QC Sample Reliability Evaluation
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Assess the reliability and quality of quality control (QC) samples in LC-MS metabolomics data by applying conservative thresholds to relative standard deviation (RSD), robust RSD*, and D-ratio metrics, then flag features that fail to meet reproducibility standards before downstream analysis.
When to use
After drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or excessive QC-versus-biological variation (D-ratio) before imputation and batch correction. Use this when QC samples are still present in the MetaboSet and you want to quantify feature reliability prior to their removal.
When NOT to use
- QC samples have already been removed from the dataset—flag_quality requires QC replicates to compute RSD and D-ratio metrics.
- Drift correction has not yet been performed—drift-induced systematic shifts will artificially inflate RSD and invalidate quality assessment.
- Your LC-MS data has no replicate QC injections—the function requires multiple QC runs to calculate reproducibility statistics.
Inputs
- MetaboSet object (Biobase ExpressionSet subclass) with drift-corrected intensity matrix in exprs slot
- fData feature annotation table with quality metric columns (RSD, RSD*, D-ratio)
- pData sample metadata distinguishing QC and biological samples
Outputs
- MetaboSet object with Flag column added to fData, marking low-quality features with TRUE/FALSE or a flag code
- Quality metric summary table showing proportion of features flagged by each metric
- Visualization (histogram/density plot) of RSD, D-ratio, and detection rate distributions across all features
How to apply
Load the drift-corrected MetaboSet object and apply the flag_quality function with conservative thresholds (RSD limit 0.1, RSD* limit 0.1, D-ratio limit 0.1) that are stricter than recommended defaults (RSD 0.2, D-ratio 0.4). The function evaluates RSD as the coefficient of variation of feature intensity across all QC injections, RSD* as the robust variant using median absolute deviation, and D-ratio as the ratio of between-group spread (QC vs. biological samples) to within-group spread. Features exceeding any of these thresholds are marked in the Flag column of the fData slot. Inspect the resulting Flag column to identify newly flagged features and cross-reference them against the distribution of quality metrics (RSD, D-ratio, detection rate) to assess what proportion of the feature set falls below your quality thresholds.
Related tools
- notame (R package providing the flag_quality function and MetaboSet data structure for non-targeted LC-MS metabolomics preprocessing and quality control) — https://github.com/hanhineva-lab/notame
- Biobase (Bioconductor package providing the ExpressionSet class upon which MetaboSet is built, enabling consistent fData/pData/exprs slot access)
- R (Statistical computing language in which notame and the flag_quality function are implemented)
Examples
mset_flagged <- flag_quality(mset_drift_corrected, rsd_limit = 0.1, rsd_star_limit = 0.1, d_ratio_limit = 0.1); fData(mset_flagged)$Flag
Evaluation signals
- Flag column is present in fData with boolean or flag-code values for all features; no missing values in the Flag column.
- Flagged feature count and proportion match expectations based on the conservative thresholds (RSD 0.1, RSD* 0.1, D-ratio 0.1)—typically a higher proportion than recommended thresholds would flag.
- Quality metric distributions (RSD, D-ratio, detection rate histograms) show clear separation between flagged and unflagged populations, with flagged features concentrated at the high end of the metric range.
- Spot-check: manually verify 5–10 flagged features by examining their RSD, RSD*, and D-ratio values to confirm they exceed at least one threshold.
- Unflagged features should have RSD < 0.1 AND RSD* < 0.1 AND D-ratio < 0.1 (all three conditions must be satisfied to pass).
Limitations
- Conservative thresholds (0.1 for all metrics) may be overly stringent for exploratory metabolomics; 30–50% of features may be flagged depending on data quality, potentially excluding true biology.
- D-ratio calculation assumes adequate separation between QC (technical replicates) and biological samples; poor experimental design or confounding variables will invalidate the metric.
- RSD and RSD* are sensitive to low-abundance features and features with missing values; imputation strategy applied post-flagging can alter the reliability assessment retroactively.
- The function does not account for feature-specific biological variability; a feature with high D-ratio may reflect genuine biological heterogeneity rather than poor QC performance.
- Recommended thresholds (RSD 0.2, D-ratio 0.4) are dataset- and platform-dependent; the choice of 0.1 should be justified by piloting or reference materials.
Evidence
- [other] The flag_quality function flags features based on quality metrics using conservative limits of 0.1 for classic RSD, RSD*, and basic D-ratio (compared to recommended thresholds of 0.2 for RSD and 0.4 for D-ratio), producing a Flag column on the MetaboSet object to mark low-quality features.: "The flag_quality function flags features based on quality metrics using conservative limits of 0.1 for classic RSD, RSD*, and basic D-ratio (compared to recommended thresholds of 0.2 for RSD and 0.4"
- [other] Apply flag_quality function with conservative limit of 0.1 for classic RSD, RSD* (robust version using median absolute deviation), and basic D-ratio to flag features where internal spread or QC-versus-biological spread exceeds thresholds.: "Apply flag_quality function with conservative limit of 0.1 for classic RSD, RSD* (robust version using median absolute deviation), and basic D-ratio to flag features where internal spread or"
- [readme] Identifying and flagging (or removing) low-quality molecular features using quality metrics defined by Broadhurst et al.: "Identifying and flagging (or removing) low-quality molecular features using quality metrics"
- [readme] Drift correction: correcting for systematic drift in the intensity of molecular features using cubic spline correction: "After peak picking with the dedicated software, we use R for data preprocessing, quality control, statistical analysis and visualization"
- [other] flag_quality is used to flag features based on the other quality metrics: "flag_quality is used to flag features based on the other quality metrics"
1---2name: qc-sample-reliability-evaluation-23description: Use when after drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or excessive QC-versus-biological variation (D-ratio) before imputation and batch correction.4license: CC-BY-4.05---67# QC Sample Reliability Evaluation89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Assess the reliability and quality of quality control (QC) samples in LC-MS metabolomics data by applying conservative thresholds to relative standard deviation (RSD), robust RSD*, and D-ratio metrics, then flag features that fail to meet reproducibility standards before downstream analysis.1314## When to use1516After drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or excessive QC-versus-biological variation (D-ratio) before imputation and batch correction. Use this when QC samples are still present in the MetaboSet and you want to quantify feature reliability prior to their removal.1718## When NOT to use1920- QC samples have already been removed from the dataset—flag_quality requires QC replicates to compute RSD and D-ratio metrics.21- Drift correction has not yet been performed—drift-induced systematic shifts will artificially inflate RSD and invalidate quality assessment.22- Your LC-MS data has no replicate QC injections—the function requires multiple QC runs to calculate reproducibility statistics.2324## Inputs2526- MetaboSet object (Biobase ExpressionSet subclass) with drift-corrected intensity matrix in exprs slot27- fData feature annotation table with quality metric columns (RSD, RSD*, D-ratio)28- pData sample metadata distinguishing QC and biological samples2930## Outputs3132- MetaboSet object with Flag column added to fData, marking low-quality features with TRUE/FALSE or a flag code33- Quality metric summary table showing proportion of features flagged by each metric34- Visualization (histogram/density plot) of RSD, D-ratio, and detection rate distributions across all features3536## How to apply3738Load the drift-corrected MetaboSet object and apply the flag_quality function with conservative thresholds (RSD limit 0.1, RSD* limit 0.1, D-ratio limit 0.1) that are stricter than recommended defaults (RSD 0.2, D-ratio 0.4). The function evaluates RSD as the coefficient of variation of feature intensity across all QC injections, RSD* as the robust variant using median absolute deviation, and D-ratio as the ratio of between-group spread (QC vs. biological samples) to within-group spread. Features exceeding any of these thresholds are marked in the Flag column of the fData slot. Inspect the resulting Flag column to identify newly flagged features and cross-reference them against the distribution of quality metrics (RSD, D-ratio, detection rate) to assess what proportion of the feature set falls below your quality thresholds.3940## Related tools4142- **notame** (R package providing the flag_quality function and MetaboSet data structure for non-targeted LC-MS metabolomics preprocessing and quality control) — https://github.com/hanhineva-lab/notame43- **Biobase** (Bioconductor package providing the ExpressionSet class upon which MetaboSet is built, enabling consistent fData/pData/exprs slot access)44- **R** (Statistical computing language in which notame and the flag_quality function are implemented)4546## Examples4748```49mset_flagged <- flag_quality(mset_drift_corrected, rsd_limit = 0.1, rsd_star_limit = 0.1, d_ratio_limit = 0.1); fData(mset_flagged)$Flag50```5152## Evaluation signals5354- Flag column is present in fData with boolean or flag-code values for all features; no missing values in the Flag column.55- Flagged feature count and proportion match expectations based on the conservative thresholds (RSD 0.1, RSD* 0.1, D-ratio 0.1)—typically a higher proportion than recommended thresholds would flag.56- Quality metric distributions (RSD, D-ratio, detection rate histograms) show clear separation between flagged and unflagged populations, with flagged features concentrated at the high end of the metric range.57- Spot-check: manually verify 5–10 flagged features by examining their RSD, RSD*, and D-ratio values to confirm they exceed at least one threshold.58- Unflagged features should have RSD < 0.1 AND RSD* < 0.1 AND D-ratio < 0.1 (all three conditions must be satisfied to pass).5960## Limitations6162- Conservative thresholds (0.1 for all metrics) may be overly stringent for exploratory metabolomics; 30–50% of features may be flagged depending on data quality, potentially excluding true biology.63- D-ratio calculation assumes adequate separation between QC (technical replicates) and biological samples; poor experimental design or confounding variables will invalidate the metric.64- RSD and RSD* are sensitive to low-abundance features and features with missing values; imputation strategy applied post-flagging can alter the reliability assessment retroactively.65- The function does not account for feature-specific biological variability; a feature with high D-ratio may reflect genuine biological heterogeneity rather than poor QC performance.66- Recommended thresholds (RSD 0.2, D-ratio 0.4) are dataset- and platform-dependent; the choice of 0.1 should be justified by piloting or reference materials.6768## Evidence6970- [other] The flag_quality function flags features based on quality metrics using conservative limits of 0.1 for classic RSD, RSD*, and basic D-ratio (compared to recommended thresholds of 0.2 for RSD and 0.4 for D-ratio), producing a Flag column on the MetaboSet object to mark low-quality features.: "The flag_quality function flags features based on quality metrics using conservative limits of 0.1 for classic RSD, RSD*, and basic D-ratio (compared to recommended thresholds of 0.2 for RSD and 0.4"71- [other] Apply flag_quality function with conservative limit of 0.1 for classic RSD, RSD* (robust version using median absolute deviation), and basic D-ratio to flag features where internal spread or QC-versus-biological spread exceeds thresholds.: "Apply flag_quality function with conservative limit of 0.1 for classic RSD, RSD* (robust version using median absolute deviation), and basic D-ratio to flag features where internal spread or"72- [readme] Identifying and flagging (or removing) low-quality molecular features using quality metrics defined by Broadhurst et al.: "Identifying and flagging (or removing) low-quality molecular features using quality metrics"73- [readme] Drift correction: correcting for systematic drift in the intensity of molecular features using cubic spline correction: "After peak picking with the dedicated software, we use R for data preprocessing, quality control, statistical analysis and visualization"74- [other] flag_quality is used to flag features based on the other quality metrics: "flag_quality is used to flag features based on the other quality metrics"
HolobiomicsLab