FindClusters: Cluster Determination

In satijalab/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 rewrite 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 = "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 class '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,
  ...
)

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).

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

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.