runBcds: Find doublets/multiplets using bcds.

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

Description

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.

Usage

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)

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