FindClusters: Cluster Determination
In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description Usage Arguments Value

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.

FindClusters(object, genes.use = NULL, reduction.type = "pca",
  dims.use = NULL, k.param = 30, k.scale = 25, plot.SNN = FALSE,
  prune.SNN = 1/15, print.output = TRUE, distance.matrix = NULL,
  save.SNN = FALSE, reuse.SNN = FALSE, force.recalc = FALSE,
  modularity.fxn = 1, resolution = 0.8, algorithm = 1, n.start = 100,
  n.iter = 10, random.seed = 0, temp.file.location = NULL)

`object`	Seurat object
`genes.use`	A vector of gene names to use in construction of SNN graph if building directly based on expression data rather than a dimensionally reduced representation (i.e. PCs).
`reduction.type`	Name of dimensional reduction technique to use in construction of SNN graph. (e.g. "pca", "ica")
`dims.use`	A vector of the dimensions to use in construction of the SNN graph (e.g. To use the first 10 PCs, pass 1:10)
`k.param`	Defines k for the k-nearest neighbor algorithm
`k.scale`	Granularity option for k.param
`plot.SNN`	Plot the SNN graph
`prune.SNN`	Sets the cutoff for acceptable Jaccard distances when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 — no pruning, 1 — prune everything).
`print.output`	Whether or not to print output to the console
`distance.matrix`	Build SNN from distance matrix (experimental)
`save.SNN`	Saves the SNN matrix associated with the calculation in object@snn
`reuse.SNN`	Force utilization of stored SNN. If none store, this will throw an error.
`force.recalc`	Force recalculation of SNN.
`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.