identifyClusters: Identify cell clusters

In sqjin/scAI: integrative analysis of single-cell transcriptomic and epigenomic profiles

Description

Identify cell clusters

Usage

identifyClusters(object, resolution = 1,
  partition.type = "RBConfigurationVertexPartition", seed.use = 42L,
  n.iter = 10L, initial.membership = NULL, weights = NULL,
  node.sizes = NULL, K = NULL)

Arguments

object	scAI object
resolution	A parameter controlling the coarseness of the clusters
partition.type	Type of partition to use. Defaults to RBConfigurationVertexPartition. Options include: ModularityVertexPartition, RBERVertexPartition, CPMVertexPartition, MutableVertexPartition, SignificanceVertexPartition, SurpriseVertexPartition (see the Leiden python module documentation for more details)
seed.use	set seed
n.iter	number of iteration
initial.membership	arameters to pass to the Python leidenalg function defaults initial_membership=None
weights	defaults weights=None
node.sizes	Parameters to pass to the Python leidenalg function
K	Number of clusters if performing hierarchical clustering of the consensus matrix

sqjin/scAI documentation built on Nov. 19, 2020, 4:04 p.m.