Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.
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Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
Valid drift correction requires QC injections that bracket the samples at both ends and sample the drift curve (~1 QC every 5-10 injections); conditioning injections must be excluded. Valid batch correction requires biological groups randomized across batches; a confounded design cannot be rescued by any algorithm.
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Metabolomics Normalization and QC
"Normalize my metabolomics data and correct for batch effects" -> Filter junk features by QC quality, correct within-batch drift against injection order, normalize per-sample dilution, and impute by missingness mechanism -- each step verified against held-out QCs, not just QC clustering.
R drift correction: QCRSC() (pmp), shiftCor() (statTarget)
R normalization: pqn_normalisation() (pmp)
R imputation: mv_imputation() (pmp), impute.QRILC() (imputeLCMD), missForest() (missForest)
The Single Most Important Insight -- Normalization Is a Modeling Decision, Not a Cleanup Step
Every preprocessing step imposes an assumption about where the unwanted variance lives; if that assumption is wrong the result is not noisier, it is confidently wrong. Three corollaries reorganize the whole skill. (1) QC-based correction assumes the pooled QC's per-feature drift trajectory is the samples' trajectory -- false for subgroup-specific features (the pool dilutes them toward absence) and for features at different abundance in samples vs pool (suppression is concentration-dependent), so correcting them extrapolates from noise. (2) The order/batch/biology confound is information-theoretically unwinnable post hoc: if group is collinear with batch or injection order, no estimator can attribute the shared variance to one source -- it only redistributes it, wrongly. Randomization at the bench is the only real fix. (3) Over-correction is invisible to the metric everyone reports: "QC RSD dropped / QCs cluster tighter" is exactly what a too-flexible model games (a cubic spline threading every QC drives QC RSD to ~0% while raising biological-sample RSD). Validate on held-out QCs and dilution-QC linearity, never on the metric the model optimized.
QC-anchored offsets removed after within-batch drift is flat
median-of-QC / batchCorr
5
Missing-value imputation
Filter aggressively first, then impute only the sparse residual holes by mechanism
mv_imputation (pmp), impute.QRILC, missForest
6
Sample normalization
Dilution correction on quality features, after junk removed
pqn_normalisation (pmp)
7
Transformation + scaling
Defers to metabolomics/statistical-analysis
glog_transformation (pmp)
Detection-rate filtering must precede imputation: never impute a feature that is 90% missing, which would fabricate 90% of it.
Decision Tree -- Sample Normalization by Matrix
Matrix / situation
Use
Why
Urine / variable-dilution biofluid
PQN or MSTUS (osmolality/SG if measured)
Dilution varies wildly; PQN's median-quotient isolates the common dilution factor; MSTUS excludes drug/diet xenobiotics that corrupt TIC
Plasma / serum
PQN or median (TIC only if no dominant peak)
Volume relatively constant; closure risk lower but still present
Tissue / cells
Per measured amount (mass, protein, cell count) at the bench
The confounder (input amount) is known -- more honest than any data-driven post-hoc method
Targeted / few analytes
Per-class internal standards
One IS cannot represent all chemical classes/RT regions
Global profile genuinely differs between groups
Avoid quantile normalization
It forces all samples to one distribution, erasing real distributional biology
Creatinine for urine
Avoid as sole method
Fails under renal impairment / muscle-mass differences (Warrack 2009)
When >50% of features move coherently (potent drug, gross pathology), the PQN median-quotient measures the biology, not dilution, and subtracts it out -- switch to a measured external quantity and check whether the normalization factor correlates with the phenotype.
Decision Tree -- Drift Correction Method
Situation
Do
Why
Smooth monotonic drift, frequent QCs, small/medium study
QCRSC (spline) or QC-RLSC
Per-feature fit vs order; CV-select span to avoid overfit
Non-smooth / multi-pattern drift within a batch
QC-RFSC (statTarget) or batchCorr clusters
RF / cluster-based captures non-monotonic trend
Large cohort (>~500), complex multi-source error, want lowest RSD
SERRF
Borrows strength across correlated features (~5% RSD on >800-sample cohorts, Fan 2019)
Sparse QCs (<5-6 spanning the batch)
Coarse median-of-QC offset or no within-batch correction
LOESS/spline with too few QCs produces gaps/garbage
Feature weak/absent in QCs
Exclude from correction
Correcting it extrapolates from noise
No detectable drift in a feature
Do not correct it
Correcting a flat QC trajectory only adds the model's wiggle
Run order confounded with biology
Do not drift-correct; fix design or caveat
A smooth function of order absorbs and subtracts the biological trend
Flexible ML methods (SERRF/RF/adversarial) win on large complex cohorts but are more prone to learning-and-removing biology that tracks order/batch. Always confirm QC RSD dropped AND biological-sample RSD did not rise.
Filter Features by QC Quality (RSD and D-ratio)
Goal: Keep only reproducible features whose technical variance is small relative to biological variance.
Approach: Compute per-feature QC RSD and the robust D-ratio (technical SD / biological SD), then apply a boolean mask. Lead with D-ratio: CV alone is matrix-blind, scoring a precisely-measured-but-flat feature as good and a noisy-but-biologically-huge feature as bad.
Goal: Flatten per-feature, injection-order-dependent signal drift using the QC trajectory.
Approach: Fit a QC-robust smoothing spline of intensity vs injection order per feature, interpolate at every sample position, and divide. pmp's QCRSC selects the spline smoothing by leave-one-out CV when spar=0, requires minQC QCs per batch, and excludes features too weak in QC automatically.
library(pmp)# df: features in ROWS, samples in COLUMNS (pmp convention)
corrected <- QCRSC(df = feature_matrix, order = injection_order, batch = batch_id,
classes = sample_class, spar =0,log=TRUE,
minQC =5, qc_label ='QC')# Verify correction worked on HELD-OUT QCs / dilution linearity, not on QC clustering.
statTarget alternative (MLmethod='QCRFSC' for RF, 'QCRLSC' for LOESS; QCspan=0 auto-GCV span applies to QCRLSC; inputs are two order-aligned CSVs):
Goal: Remove per-sample global intensity differences (dilution, extraction efficiency) without subtracting genuine fold changes.
Approach: Build a reference spectrum (median of QCs), compute per-feature sample/reference quotients, take the median quotient as the dilution factor, and divide. The median is robust because it ignores the minority of genuinely-changed features.
library(pmp)# df: features in ROWS, samples in COLUMNS; reference built from QC samples
normalized <- pqn_normalisation(df = feature_matrix, classes = sample_class,
qc_label ='QC')
Impute by Missingness Mechanism
Goal: Fill residual sparse holes with the method matched to why the value is missing.
Approach: Diagnose the mechanism per feature -- missingness correlated with low abundance is MNAR (left-censored) and needs QRILC/GSimp; sporadic missingness across the abundance range is MAR and needs RF/kNN. Using a MAR method on MNAR zeros pulls the censored group's mean up and erases the on/off signal.
library(imputeLCMD)
library(missForest)# MNAR / left-censored: random draws from a fitted truncated-normal (features in ROWS)
qrilc_imputed <- impute.QRILC(feature_matrix_features_in_rows, tune.sigma =1)[[1]]# MAR / sporadic: iterative random-forest prediction (samples in ROWS, features in COLS)
rf_imputed <- missForest(sample_by_feature_matrix, maxiter =10, ntree =100)$ximp
Half-min imputation collapses the imputed subset's variance to zero, understating SE and inflating false significance -- prefer QRILC/GSimp, which draw a distribution of plausible low values. Re-run key results under >=2 imputation methods; if headline metabolites flip, the finding lives in the imputation.
Per-Method Failure Modes
QC-spline / LOESS over-correction
Trigger: Span too small, too few QCs, or a cubic spline using each QC as a lock-point.
Mechanism: The fit threads the QCs perfectly, modeling inter-QC noise as drift and injecting it into samples.
Symptom: QC RSD drops toward 0% while biological-sample RSD rises and the number of significant features explodes.
Fix: CV-select the span, validate on held-out QCs and dilution-QC linearity, compare biological-sample RSD before/after, not just QC RSD.
Edge extrapolation
Trigger: Samples injected before the first QC or after the last QC.
Mechanism: LOESS/spline interpolate between QCs but extrapolate beyond them, producing unstable correction factors.
Fix: Bracket samples with QCs at both ends; exclude conditioning injections.
TIC closure artifact
Trigger: One dominant or up-regulated feature under TIC/sum normalization.
Mechanism: The constant-sum constraint forces every other feature's normalized value down, manufacturing apparent coordinated down-regulation (Aitchison closure; spurious negative correlations).
Fix: Use PQN/MSTUS or log-ratios; if "many features moved together," suspect closure from one big mover before believing coordination.
ComBat under imbalance
Trigger: Biological groups unbalanced across batches.
Mechanism: Empirical-Bayes shrinkage confounds class with batch; under imbalance it can fabricate thousands of false differences or, without the covariate, delete real ones (Nygaard 2016).
Fix: Prefer QC-anchored between-batch alignment (the pool has no group, so it cannot confound). Reserve ComBat for balanced designs and always pass the biological covariate via mod=. Randomize so it is never needed.
Wrong imputation mechanism
Trigger: kNN/RF applied to below-LOD (MNAR) values.
Mechanism: MAR methods borrow abundance from detected samples, pulling the censored group's mean up and shrinking the very difference under test.
Fix: Diagnose mechanism per feature; QRILC/GSimp for left-censored, RF/kNN only for sporadic MAR.
Quantitative Thresholds
Threshold
Source
Rationale
QC RSD <= 20-30% (15% gold)
Dunn 2011; Broadhurst 2018
Reproducibility floor in the matrix-matched pool; 20% aspirational for LC-MS, 30% common
D-ratio <= 0.5 (0.2 excellent), robust/MAD form
Broadhurst 2018
Technical SD < biological SD -- the honest, matrix-aware filter; MAD form because MS intensities are right-skewed
Blank ratio >= 3-5x
community convention (Dunn lineage)
Features below 3-5x blank are dominated by background/carryover, not biology
Detection rate >= 50-80% (or 80% within any one group)
Reliable signal; "within any group" preserves on/off group-specific metabolites
Dilution-QC correlation r >= 0.7-0.8
community convention
Real metabolites scale with dilution; artefacts/in-source ions do not
QCs >= 5-10% of injections, ~1 every 5-10 samples
Broadhurst 2018; mQACC 2022
Must sample the drift curve densely enough to avoid LOESS extrapolation
Thresholds are conventions, not laws: choose them a priori, report each one, and report how many features each filter removed (mQACC reporting standard).
Common Errors
Error / symptom
Cause
Solution
could not find function "statTarget"
No such entry point
Use shiftCor() (drift correction) and statAnalysis() (post-hoc stats)
mv_imputation errors on method='sm'
Small-value method is 'sv', not 'sm'
Use method='sv' (also valid: knn, rf, bpca, mn, md)
QRILC output is malformed
impute.QRILC returns a list, not a matrix; expects features in rows
Index [[1]]; transpose so features are rows
MetaboAnalystR Normalization errors
SanityCheckData(mSet) not run first
Call SanityCheckData -> ReplaceMin -> Normalization in order
Correction made data worse
Span overfit / weak-in-QC features corrected / order confounded with biology
Back off span, exclude weak-in-QC features, check randomization
Effect vanished after drift correction
Run order confounded with group; trend absorbed the biology
Check the design; report drift and effect as inseparable if confounded
References
Dunn WB, Broadhurst D, Begley P, et al. 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 6:1060-1083.
Broadhurst D, Goodacre R, Reinke SN, et al. 2018. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14:72.
Dieterle F, Ross A, Schlotterbeck G, Senn H. 2006. Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics. Analytical Chemistry 78:4281-4290.
Fan S, Kind T, Cajka T, et al. 2019. Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data. Analytical Chemistry 91:3590-3596.
Brunius C, Shi L, Landberg R. 2016. Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics 12:173.
Luan H, Ji F, Chen Y, Cai Z. 2018. statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. Analytica Chimica Acta 1036:66-72.
Wei R, Wang J, Su M, et al. 2018. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Scientific Reports 8:663.
Wei R, Wang J, Jia E, et al. 2018. GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies. PLoS Computational Biology 14:e1005973.
Stekhoven DJ, Buhlmann P. 2012. MissForest -- non-parametric missing value imputation for mixed-type data. Bioinformatics 28:112-118.
Nygaard V, Rodland EA, Hovig E. 2016. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17:29-39.
van den Berg RA, Hoefsloot HCJ, Westerhuis JA, et al. 2006. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142.
Warrack BM, Hnatyshyn S, Ott KH, et al. 2009. Normalization strategies for metabonomic analysis of urine samples. Journal of Chromatography B 877:547-552.
Thonusin C, IglayReger HB, Soni T, et al. 2017. Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data. Journal of Chromatography A 1523:265-274.
Chamberlain CA, Rubio VY, Garrett TJ. 2019. Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics. Metabolomics 15:135.
Wehrens R, Hageman JA, van Eeuwijk F, et al. 2016. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12:88.
Related Skills
metabolomics/xcms-preprocessing - Generates the feature table this skill consumes
metabolomics/msdial-preprocessing - Alternative feature-table source
metabolomics/statistical-analysis - Transformation/scaling and downstream multivariate stats
experimental-design/batch-design - Randomization and design that make correction valid
differential-expression/batch-correction - ComBat/SVA mechanics shared with transcriptomics
