Louvain algorithm for community detection

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.

Arguments

object

liger object. Should run quantile_norm before calling.

k

The maximum number of nearest neighbours to compute. (default 20)

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)

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)

dims.use

Indices of factors to use for clustering. Default NULL uses all available factors.

Value

object with refined cluster assignment updated in "louvain_cluster" variable in cellMeta slot. Can be fetched with object$louvain_cluster

See also

rliger-deprecated