runDoubletCells {singleCellTK} | R Documentation

## Detect doublet cells using scDblFinder.

### Description

A wrapper function for scDblFinder. Identify potential doublet cells based on simulations of putative doublet expression profiles. Generate a doublet score for each cell.

### Usage

    runDoubletCells(
      inSCE,
      sample = NULL,
      useAssay = "counts",
      nNeighbors = 50,
      simDoublets = max(10000, ncol(inSCE)),
      seed = 12345,
      BPPARAM = BiocParallel::SerialParam()
    )

### Arguments

`inSCE` | A SingleCellExperiment object.
`sample` | Character vector. Indicates which sample each cell belongs to. scDblFinder will be run on cells from each sample separately.
`useAssay` | A string specifying which assay in the SCE to use.
`nNeighbors` | Number of nearest neighbors used to calculate density for doublet detection. Default 50.
`simDoublets` | Number of simulated doublets created for doublet detection. Default 10000.
`seed` | Seed for the random number generator. Default 12345.
`BPPARAM` | A `BiocParallelParam` object specifying whether the neighbour searches should be parallelized.

### Details

This function is a wrapper function for scDblFinder. `runDoubletCells` runs scDblFinder for each `sample` within `inSCE` iteratively. The resulting doublet scores for all cells will be appended to the colData of `inSCE`.

### Value

A SingleCellExperiment object with the 'scran_doubletCells_score' column added to the colData slot.

### References

Lun ATL (2018). Detecting doublet cells with scran. https://ltla.github.io/SingleCellThoughts/software/doublet_detection/bycell.html

### See Also

doubletCells

### Examples

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

[Package singleCellTK version 2.0.0 Index]