cluster_leiden: Run Leiden clustering algorithm

In hesselberthlab/scrunchy: Single Cell Reconstruction of Functional Heterogeneity

Description

Runs Leiden community detection algorithm to detect clusters.

Usage

cluster_leiden(fsce, expt = "rnaseq", method = "PCA", dims = 1:5,
  prune = 1/15, seed = NULL,
  partition_type = "ModularityVertexPartition", ...)

Arguments

fsce	An object of class FunctionalSingleCellExperiment
expt	Data to use for calculating variable features (default is rnaseq). Must be present in names(fsce).
method	dimensionality reduction method for clustering (defaults to PCA)
dims	dimensions to use for nearest-neighbor calculation
prune	Pruning parameter for shared nearest-neighbor calculation.
seed	seed for leidenalg$find_partition()
partition_type	partitioning algorithm (see leiden::leiden). (defaults to "ModularityVertexPartition")
...	Parameters to pass to the Python leidenalg function.

Details

Execute install_py_deps() to install required python modules leidenalg and igraph.

Value

fsce with leiden_cluster in expt colData.

Source

https://github.com/vtraag/leidenalg

https://github.com/TomKellyGenetics/leiden

See Also

Other clustering functions: cluster_kmeans

Examples

fsce_small <- cluster_leiden(fsce_small)
SingleCellExperiment::colData(fsce_small[["rnaseq"]])

hesselberthlab/scrunchy documentation built on Nov. 11, 2019, 2:29 p.m.