runDoubletFinder {singleCellTK} | R Documentation

## Generates a doublet score for each cell via doubletFinder

### 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)

[Package singleCellTK version 2.0.0 Index]