FindClusters: Cluster Determination

In Seurat: Tools for Single Cell Genomics

Cluster Determination

Description

Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors and construct the SNN graph. Then optimize the modularity function to determine clusters. For a full description of the algorithms, see Waltman and van Eck (2013) The European Physical Journal B. Thanks to Nigel Delaney (evolvedmicrobe@github) for the initial rewrite (v2.3.4) of the Java modularity optimizer code in Rcpp!

Usage

FindClusters(object, ...)

## Default S3 method:
FindClusters(
  object,
  modularity.fxn = 1,
  initial.membership = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = deprecated(),
  algorithm = 1,
  leiden_method = c("leidenbase", "igraph"),
  leiden_objective_function = c("modularity", "CPM"),
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
FindClusters(
  object,
  graph.name = NULL,
  cluster.name = NULL,
  modularity.fxn = 1,
  initial.membership = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = NULL,
  algorithm = 1,
  leiden_method = c("leidenbase", "igraph"),
  leiden_objective_function = c("modularity", "CPM"),
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  verbose = TRUE,
  ...
)

Arguments

object	An object
...	Arguments passed to other methods
modularity.fxn	Modularity function (1 = standard; 2 = alternative).
initial.membership	Passed to the 'initial_membership' parameter of 'leidenbase::leiden_find_partition'.
node.sizes	Passed to the 'node_sizes' parameter of 'leidenbase::leiden_find_partition'.
resolution	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
method	DEPRECATED.
algorithm	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm).
leiden_method	Choose from the leidenbase ("leidenbase") or igraph ("igraph") packages for running leiden. Default is "leidenbase"
leiden_objective_function	objective function to use if 'leiden_method = "igraph"'. See cluster_leiden for more information. Default is "modularity".
n.start	Number of random starts.
n.iter	Maximal number of iterations per random start.
random.seed	Seed of the random number generator.
group.singletons	Group singletons into nearest cluster. If FALSE, assign all singletons to a "singleton" group
temp.file.location	Directory where intermediate files will be written. Specify the ABSOLUTE path.
edge.file.name	Edge file to use as input for modularity optimizer jar.
verbose	Print output
graph.name	Name of graph to use for the clustering algorithm
cluster.name	Name of output clusters

Details

Leiden clustering can be run with either the leidenbase or igraph backend; see Traag et al (2018).

Value

Returns a Seurat object where the idents have been updated with new cluster info; latest clustering results will be stored in object metadata under 'seurat_clusters'. Note that 'seurat_clusters' will be overwritten everytime FindClusters is run

Note

When running Louvain clustering with multiple threads, results may differ from single-threaded runs only when n.start > 1, because each random start uses an independent random number stream based on random.seed plus the restart index. Single-threaded runs use one random number stream across all starts. With n.start = 1, all thread settings use the same serial path. For a fixed random.seed, multi-threaded Louvain results are expected to be reproducible across thread counts.

Seurat documentation built on Oct. 3, 2026, 5:06 p.m.