R/scds_doubletdetection.R
runCxdsBcdsHybrid.Rd
A wrapper function for cxds_bcds_hybrid. Annotate
doublets/multiplets using a binary classification approach to discriminate
artificial doublets from original data. Generate a doublet
score for each cell. Infer doublets if estNdbl
is TRUE
.
runCxdsBcdsHybrid( inSCE, sample = NULL, seed = 12345, nTop = 500, cxdsArgs = list(), bcdsArgs = list(), verb = FALSE, estNdbl = FALSE, force = FALSE, useAssay = "counts" )
inSCE | A SingleCellExperiment object.
Needs |
---|---|
sample | Character vector. Indicates which sample each cell belongs to. cxds_bcds_hybrid will be run on cells from each sample separately. If NULL, then all cells will be processed together. Default NULL. |
seed | Seed for the random number generator. Default 12345. |
nTop | The number of top varialbe genes to consider. Used in both |
cxdsArgs | See cxds_bcds_hybrid for more information. Default |
bcdsArgs | See cxds_bcds_hybrid for more information. Default |
verb | See cxds_bcds_hybrid for more information. Default |
estNdbl | See cxds_bcds_hybrid for more information. Default |
force | See cxds_bcds_hybrid for more information. Default |
useAssay | A string specifying which assay in the SCE to use. |
A SingleCellExperiment object with cxds_bcds_hybrid output appended to the colData slot. The columns include hybrid_score and optionally hybrid_call. Please refer to the documentation of cxds_bcds_hybrid for details.
data(scExample, package = "singleCellTK") sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'") sce <- runCxdsBcdsHybrid(sce)#>#> [15:46:36] WARNING: amalgamation/../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.