Apply scMerge batch effect correction method to SingleCellExperiment object

Source: R/runBatchCorrection.R

The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple scRNA-Seq data.

runSCMerge(
  inSCE,
  useAssay = "logcounts",
  batch = "batch",
  assayName = "scMerge",
  seg = NULL,
  kmeansK = NULL,
  cellType = "cell_type",
  nCores = 1L
)

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".

assayName

A single characeter. The name for the corrected assay. Will be saved to assay. Default "scMerge".

seg

A vector of gene names or indices that specifies SEG (Stably Expressed Genes) set as negative control. Pre-defined dataset with human and mouse SEG lists is available to user by running data('SEG'). Default NULL, and this value will be auto-detected by default with scSEGIndex.

kmeansK

An integer vector. Indicating the kmeans' K-value for each batch (i.e. how many subclusters in each batch should exist), in order to construct pseudo-replicates. The length of codekmeansK needs to be the same as the number of batches. Default NULL, and this value will be auto-detected by default, depending on cellType.

cellType

A single character. A string indicating a field in colData(inSCE) that defines different cell types. Default 'cell_type'.

nCores

An integer. The number of cores of processors to allocate for the task. Default 1L.

Value

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

References

Hoa, et al., 2020

Examples

data('sceBatches', package = 'singleCellTK')
if (FALSE) {
logcounts(sceBatches) <- log(counts(sceBatches) + 1)
sceCorr <- runSCMerge(sceBatches)
}