runDoubletFinder: Generates a doublet score for each cell via doubletFinder

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

Description

Uses doubletFinder to determine cells within the dataset suspected to be doublets.

Usage

runDoubletFinder(
  inSCE,
  useAssay = "counts",
  sample = NULL,
  seed = 12345,
  seuratNfeatures = 2000,
  seuratPcs = 1:15,
  seuratRes = 1.5,
  formationRate = 0.075,
  nCores = NULL,
  verbose = FALSE
)

Arguments

inSCE	Input SingleCellExperiment object. Must contain a counts matrix
useAssay	Indicate which assay to use. Default "counts".
sample	Numeric vector. Each cell will be assigned a sample number.
seed	Seed for the random number generator. Default 12345.
seuratNfeatures	Integer. Number of highly variable genes to use. Default 2000.
seuratPcs	Numeric vector. The PCs used in seurat function to determine number of clusters. Default 1:15.
seuratRes	Numeric vector. The resolution parameter used in seurat, which adjusts the number of clusters determined via the algorithm. Default 1.5.
formationRate	Doublet formation rate used within algorithm. Default 0.075.
nCores	Number of cores used for running the function.
verbose	Boolean. Wheter to print messages from Seurat and DoubletFinder. Default FALSE.

Value

SingleCellExperiment object containing the 'doublet_finder_doublet_score'.

Examples

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

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