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,
  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

Details

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