This functions allows you to preprocess, cluster and reduce dimensions for one SingleCellExperiment object.
Usage
sce_process(
object,
experiment = "gene",
resolution = 0.6,
reduction = "PCA",
organism = "human",
process = TRUE,
...
)Arguments
- object
A SingleCellExperiment object
- experiment
Assay of interest in SingleCellExperiment object
- resolution
Resolution for clustering cells. Default set to 0.6.
- reduction
Dimensional reduction object
- organism
Organism
- process
whether to run dimensional reduction and clustering
- ...
extra parameters passed to internal functions
Examples
data(tiny_sce)
sce_process(tiny_sce, process = FALSE)
#> class: SingleCellExperiment
#> dim: 10 883
#> metadata(2): markers experiment
#> assays(3): counts logcounts scaledata
#> rownames(10): PDE6H GUCA1A ... NRL FOS
#> rowData names(0):
#> colnames(883): ds20181001-0001 ds20181001-0002 ... ds20181001-1039
#> ds20181001-1040
#> colData names(49): orig.ident nCount_gene ... nFeature_transcript ident
#> reducedDimNames(2): PCA UMAP
#> mainExpName: gene
#> altExpNames(1): transcript