the metabolomic SummarizedExperiment assay matrix and compute coefficient of variation (CV = sd/mean) for each metabolite feature across these QC replicates using the QC_CV function, producing a named CV vector. Second, calculate the cumulative distribution of CV values and report the percentage of features meeting each FDA threshold (< 0.30 and < 0.15) to document reproducibility. Third, apply the CV_filter function with a chosen threshold (typically CV_th = 0.30) to subset the SummarizedExperiment, retaining only features below the threshold and removing high-variance metabolites. Validate that the reported percentages match your computed values (e.g., 99% < 0.30, 92% < 0.15) and generate a distribution plot with threshold lines marked to visualize the filtering decision.

Related tools

• MWASTools (R package providing QC_CV and CV_filter functions for computing and applying CV thresholds to metabolomic SummarizedExperiment objects) — https://github.com/AndreaRMICL/MWASTools
• R (Programming environment for loading SummarizedExperiment objects and executing MWASTools functions (>=3.3 required))
• Bioconductor (Framework providing SummarizedExperiment class and infrastructure for metabolomic data representation)

Examples

metabo_CV <- QC_CV(metabo_SE); metabo_SE_filtered <- CV_filter(metabo_SE, metabo_CV, CV_th = 0.30); prop_pass_030 <- sum(metabo_CV < 0.30) / length(metabo_CV); prop_pass_015 <- sum(metabo_CV < 0.15) / length(metabo_CV)

Evaluation signals

• Reported percentages of features meeting FDA thresholds (99% < 0.30, 92% < 0.15) match independently computed values from the CV distribution
• Filtered SummarizedExperiment object contains only features with CV ≤ threshold; verify no features above threshold remain in assay matrix
• Distribution plot shows bimodal or expected unimodal CV distribution with threshold lines correctly positioned at 0.15 and 0.30
• Feature count decreases predictably: applying CV_th = 0.30 removes ~1% of features; applying CV_th = 0.15 removes ~8% of features relative to unfiltered set
• No missing values or NaN entries in metabo_CV vector; all feature names from input SummarizedExperiment are present in output CV vector

Limitations

• CV computation is sensitive to low absolute abundances (features with mean near zero produce inflated CV estimates); consider log-transformation or lower abundance thresholds
• FDA thresholds (0.30 and 0.15) are population-specific; coefficients vary by analytical platform (NMR vs. MS), sample matrix, and instrument tuning and should be validated in your own QC cohort
• Filtering by CV alone does not account for biological relevance; low-variance features may be uninformative metabolites whereas higher-variance features may still carry phenotype association signal
• Small QC sample sets (< 5 replicates) produce unstable CV estimates; the original study used 10 QC samples

Evidence

• [other] QC_CV function computes coefficient of variation for metabolites: "The QC_CV function calculates the coefficient of variation (sd/mean) for each NMR signal across the QC samples, producing a metabo_CV output vector that quantifies signal reproducibility."
• [other] FDA reproducibility thresholds for biomarker discovery and quantification: "99% of metabolic features exhibit CV < 0.30 and 92% exhibit CV < 0.15, confirming reproducibility of the NMR dataset according to FDA thresholds."
• [other] CV_filter function removes non-reproducible features: "CV_filter(metabo_SE, metabo_CV, CV_th = 0.30) retains only metabolic features with coefficient of variation below 0.30, removing non-reproducible features from the metabolic matrix to produce a"
• [abstract] Quality control analysis is a key MWASTools functionality: "Key functionalities of the package include: quality control analysis; metabolite-phenotype association models; data visualization tools; and metabolite assignment using statistical total correlation"
• [intro] NMR platform for metabolomic data acquisition: "1H NMR plasma spectra were acquired on a Bruker Avance III 600 MHz spectrometer"