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Correct drift using cubic spline

Source: R/drift_correction.R
correct_drift.Rd

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",
  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 SummarizedExperiment object

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

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

Value

A SummarizedExperiment 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 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(toy_notame_set)
corrected <- correct_drift(mark_nas(toy_notame_set[1:5, ], value = 0))
#> INFO [2025-10-29 12:02:54] Starting drift correction
#> INFO [2025-10-29 12:02:57] Recomputing quality metrics for drift corrected data
#> INFO [2025-10-29 12:02:57] Drift correction performed
#> INFO [2025-10-29 12:02:57] Inspecting drift correction results
#> INFO [2025-10-29 12:02:57] Original quality metrics missing, recomputing
#> INFO [2025-10-29 12:02:57] Drift correction results inspected: Drift_corrected: 100%