Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object

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"
)

Arguments

inSCE: SingleCellExperiment inherited object. Required.
useAssay: 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".
batch: A single character indicating a field in colData that annotates the batches. Default "batch".
nHVG: An integer. Number of highly variable genes to use when fitting the model. Default 1000L.
nComponents: An integer. The number of principle components or dimensionality to generate in the resulting matrix. Default 50L.
epsilon: An integer. Algorithmic parameter. Empirically, a high epsilon is often required to obtained a good low-level representation. Default 1000L.
nIter: An integer, The max number of iterations to perform. Default 10L.
reducedDimName: A single character. The name for the corrected low-dimensional representation. Will be saved to reducedDim(inSCE). Default "zinbwave".

Value

The input SingleCellExperiment object with reducedDim(inSCE, reducedDimName) updated.

References

Pollen, Alex A et al., 2014

Examples

data('sceBatches', package = 'singleCellTK')
if (FALSE) {
    sceCorr <- runZINBWaVE(sceBatches, nIter = 5)
}