Drift and batch correction

Manual

MRMhub provides functions for run-order drift and batch correction. The correction is based on user-selected reference sample types (ref_qc_types), relative to which all other samples are adjusted. The corrections can be applied to intensity, norm_intensity, or conc data.

Which samples a correction is fitted on depends on the modelling method. The QC-based smoothers (correct_drift_loess(), correct_drift_cubicspline()) are fitted on the reference sample types selected with ref_qc_types, typically QC samples, i.e. the batch QC (BQC), or the technical QC (TQC) in case of clear instrument drifts, following the QC-based paradigm (Broadhurst et al. 2018). The gaussian-kernel method (correct_drift_gaussiankernel()) instead fits on the study samples and is only appropriate for large, well-randomised cohorts.

Drift correction (smoothing)

The following drift correction methods are available in MRMhub, two of which are typically used for QC samples and one (gaussian kernel-based) for study samples.

Corrections can be applied on a batch-by-batch basis (batch_wise = TRUE, default) or across all batches (batch_wise = FALSE). The correction can either replace existing drift or batch corrections (replace_previous = TRUE, default) or applied on top of existing corrections (replace_previous = FALSE).

Drift correction can be applied to all features (conditional_correction = FALSE) or conditionally, based on whether the sample CV difference before and after correction is below a defined threshold (cv_diff_threshold). The conditional correction is applied separately for each batch if batch_wise = TRUE.

It is recommended to visually inspect the correction using the plot_runscatter() function. Set the argument recalc_trend_after = TRUE so that the trends after correction are also available for plotting. For further details, refer to the description of plot_runscatter(). This, however, doubles the processing time.

The function outputs a message indicating the median CV change and the mean absolute CV before and after correction for all samples. However, these metrics are experimental and should not be used as definitive criteria for correction (see function documentation).

Method	Function	Details
Cubic Spline	correct_drift_cubicspline()	Smoothing parameter determined via cross-validation or set as fixed. Typically used with QC samples as reference.
Loess	correct_drift_loess()	Loess smoothing with fixed span. Typically used with QC samples as reference.
Gaussian Kernel	correct_drift_gaussiankernel()	Fixed kernel size. Option to smooth scale (variability). Typically used with study samples as reference. Only suitable for large, well-randomized sample sets.

The cubic spline smoothing approach, particularly when used with the regularization parameter lambda, is similar but not identical to previously described QC-based drift correction methods, such as QC-RSC (Quality Control Regularized Spline Correction), described in Dunn et al. (2011) and Kirwan et al. (2013).

See the tutorial Drift and batch correction for more information on how to use these functions and plot the results.

Batch-effect correction

MRMhub provides three batch-correction methods. Batch correction is applied after normalization and, where used, drift correction.

• Median centering (correct_batch_centering()) aligns the per-batch medians of the selected QC types (ref_qc_types), following the QC-based paradigm (Broadhurst et al. 2018); the scale of the batches can optionally also be equalized. This is the default, most robust choice.
• ComBat (correct_batch_combat()) applies the empirical-Bayes location and scale model of Johnson et al. (2007), shrinking the batch estimates across features. Unlike the other two methods, ComBat estimates batch effects from all samples rather than from the reference QCs; supply covariates to protect biological signal on unbalanced designs.
• SERRF (correct_batch_serrf()) removes systematic error with per-feature random forests trained on the reference QCs, using each feature’s most correlated features as predictors (Fan et al. 2019). It captures non-linear batch and drift effects jointly and is best suited to larger panels with dense QC coverage.

correct_batch_combat() and correct_batch_serrf() are experimental. ComBat requires the optional sva package and SERRF the optional ranger package. The SERRF implementation adapts the reference code in the malbacR package (Leach et al. 2023); validate results against the reference implementation for your data.

See the tutorial Drift and batch correction for more information.

Next steps

• Drift and batch correction (tutorial): worked examples of both corrections
• Exploring QC: RunScatter and PCA: visualise run-order and batch effects

References

Broadhurst, David, Royston Goodacre, Stacey N. Reinke, 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 (6): 72. https://doi.org/10.1007/s11306-018-1367-3.

Dunn, Warwick B., David Broadhurst, Paul Begley, 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 (7): 1060–83. https://doi.org/10.1038/nprot.2011.335.

Fan, Sili, Tobias Kind, Tomas Cajka, et al. 2019. “Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data.” Analytical Chemistry 91 (5): 3590–96. https://doi.org/10.1021/acs.analchem.8b05592.

Johnson, W. Evan, Cheng Li, and Ariel Rabinovic. 2007. “Adjusting Batch Effects in Microarray Expression Data Using Empirical Bayes Methods.” Biostatistics 8 (1): 118–27. https://doi.org/10.1093/biostatistics/kxj037.

Kirwan, J. A., D. I. Broadhurst, R. L. Davidson, and M. R. Viant. 2013. “Characterising and Correcting Batch Variation in an Automated Direct Infusion Mass Spectrometry (DIMS) Metabolomics Workflow.” Analytical and Bioanalytical Chemistry 405 (15): 5147–57. https://doi.org/10.1007/s00216-013-6856-7.

Leach, Damon L., Kelly A. Trujillo, Rachel A. Richardson, et al. 2023. “malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data.” Metabolites 13 (11): 1130. https://doi.org/10.3390/metabo13111130.