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, pc.use = NULL, k.param = 30,
k.scale = 25, plot.SNN = FALSE, prune.SNN = 1/15, save.SNN = FALSE,
do.sparse = FALSE, modularity.fxn = 1, resolution = 0.8,
algorithm = 1, n.start = 100, n.iter = 10, random.seed = 0,
print.output = TRUE)Seurat object
Gene expression data
Which PCs to use for construction of the SNN graph
Defines k for the k-nearest neighbor algorithm
granularity option for k.param
Plot the SNN graph
Stringency of pruning for the SNN graph (0 - no pruning, 1 - prune everything)
Whether to save the SNN in an object slot
Option to store and use SNN matrix as a sparse matrix. May be necessary datasets containing a large number of cells.
Modularity function (1 = standard; 2 = alternative).
Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm).
Number of random starts.
Maximal number of iterations per random start.
Seed of the random number generator.
Whether or not to print output to the console
Returns a Seurat object and optionally the SNN matrix, object@ident has been updated with new cluster info