HC clustering for a number of resolutions

performs 40 clustering runs or more depending on windows

clustering(
  object = NULL,
  ngenes = 1500,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  verbose = FALSE,
  log_transform = FALSE
)

Arguments

object	is a SingleCellExperiment object from the train mixed population
ngenes	number of top variable genes to be used
windows	a numeric specifying the number of windows to test
remove_outlier	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons
nRounds	number of iterations to remove a selected clusters
PCA	logical specifying if PCA is used before calculating distance matrix
nPCs	number of principal components from PCA dimensional reduction to be used
verbose	a logical whether to display additional messages
log_transform	boolean whether log transform should be computed

Value

clustering results

Author

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering(mixedpop2, remove_outlier = c(0))
#> Performing 1 round of filtering
#> Identifying top variable genes
#> Calculating distance matrix
#> Performing hierarchical clustering
#> Finding clustering information
#> No more outliers detected in filtering round 1
#> Identifying top variable genes
#> Calculating distance matrix
#> Performing hierarchical clustering
#> Finding clustering information
#> Done clustering, moving to stability calculation...