R/runBatchCorrection.R
runZINBWaVE.Rd
A general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of scRNAseq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates.
runZINBWaVE(
inSCE,
useAssay = "counts",
batch = "batch",
nHVG = 1000L,
nComponents = 50L,
epsilon = 1000,
nIter = 10L,
reducedDimName = "zinbwave"
)
SingleCellExperiment inherited object. Required.
A single character indicating the name of the assay requiring
batch correction. Note that ZINBWaVE works for counts (integer) input rather
than logcounts that other methods prefer. Default "counts"
.
A single character indicating a field in
colData
that annotates the batches.
Default "batch"
.
An integer. Number of highly variable genes to use when fitting
the model. Default 1000L
.
An integer. The number of principle components or
dimensionality to generate in the resulting matrix. Default 50L
.
An integer. Algorithmic parameter. Empirically, a high epsilon
is often required to obtained a good low-level representation. Default
1000L
.
An integer, The max number of iterations to perform. Default
10L
.
A single character. The name for the corrected
low-dimensional representation. Will be saved to reducedDim(inSCE)
.
Default "zinbwave"
.
The input SingleCellExperiment object with
reducedDim(inSCE, reducedDimName)
updated.
Pollen, Alex A et al., 2014
data('sceBatches', package = 'singleCellTK')
if (FALSE) {
sceCorr <- runZINBWaVE(sceBatches, nIter = 5)
}