FindClusters: Cluster Determination

In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

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,
  resolution = 0.8, algorithm = 1, n.start = 10, n.iter = 10,
  random.seed = 0, temp.file.location = NULL, edge.file.name = NULL,
  verbose = TRUE, ...)

## S3 method for class 'Seurat'
FindClusters(object, graph.name = NULL,
  modularity.fxn = 1, resolution = 0.8, algorithm = 1,
  n.start = 10, n.iter = 10, random.seed = 0,
  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).
resolution	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
algorithm	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm).
n.start	Number of random starts.
n.iter	Maximal number of iterations per random start.
random.seed	Seed of the random number generator.
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

Value

Returns a Seurat object and optionally the SNN matrix, object idents have been updated with new cluster info

atakanekiz/Seurat3.0 documentation built on May 26, 2019, 2:33 a.m.