This function performs batch centering correction on each feature.
Optionally, the scale of the batches can be equalized across batches.
The selected QC types (ref_qc_types) are used to calculate
the medians, which are then used to align all other samples. The
correction can be applied to one of three variables: "intensity",
"norm_intensity", or "conc". The correction can either be applied
on top of previous corrections or replace all prior batch corrections.
Usage
correct_batch_centering(
data = NULL,
variable,
ref_qc_types,
correct_scale = FALSE,
replace_previous = TRUE,
log_transform_internal = TRUE,
replace_exisiting_trendcurves = FALSE,
...
)Arguments
- data
A
MidarExperimentobject containing the data to be corrected. This object must include information about QC types and measurements.- variable
The variable to be corrected. Must be one of "intensity", "norm_intensity", or "conc".
- ref_qc_types
A character vector specifying the QC types to be used as references for batch centering.
- correct_scale
A logical value indicating whether to equalize the scale of the batches in addition to center them. Defaults to
FALSE.- replace_previous
A logical value indicating whether to replace any previous batch corrections or apply the new correction on top. Defaults to
TRUE(replace).- log_transform_internal
A logical value indicating whether to log-transform the data internally during correction. Defaults to
TRUE.- replace_exisiting_trendcurves
A logical value indicating whether to replace trend curves from previous corrections. This is only use for plotting using
plot_runscatter(). Default isFALSE.- ...
Additional arguments that can be passed to the batch correction function.