sc_clustering.soup: Perform Single Cell data clustering using SOUP

In pengminshi/MRtree: Multi-resolution Reconciled Tree

Perform Single Cell data clustering using SOUP

Description

Perform Single Cell data clustering using SOUP

Usage

sc_clustering.soup(
  exprs,
  Ks,
  type = c("count", "log"),
  estimate.k = FALSE,
  pure.prop = 0.5,
  ext.prop = NULL,
  scale.factor = 10000,
  column.prefix = "soup_"
)

Arguments

exprs	n.genes-by-n.cells expression matrix
Ks	vector of resolution, number of clusters
type	string, type of the expression matrix, choices are 'count' and 'log', and default by 'counts'
estimate.k	boolean, whether to estimate optimal number of clusters by soup
pure.prop	the proportion of pure cells, SOUP parameter
ext.prop	the proportion of extreme neighbors for each cell, SOUP parameter
scale.factor	scalar sets the scale factor for cell-level normalization
column.prefix	string, output column prefix, default 'soup_'

Value

a list containing

labelmat

a data frame, columns are clusterings for each resolution specified

est.k

integer, estimated number of clusters

pengminshi/MRtree documentation built on March 6, 2023, 4:20 p.m.