A wrapper function for applying cubic spline drift correction and saving before and after plots.
Usage
correct_drift(
object,
log_transform = TRUE,
spar = NULL,
spar_lower = 0.5,
spar_upper = 1.5,
check_quality = FALSE,
condition = "RSD_r < 0 & D_ratio_r < 0",
plotting = FALSE,
file = NULL,
width = 16,
height = 8,
color = "QC",
shape = color,
color_scale = getOption("notame.color_scale_dis"),
shape_scale = scale_shape_manual(values = c(15, 16)),
assay.type = NULL,
name = NULL
)Arguments
- object
a
SummarizedExperimentorMetaboSetobject- log_transform
logical, should drift correction be done on log-transformed values? See Details
- spar
smoothing parameter as in
smooth.spline- spar_lower, spar_upper
lower and upper limits for the smoothing parameter
- check_quality
logical, whether quality should be monitored.
- condition
a character specifying the condition used to decide whether drift correction works adequately, see Details
- plotting
logical, whether plots should be drawn
- file
path to the PDF file where the plots should be saved
- width, height
width and height of the plots in inches
- color
character, name of the column used for coloring the points
- shape
character, name of the column used for shape
- color_scale, shape_scale
the color and shape scales as returned by a ggplot function
- assay.type
character, assay to be used in case of multiple assays
- name
character, name of the resultant assay in case of multiple assays
Value
A SummarizedExperiment or MetaboSet object as the one supplied, with drift corrected features.
Details
If log_transform = TRUE, the correction will be done on
log-transformed values.
The correction formula depends on whether the correction is run on original
values or log-transformed values.
In log-space: \(corrected = original + mean of QCs - prediction by cubic
spline\).
In original space: \(corrected = original * prediction for first QC /
prediction for current point\).
We recommend doing the correction in the log-space since the log-transfomred
data better follows the assumptions of cubic spline regression. The drift
correction in the original space also sometimes results
in negative values, and results in rejection of the drift corrrection
procedure.
If spar is set to NULL (the default), the smoothing parameter
will be separately chosen for each feature from the range
[spar_lower, spar_upper] using cross validation.
If check_quality = TRUE, the condition parameter should be a
character giving a condition compatible with filter.
The condition is applied on the changes in the quality metrics
RSD, RSD_r, D_ratio and D_ratio_r. For example, the default is "RSD_r < 0
and D_ratio_r < 0", meaning that both RSD_r and D_ratio_r need to decrease
in the drift correction, otherwise the drift corrected feature is discarded
and the original is retained.
By default, the column used for color is also used for shape.
See also
smooth.spline for details about the regression
Examples
data(example_set)
corrected <- correct_drift(example_set)
#> INFO [2025-06-23 22:36:30]
#> Starting drift correction at 2025-06-23 22:36:30.643302
#> INFO [2025-06-23 22:36:30] Zero values in feature abundances detected. Zeroes will be replaced with 1.1.
#> INFO [2025-06-23 22:36:31] Drift correction performed at 2025-06-23 22:36:31.005327
#> INFO [2025-06-23 22:36:31] Inspecting drift correction results 2025-06-23 22:36:31.173394
#> INFO [2025-06-23 22:36:31] Drift correction results inspected at 2025-06-23 22:36:31.449137
#> INFO [2025-06-23 22:36:31]
#> Drift correction results inspected, report:
#> Drift_corrected: 100%