rliger: louvainCluster – R documentation

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louvainCluster

Louvain algorithm for community detection

Description

After quantile normalization, users can additionally run the Louvain algorithm for community detection, which is widely used in single-cell analysis and excels at merging small clusters into broad cell classes.

Usage

louvainCluster(
  object,
  resolution = 1,
  k = 20,
  prune = 1/15,
  eps = 0.1,
  nRandomStarts = 10,
  nIterations = 100,
  random.seed = 1,
  verbose = TRUE
)

Arguments

`object`	`liger` object. Should run quantile_norm before calling.
`resolution`	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. (default 1.0)
`k`	The maximum number of nearest neighbours to compute. (default 20)
`prune`	Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 — no pruning, 1 — prune everything). (default 1/15)
`eps`	The error bound of the nearest neighbor search. (default 0.1)
`nRandomStarts`	Number of random starts. (default 10)
`nIterations`	Maximal number of iterations per random start. (default 100)
`random.seed`	Seed of the random number generator. (default 1)
`verbose`	Print messages (TRUE by default)

Value

liger object with refined 'clusters' slot set.

Examples

## Not run: 
# ligerex (liger object), factorization complete
ligerex <- louvainCluster(ligerex, resulotion = 0.3)

## End(Not run)

rliger

Linked Inference of Genomic Experimental Relationships

v1.0.0

GPL-3

Authors

Joshua Welch [aut, ctb], Chao Gao [aut, ctb, cre], Jialin Liu [aut, ctb], Joshua Sodicoff [aut, ctb], Velina Kozareva [aut, ctb], Evan Macosko [aut, ctb], Paul Hoffman [ctb], Ilya Korsunsky [ctb], Robert Lee [ctb]

Initial release

2021-04-18