runComBat: Apply ComBat batch effect correction method to...
In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description Usage Arguments Value Examples

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"
)

`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"`.

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

## Not run: 
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')

## End(Not run)