runDoubletCells: Detect doublet cells using scDblFinder.

In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description

A wrapper function for scDblFinder. Identify potential doublet cells based on simulations of putative doublet expression profiles. Generate a doublet score for each cell.

Usage

runDoubletCells(
  inSCE,
  sample = NULL,
  useAssay = "counts",
  nNeighbors = 50,
  simDoublets = max(10000, ncol(inSCE)),
  seed = 12345,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

inSCE	A SingleCellExperiment object.
sample	Character vector. Indicates which sample each cell belongs to. scDblFinder will be run on cells from each sample separately.
useAssay	A string specifying which assay in the SCE to use.
nNeighbors	Number of nearest neighbors used to calculate density for doublet detection. Default 50.
simDoublets	Number of simulated doublets created for doublet detection. Default 10000.
seed	Seed for the random number generator. Default 12345.
BPPARAM	A BiocParallelParam object specifying whether the neighbour searches should be parallelized.

Details

This function is a wrapper function for scDblFinder. runDoubletCells runs scDblFinder for each sample within inSCE iteratively. The resulting doublet scores for all cells will be appended to the colData of inSCE.

Value

A SingleCellExperiment object with the 'scran_doubletCells_score' column added to the colData slot.

References

Lun ATL (2018). Detecting doublet cells with scran. https://ltla.github.io/SingleCellThoughts/software/doublet_detection/bycell.html

See Also

doubletCells

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runDoubletCells(sce)

singleCellTK documentation built on Nov. 8, 2020, 5:21 p.m.