Wrapper function to repeatively run clustering on subsampled cells and infer consensus clusters

run_consensus_clust(
  norm.dat,
  select.cells = colnames(norm.dat),
  niter = 100,
  sample.frac = 0.8,
  co.result = NULL,
  output_dir = "subsample_result",
  mc.cores = 1,
  de.param = de_param(),
  merge.type = c("undirectional", "directional"),
  override = FALSE,
  init.result = NULL,
  cut.method = "auto",
  confusion.th = 0.6,
  ...
)

Arguments

norm.dat: normalized expression data matrix in log transform, using genes as rows, and cells and columns. Users can use log2(FPKM+1) or log2(CPM+1).
select.cells: The cells to be clustered. Default: columns of norm.dat
niter: The number of iteractions to run. Default 100.
sample.frac: The fraction of of cells sampled per run. Default: 0.8.
output_dir: The output directory to store clutering results for each iteraction.
mc.cores: The number of cores to be used for parallel processing.
de.param: The differential gene expression threshold. See de_param() function for details.
merge.type: Determine if the DE gene score threshold should be applied to combined de.score, or de.score for up and down directions separately.
override: binary variable determine if the clustering results already stored in output_dir should be overriden.
init.result: The pre-set high level clusters. If set, the function will only find finer splits of the current clusters.
...: Other parameters passed to iter_clust