run_consensus_clust: Wrapper function to repeatively run clustering on subsampled...

In AllenInstitute/scrattch.hicat: Hierarchical Iterative Clustering Analysis for Transcriptomic data

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

Description

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

Usage

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

AllenInstitute/scrattch.hicat documentation built on Oct. 20, 2023, 6:55 a.m.