FindClusters

0th

Percentile

Cluster Determination

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, weights = 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, weights = 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, weights, 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).

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

Aliases
  • FindClusters
  • FindClusters.default
  • FindClusters.Seurat
Documentation reproduced from package Seurat, version 3.1.4, License: GPL-3 | file LICENSE

Community examples

Looks like there are no examples yet.