A wrapper function for bcds. 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
.
runBcds(
inSCE,
sample = NULL,
seed = 12345,
ntop = 500,
srat = 1,
verb = FALSE,
retRes = FALSE,
nmax = "tune",
varImp = FALSE,
estNdbl = FALSE,
useAssay = "counts"
)
A SingleCellExperiment object.
Needs counts
in assays slot.
Character vector. Indicates which sample each cell belongs to. bcds will be run on cells from each sample separately. If NULL, then all cells will be processed together. Default NULL.
Seed for the random number generator. Default 12345.
See bcds for more information. Default 500
.
See bcds for more information. Default 1
.
See bcds for more information. Default FALSE
.
See bcds for more information. Default FALSE
.
See bcds for more information. Default "tune"
.
See bcds for more information. Default FALSE
.
See bcds for more information. Default FALSE
.
A string specifying which assay in the SCE to use.
A SingleCellExperiment object with
bcds output appended to the
colData slot. The columns include
bcds_score and optionally bcds_call. Please refer to the documentation of bcds for details.
data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runBcds(sce)
#> Tue Jun 28 22:06:17 2022 ... Running 'bcds'