Find doublets/multiplets using cxds_bcds_hybrid.

Source: R/scds_doubletdetection.R

A wrapper function for cxds_bcds_hybrid. Annotate doublets/multiplets using a binary classification approach to discriminate artificial doublets from original data. Generate a doublet score for each cell. Infer doublets if estNdbl is TRUE.

runCxdsBcdsHybrid(
  inSCE,
  sample = NULL,
  seed = 12345,
  nTop = 500,
  cxdsArgs = list(),
  bcdsArgs = list(),
  verb = FALSE,
  estNdbl = FALSE,
  force = FALSE,
  useAssay = "counts"
)

Arguments

inSCE

A SingleCellExperiment object. Needs counts in assays slot.

sample

Character vector. Indicates which sample each cell belongs to. cxds_bcds_hybrid will be run on cells from each sample separately. If NULL, then all cells will be processed together. Default NULL.

seed

Seed for the random number generator. Default 12345.

nTop

The number of top varialbe genes to consider. Used in both csds and bcds. Default 500.

cxdsArgs

See cxds_bcds_hybrid for more information. Default NULL.

bcdsArgs

See cxds_bcds_hybrid for more information. Default NULL.

verb

See cxds_bcds_hybrid for more information. Default FALSE.

estNdbl

See cxds_bcds_hybrid for more information. Default FALSE.

force

See cxds_bcds_hybrid for more information. Default FALSE.

useAssay

A string specifying which assay in the SCE to use.

Value

A SingleCellExperiment object with

cxds_bcds_hybrid output appended to the

colData slot. The columns include

hybrid_score and optionally hybrid_call. Please refer to the documentation of cxds_bcds_hybrid for details.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runCxdsBcdsHybrid(sce)
#> Sat Mar 18 10:30:31 2023 ... Running 'cxds_bcds_hybrid'