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,
force.recalc = FALSE,
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,
force.recalc = FALSE,
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,
force.recalc = FALSE,
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,
force.recalc = FALSE,
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

force.recalc

Force recalculation of (S)NN.

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`

.

```
# NOT RUN {
data("pbmc_small")
pbmc_small
# Compute an SNN on the gene expression level
pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small))
# 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)
# }
```

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