R/runBatchCorrection.R
runSCMerge.Rd
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
)
SingleCellExperiment inherited object. Required.
A single character indicating the name of the assay requiring
batch correction. Default "logcounts"
.
A single character indicating a field in
colData
that annotates the batches.
Default "batch"
.
A single characeter. The name for the corrected assay. Will
be saved to assay
. Default
"scMerge"
.
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
.
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
.
A single character. A string indicating a field in
colData(inSCE)
that defines different cell types. Default
'cell_type'
.
An integer. The number of cores of processors to allocate for
the task. Default 1L
.
The input SingleCellExperiment object with
assay(inSCE, assayName)
updated.
Hoa, et al., 2020