rliger (version 1.0.0)

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)
# }
# NOT RUN {
# }