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Cluster Determination

Source: R/generics.R, R/clustering.R
FindClusters.Rd

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

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

Details

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

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.