Computes the `k.param`

nearest neighbors for a given dataset. Can also
optionally (via `compute.SNN`

), construct a shared nearest neighbor
graph by calculating the neighborhood overlap (Jaccard index) between every
cell and its `k.param`

nearest neighbors.

```
FindNeighbors(object, ...)
# S3 method for default
FindNeighbors(
object,
query = NULL,
distance.matrix = FALSE,
k.param = 20,
return.neighbor = FALSE,
compute.SNN = !return.neighbor,
prune.SNN = 1/15,
nn.method = "annoy",
n.trees = 50,
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
l2.norm = FALSE,
cache.index = FALSE,
index = NULL,
...
)
# S3 method for Assay
FindNeighbors(
object,
features = NULL,
k.param = 20,
return.neighbor = FALSE,
compute.SNN = !return.neighbor,
prune.SNN = 1/15,
nn.method = "annoy",
n.trees = 50,
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
l2.norm = FALSE,
cache.index = FALSE,
...
)
# S3 method for dist
FindNeighbors(
object,
k.param = 20,
return.neighbor = FALSE,
compute.SNN = !return.neighbor,
prune.SNN = 1/15,
nn.method = "annoy",
n.trees = 50,
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
l2.norm = FALSE,
cache.index = FALSE,
...
)
# S3 method for Seurat
FindNeighbors(
object,
reduction = "pca",
dims = 1:10,
assay = NULL,
features = NULL,
k.param = 20,
return.neighbor = FALSE,
compute.SNN = !return.neighbor,
prune.SNN = 1/15,
nn.method = "annoy",
n.trees = 50,
annoy.metric = "euclidean",
nn.eps = 0,
verbose = TRUE,
do.plot = FALSE,
graph.name = NULL,
l2.norm = FALSE,
cache.index = FALSE,
...
)
```

- object
An object

- ...
Arguments passed to other methods

- query
Matrix of data to query against object. If missing, defaults to object.

- distance.matrix
Boolean value of whether the provided matrix is a distance matrix; note, for objects of class

`dist`

, this parameter will be set automatically- k.param
Defines k for the k-nearest neighbor algorithm

- return.neighbor
Return result as

`Neighbor`

object. Not used with distance matrix input.- compute.SNN
also compute the shared nearest neighbor graph

- prune.SNN
Sets the cutoff for acceptable Jaccard index 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 stringency of pruning (0 --- no pruning, 1 --- prune everything).

- nn.method
Method for nearest neighbor finding. Options include: rann, annoy

- n.trees
More trees gives higher precision when using annoy approximate nearest neighbor search

- annoy.metric
Distance metric for annoy. Options include: euclidean, cosine, manhattan, and hamming

- nn.eps
Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search

- verbose
Whether or not to print output to the console

- l2.norm
Take L2Norm of the data

- cache.index
Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE)

- index
Precomputed index. Useful if querying new data against existing index to avoid recomputing.

- features
Features to use as input for building the (S)NN; used only when

`dims`

is`NULL`

- reduction
Reduction to use as input for building the (S)NN

- dims
Dimensions of reduction to use as input

- assay
Assay to use in construction of (S)NN; used only when

`dims`

is`NULL`

- do.plot
Plot SNN graph on tSNE coordinates

- graph.name
Optional naming parameter for stored (S)NN graph (or Neighbor object, if return.neighbor = TRUE). Default is assay.name_(s)nn. To store both the neighbor graph and the shared nearest neighbor (SNN) graph, you must supply a vector containing two names to the

`graph.name`

parameter. The first element in the vector will be used to store the nearest neighbor (NN) graph, and the second element used to store the SNN graph. If only one name is supplied, only the NN graph is stored.

This function can either return a `Neighbor`

object
with the KNN information or a list of `Graph`

objects with
the KNN and SNN depending on the settings of `return.neighbor`

and
`compute.SNN`

. When running on a `Seurat`

object, this
returns the `Seurat`

object with the Graphs or Neighbor objects
stored in their respective slots. Names of the Graph or Neighbor object can
be found with `Graphs`

or `Neighbors`

.

```
data("pbmc_small")
pbmc_small
#> An object of class Seurat
#> 230 features across 80 samples within 1 assay
#> Active assay: RNA (230 features, 20 variable features)
#> 3 layers present: counts, data, scale.data
#> 2 dimensional reductions calculated: pca, tsne
# Compute an SNN on the gene expression level
pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small))
#> Computing nearest neighbor graph
#> Computing SNN
# More commonly, we build the SNN on a dimensionally reduced form of the data
# such as the first 10 principle components.
pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)
#> Computing nearest neighbor graph
#> Computing SNN
```