Apply ComBat batch effect correction method to SingleCellExperiment object

The ComBat batch adjustment approach assumes that batch effects represent non-biological but systematic shifts in the mean or variability of genomic features for all samples within a processing batch. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects.

runComBat(
  inSCE,
  useAssay = "logcounts",
  batch = "batch",
  par.prior = TRUE,
  covariates = NULL,
  mean.only = FALSE,
  ref.batch = NULL,
  assayName = "ComBat"
)

Arguments

inSCE	SingleCellExperiment inherited object. Required.
useAssay	A single character indicating the name of the assay requiring batch correction. Default "logcounts".
batch	A single character indicating a field in colData that annotates the batches. Default "batch".
par.prior	A logical scalar. TRUE indicates parametric adjustments will be used, FALSE indicates non-parametric adjustments will be used. Default TRUE.
covariates	List of other column names in colData to be added to the ComBat model as covariates. Default NULL.
mean.only	If TRUE ComBat only corrects the mean of the batch effect. Default FALSE.
ref.batch	If given, will use the selected batch as a reference for batch adjustment. Default NULL.
assayName	A single characeter. The name for the corrected assay. Will be saved to assay. Default "ComBat".

Value

The input SingleCellExperiment object with assay(inSCE, assayName) updated.

Examples

if (FALSE) {
data('sceBatches', package = 'singleCellTK')
# parametric adjustment
sceCorr <- runComBat(sceBatches)
# non-parametric adjustment, mean-only version
sceCorr <- runComBat(sceBatches, par.prior=FALSE, mean.only=TRUE)
# reference-batch version, with covariates
sceCorr <- runComBat(sceBatches, covariates = "cell_type", ref.batch = 'w')
}