Find doublets/multiplets using bcds.

Source: R/scds_doubletdetection.R

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"
)

Arguments

inSCE

A SingleCellExperiment object. Needs counts in assays slot.

sample

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

Seed for the random number generator. Default 12345.

ntop

See bcds for more information. Default 500.

srat

See bcds for more information. Default 1.

verb

See bcds for more information. Default FALSE.

retRes

See bcds for more information. Default FALSE.

nmax

See bcds for more information. Default "tune".

varImp

See bcds for more information. Default FALSE.

estNdbl

See bcds for more information. Default FALSE.

useAssay

A string specifying which assay in the SCE to use.

Value

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.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runBcds(sce)
#> Thu Apr 28 11:27:41 2022 ... Running 'bcds'
#> [11:27:43] WARNING: amalgamation/../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.