Metabolite cv ratio calculation

metabolite-cv-ratio-calculation

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Summary

Calculate and apply coefficient-of-variation (CV) ratios between sample and QC groups to filter metabolomic features by reproducibility. This skill identifies features with elevated variability in real samples relative to quality-control replicates, retaining only those exceeding a ratio threshold (typically CV_non-QC / CV_QC > 1.0).

When to use

Apply this skill after normalizing a metabolomic feature matrix when you have both non-QC (study) samples and QC (quality-control) replicates in the same experiment. Use it to remove features that are poorly reproducible or show inconsistent signal across samples relative to instrument/technical variation. This is especially valuable when you need to identify genuine biological signals amid high noise or batch effects typical of LC-MS/MS profiling.

When NOT to use

• Input is already a feature table filtered by other QC metrics (e.g., mass defect, missing value thresholds) — combine CV filtering into a unified QC pipeline rather than applying sequentially.
• Study design lacks QC replicates or has fewer than 3 QC samples per instrument run — CV calculation requires sufficient replication to estimate reproducibility reliably.
• Features are sparse or zero-inflated across samples — CV calculation may be unstable for rare features; consider imputation or alternative distributional assumptions first.

Inputs

• Normalized metabolomic feature abundance matrix (e.g., Urine_RP_NEG_norm.txt)
• Sample metadata identifying QC vs. non-QC sample group membership
• Feature annotations (m/z, retention time, metabolite name)

Outputs

• Filtered feature abundance matrix with retained features only
• CV ratio values (one per feature)
• Feature count summary (n_initial, n_retained, n_removed)
• CV ratio distribution statistics (mean, median, min, max, quantiles)

How to apply

Load the normalized feature abundance matrix (e.g., Urine_RP_NEG_norm.txt) and sample metadata identifying QC vs. non-QC sample groups into R. For each metabolomic feature, separately calculate the coefficient of variation (CV = standard deviation / mean) within the non-QC sample group and within the QC sample group. Compute the CV ratio as (CV_non-QC / CV_QC) for each feature. Apply a threshold filter, retaining only features where CV_ratio exceeds the default cutoff of 1.0; features below this threshold are likely noise or artifacts. Output the filtered feature table and generate summary statistics including count of features before and after filtering, mean/median CV ratio of retained features, and the full distribution of CV ratios to assess filtering stringency.

Related tools

• margheRita (Implements CV_ratio() function for coefficient-of-variation filtering of metabolomic features post-normalization) — https://github.com/emosca-cnr/margheRita
• R (Execution environment for CV calculation, ratio computation, and statistical filtering)
• notame (Alternative R package for non-targeted LC-MS preprocessing with built-in quality-control and feature-filtering workflows) — https://github.com/hanhineva-lab/notame

Examples

# In R with margheRita:
library(margheRita)
data_filtered <- CV_ratio(feature_matrix = urine_norm, sample_metadata = metadata, group_col = 'sample_type', qc_label = 'QC', threshold = 1.0)

Evaluation signals

• Feature count decreases from initial set (e.g., 539 → 303 metabolites), with count reduction proportional to CV ratio threshold stringency.
• Retained features have median CV ratio > 1.0 and distribution mean > 1.0 (e.g., median 1.1032 in the reference dataset), confirming biological signal enrichment.
• CV ratio histogram shows bimodal or right-skewed distribution with modal peak at or above threshold, indicating clear separation of high-variance (retained) from low-variance (removed) features.
• Removed features cluster at CV ratios ≤ 1.0, consistent with features whose variation in samples matches or is less than QC instrument noise.
• Subsequent statistical tests (e.g., ANOVA, Mann–Whitney U) on retained features show lower multiple-testing burden and higher effect sizes, validating improved signal-to-noise ratio.

Limitations

• CV ratio filtering is sensitive to outlier samples or QC replicates with anomalously high/low intensity; consider flagging or removing outliers before CV calculation.
• Threshold of CV_ratio > 1.0 is heuristic and may be too stringent (retaining few features) or too lenient (retaining noisy features) depending on sample complexity, ionization efficiency, and LC-MS instrument stability; consider adjusting based on downstream statistical power.
• Features with zero or near-zero mean abundance in either QC or non-QC groups will have undefined or inflated CV values; pre-filter features by minimum abundance or presence threshold.
• CV ratio does not account for biological covariance or clustering; features with high CV ratio may reflect genuine sample heterogeneity rather than true metabolite abundance differences.

Evidence

• [other] The CV_ratio() function retains only features with a CV ratio (non-QC samples / QC samples) exceeding the default threshold of 1, producing a distribution with median 1.1032 and resulting in 303 metabolites retained out of 539 initial features.: "The CV_ratio() function retains only features with a CV ratio (non-QC samples / QC samples) exceeding the default threshold of 1, producing a distribution with median 1.1032 and resulting in 303"
• [other] Calculate the coefficient of variation (CV) for each feature separately within the non-QC sample group and within the QC sample group.: "Calculate the coefficient of variation (CV) for each feature separately within the non-QC sample group and within the QC sample group."
• [other] Compute the CV ratio (CV_non-QC / CV_QC) for each feature. Apply the threshold filter, retaining only features where CV_ratio > 1.0.: "Compute the CV ratio (CV_non-QC / CV_QC) for each feature. Apply the threshold filter, retaining only features where CV_ratio > 1.0."
• [readme] filtering by coefficient of variation (samples vs QCs) and probabilistic quotient normalization: "filtering by coefficient of variation (samples vs QCs) and probabilistic quotient normalization"