Run Louvain Clustering at Multiple Resolutions

Usage

sce_cluster(
  object = object,
  resolution = 0.6,
  custom_clust = NULL,
  reduction = "PCA",
  algorithm = 1,
  ...
)

Arguments

object: A SingleCellExperiment objects
resolution: Clustering resolution
custom_clust: custom cluster
reduction: Set dimensional reduction object
algorithm: 1
...: extra args passed to single cell packages

Value

a SingleCellExperiment object with louvain clusters

Examples

data(small_example_dataset)
sce_cluster(small_example_dataset)
#> [17:22:01] Clustering Cells...
#> clustering at 0.6 resolution
#> class: SingleCellExperiment 
#> dim: 1000 200 
#> metadata(1): markers
#> assays(2): counts logcounts
#> rownames(1000): Gene_0001 Gene_0002 ... Gene_0999 Gene_1000
#> rowData names(0):
#> colnames(200): Cell_001 Cell_002 ... Cell_199 Cell_200
#> colData names(9): Mutation_Status Cell_Cycle ... gene_snn_res.0.8
#>   gene_snn_res.1
#> reducedDimNames(3): PCA TSNE UMAP
#> mainExpName: gene
#> altExpNames(1): Spikes