runLeiden: Run Leiden clustering algorithm This code is modified from...

In KChen-lab/bindSC: Bi-order INtegration of Data from Single Cell multi-omics technologies \

Run Leiden clustering algorithm This code is modified from Tom Kelly (https://github.com/TomKellyGenetics/leiden), where we added more parameters (seed.use and n.iter) to run the Python version. In addition, we also take care of the singleton issue after running leiden algorithm.

Description

Implements the Leiden clustering algorithm in R using reticulate to run the Python version. Requires the python "leidenalg" and "igraph" modules to be installed. Returns a vector of partition indices.

Usage

runLeiden(
  SNN = matrix(),
  resolution = 1,
  partition_type = c("RBConfigurationVertexPartition", "ModularityVertexPartition",
    "RBERVertexPartition", "CPMVertexPartition", "MutableVertexPartition",
    "SignificanceVertexPartition", "SurpriseVertexPartition"),
  seed.use = 42L,
  n.iter = 10L,
  initial.membership = NULL,
  weights = NULL,
  node.sizes = NULL
)

Arguments

SNN	An adjacency matrix compatible with igraph 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

Value

A parition of clusters as a vector of integers

KChen-lab/bindSC documentation built on Sept. 29, 2022, 4:24 a.m.