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",
  hvgExprs = "counts",
  seg = NULL,
  kmeansK = NULL,
  cellType = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

inSCE

Input SingleCellExperiment object

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".

hvgExprs

A single characeter. The assay that to be used for highly variable genes identification. Default "counts".

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 with segList or segList_ensemblGeneID. 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 kmeansK 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'.

BPPARAM

A BiocParallelParam object specifying whether should be parallelized. Default BiocParallel::SerialParam().

Value

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

References

Hoa, et al., 2020

Examples

data('sceBatches', package = 'singleCellTK')
if (FALSE) { # \dontrun{
logcounts(sceBatches) <- log1p(counts(sceBatches))
sceCorr <- runSCMerge(sceBatches)
} # }