directDblClassification: directClassification

In plger/scDblFinder: scDblFinder

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)

plger/scDblFinder documentation built on March 20, 2024, 9:46 p.m.