# FindClusters

##### 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*.

##### Usage

```
FindClusters(object, genes.use = NULL, pc.use = NULL, k.param = 30,
k.scale = 25, plot.SNN = FALSE, prune.SNN = 1/15, save.SNN = FALSE,
reuse.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)
```

##### Arguments

- object
Seurat object

- genes.use
Gene expression data

- pc.use
Which PCs to use for construction of the SNN graph

- 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
Stringency of pruning for the SNN graph (0 - no pruning, 1 - prune everything)

- save.SNN
Whether to save the SNN in an object slot

- reuse.SNN
Force utilization of stored SNN. If none store, this will throw an error.

- do.sparse
Option to store and use SNN matrix as a sparse matrix. May be necessary datasets containing a large number of cells.

- 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.

- print.output
Whether or not to print output to the console

##### Value

Returns a Seurat object and optionally the SNN matrix, object@ident has been updated with new cluster info

*Documentation reproduced from package Seurat, version 1.4.0, License: GPL-3*