bcds: Find doublets/multiplets in UMI scRNA-seq data;

In kostkalab/scds: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data

Find doublets/multiplets in UMI scRNA-seq data;

Description

Annotates doublets/multiplets using a binary classification approach to discriminate artificial doublets from original data.

Usage

bcds(sce, ntop = 500, srat = 1, verb = FALSE, retRes = FALSE,
  nmax = "tune", varImp = FALSE, estNdbl = FALSE)

Arguments

sce	single cell experiment (SingleCellExperiment) object to analyze; needs counts in assays slot.
ntop	integer, indicating number of top variance genes to consider. Default: 500
srat	numeric, indicating ratio between orginal number of "cells" and simulated doublets; Default: 1
verb	progress messages. Default: FALSE
retRes	logical, should the trained classifier be returned? Default: FALSE
nmax	maximum number of training rounds; integer or "tune". Default: "tune"
varImp	logical, should variable (i.e., gene) importance be returned? Default: FALSE
estNdbl	logical, should the numer of doublets be estimated from the data. Enables doublet calls. Default:FALSE. Use with caution.

Value

sce input sce object SingleCellExperiment with doublet scores added to colData as "bcds_score" column, and possibly more (details)

Examples

data("sce_chcl")
## create small data set using only 100 cells
sce_chcl_small = sce_chcl[, 1:100]
sce_chcl_small = bcds(sce_chcl_small)

kostkalab/scds documentation built on Oct. 4, 2022, 6:56 p.m.