FindClusters: 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 rewrite of the Java modularity optimizer code in Rcpp!

Usage

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

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

Arguments

object: An object
...: Arguments passed to other methods
modularity.fxn: Modularity function (1 = standard; 2 = alternative).
initial.membership, node.sizes: Parameters to pass to the Python leidenalg function.
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: Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix.
algorithm: Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.
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

To run Leiden algorithm, you must first install the leidenalg python package (e.g. via pip install leidenalg), see Traag et al (2018).