directDblClassification {scDblFinder} | R Documentation

## directClassification

### Description

Trains a classifier directly on the expression matrix to distinguish artificial doublets from real cells.

### Usage

    directDblClassification(
      sce,
      dbr = NULL,
      processing = "default",
      iter = 2,
      dims = 20,
      nrounds = 0.25,
      max_depth = 6,
      ...
    )

### Arguments

`sce` | A `SummarizedExperiment-class`, `SingleCellExperiment-class`, or array of counts.
`dbr` | The expected doublet rate. By default this is assumed to be 1% per thousand cells captured (so 4% among 4000 thousand cells), which is appropriate for 10x datasets. Corrections for homeotypic doublets will be performed on the given rate.
`processing` | Counts (real and artificial) processing. Either 'default' (normal `scater`-based normalization and PCA), "rawPCA" (PCA without normalization), "rawFeatures" (no normalization/dimensional reduction), "normFeatures" (uses normalized features, without PCA) or a custom function with (at least) arguments 'e' (the matrix of counts) and 'dims' (the desired number of dimensions), returning a named matrix with cells as rows and components as columns.
`iter` | A positive integer indicating the number of scoring iterations. At each iteration, real cells that would be called as doublets are excluding from the training, and new scores are calculated.
`dims` | The number of dimensions used.
`nrounds` | Maximum rounds of boosting. If NULL, will be determined through cross-validation.
`max_depth` | Maximum depths of each tree.
`...` | Any doublet generation or pre-processing argument passed to 'scDblFinder'.

### Value

A `SummarizedExperiment-class` with the additional 'colData' column 'directDoubletScore'.

### Examples

    sce <- directDblClassification(mockDoubletSCE(), artificialDoublets=1)
    boxplot(sce$directDoubletScore~sce$type)

[Package scDblFinder version 1.8.0 Index]