MiDAR provides functions for run-order drift and batch
correction. The correction is based on user-selected reference sample
types (qc_types), based on which all other samples are
adjusted. The corrections can be applied to intensity,
norm_intensity, or conc data.
Drift correction (smoothing)
Following drift correction method are available in
MiDAR, 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.
Note: 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 | MiDAR function | Details |
|---|---|---|
| Cubic Spline | correct_dr ift_cubicspline() |
Cubic spline with smoothing parameter being either determined using
cross-validation or set as fixed across all features. Typically used with QC samples as references |
| Loess |
correct _drift_loess(). |
Loess smoothing with span set as fixed value for all features. Typically used with QC samples as reference |
| Ga ussian Kernel | correct_drift _gaussiankernel() |
Gaussian kernel smoothing with fixed kernel size for all features. Option to smooth the scale (variability) as well. Typically used with study samples as reference. Only suitable for large sample numbers that are well-randomized/stratified. |
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 (centering)
MiDAR currently supports median centering-based batch
correction correct_batch_centering()\, whereby the scale of
the batches can optioanally also be normalized. The selected QC types
(ref_qc_types) are used to calculate the medians, which are
then used to align all other samples.
See the tutorial Drift and Batch Correction for more information.