iter_consensus_clust: Iterative consensus clustering

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

Iterative consensus clustering

Description

Iterative consensus clustering

Usage

iter_consensus_clust(
  cl.list,
  co.ratio = NULL,
  cl.mat = NULL,
  norm.dat,
  select.cells = names(cl.list[[1]]),
  diff.th = 0.25,
  prefix = NULL,
  method = c("auto", "louvain", "ward.D"),
  verbose = FALSE,
  de.param = de.param,
  max.cl.size = 300,
  result = NULL,
  split.size = de.param$min.cells * 2,
  merge.type = c("undirectional", "directional")
)

Arguments

cl.list	The list of subsampled clustering results.
co.ratio	cell cell co-clustering matrix
norm.dat	The log2 transformed normalzied expression matrix
select.cells	Cells to be clustered
diff.th	The difference of co-clustering probablities for splitting a cluster.
prefix	Default NULL.
method	Clustering methods. Default "auto"
verbose	Default FALSE
de.param	Differentiall expressed genes criteria for merging clusters
result	Pre-computed clustering results used for further splitting. Default NULL.
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.

Value

A list with cluster membership, and top pairwise marker genes.

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