Skip to contents

Drift Correction by Cubic Spline Smoothing

Source: R/correct-drift-batch.R
correct_drift_cubicspline.Rd

This function corrects for run-order drifts within or across batches using cubic spline smoothing. The correction is typically based on QC (Quality Control) samples that are measured at specific intervals throughout the run sequence. The smoothed curve derived from the QC samples is then used to adjust all other samples in the dataset. The correction can be applied to "intensity", "norm_intensity", or "conc" data.

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. (Nat Protoc, 2011) and Kirwan et al. (Anal Bioanal Chem, 2014).

By default, the smoothing parameter is determined using cross-validation, which can lead to overfitting. To reduce overfitting the regularization parameter lambda may be defined, with a good starting point being lambda = 0.01. Additionally, the global smoothing parameter can be specified via spar.

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().

The corrections can be applied on a batch-by-batch basis (batch_wise = TRUE, default) or across all batches (batch_wise = FALSE). Existing corrections are either replaced (replace_previous = TRUE) or added on top of them (replace_previous = FALSE).

Furthermore, drift correction can be applied unconditionally (conditional_correction = FALSE) or conditionally, based on whether the sample CV change before and after correction is below a defined threshold (cv_diff_threshold). This conditional correction is assessed independently for each batch if batch_wise = TRUE, where the median of the CV changes across the batch is compared with the threshold.

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 Details below).

This cubic spline method is implemented using the base R function

Usage

correct_drift_cubicspline(
  data = NULL,
  variable,
  ref_qc_types,
  batch_wise = TRUE,
  ignore_istd = TRUE,
  replace_previous = TRUE,
  cv = TRUE,
  spar = NULL,
  lambda = NULL,
  penalty = 1,
  conditional_correction = FALSE,
  recalc_trend_after = FALSE,
  log_transform_internal = TRUE,
  feature_list = NULL,
  cv_diff_threshold = 0,
  use_original_if_fail = FALSE
)

Arguments

data

MidarExperiment object

variable

The variable to be corrected for drift effects. Must be one of "intensity", "norm_intensity", or "conc"

ref_qc_types

QC types used for drift correction

batch_wise

Logical. Apply the correction to each batch separately (TRUE, default) or across all batches (FALSE).

ignore_istd

Logical. Exclude internal standards (ISTDs) from correction if TRUE.

replace_previous

Logical. Replace existing correction (TRUE, default) or layer on top of it (FALSE).

cv

Ordinary leave-one-out (TRUE) or ‘generalized’ cross-validation (GCV) when FALSE; is used for smoothing parameter computation only when spar is not specified

spar

Smoothing parameter for cubic spline smoothing. If not specified or NULL, the smoothing parameter is computed using the specified cv method. Typically (but not necessarily) in in (0,1].

lambda

Regularization parameter for cubic spline smoothing. Default is 0, which means no regularization.

penalty

The coefficient of the penalty for degrees of freedom in the GCV criterion.

conditional_correction

Determines whether drift correction should be applied to all features unconditionally (TRUE) or conditionally, based on sample CV change.

recalc_trend_after

Recalculate trend post-drift correction for plot_qc_runscatter(). This will double calculation time.

log_transform_internal

Log transform the data for correction when TRUE (the default). Note: log transformation is solely applied internally for smoothing, results will not be log-transformed.

feature_list

Subset the features for correction whose names match the specified text using regular expression. Default is NULL.

cv_diff_threshold

Maximum allowable change in CV ratio before and after smoothing for correction to be applied.

use_original_if_fail

Determines the action when smoothing fails or results in invalid values for a feature. If FALSE (default), the result for each feature will NA for all batches, if TRUE, the original data is kept.

Value

MidarExperiment object

Details

In the output message, the median CV change is computed as the median of CV changes for all features in global correction or for features where the correction passed the defined CV difference treshold in case of conditional correction (conditional_correction = FALSE). For batch-wise correction, the change is calculated per batch, with the final median CV change being the median of these batch medians across features.

References

Dunn, W., Broadhurst, D., Begley, P. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6, 1060–1083 (2011). https://doi.org/10.1038/nprot.2011.335

Kirwan, J.A., Broadhurst, D.I., Davidson, R.L. et al. Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem 405, 5147–5157 (2013). https://doi-org.libproxy1.nus.edu.sg/10.1007/s00216-013-6856-7

See also

smooth.spline