runSCMerge {singleCellTK} | R Documentation |
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 )
inSCE |
SingleCellExperiment inherited object. Required. |
useAssay |
A single character indicating the name of the assay requiring
batch correction. Default |
batch |
A single character indicating a field in
|
assayName |
A single characeter. The name for the corrected assay. Will
be saved to |
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 |
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 |
cellType |
A single character. A string indicating a field in
|
nCores |
An integer. The number of cores of processors to allocate for
the task. Default |
The input SingleCellExperiment object with
assay(inSCE, assayName)
updated.
Hoa, et al., 2020
data('sceBatches', package = 'singleCellTK') ## Not run: logcounts(sceBatches) <- log(counts(sceBatches) + 1) sceCorr <- runSCMerge(sceBatches) ## End(Not run)