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 
k.param  Defines k for the knearest neighbor algorithm 
return.neighbor  Return result as 
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

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 
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 
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
.
#> An object of class Seurat #> 230 features across 80 samples within 1 assay #> Active assay: RNA (230 features, 20 variable features) #> 2 dimensional reductions calculated: pca, tsne# 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)#>#>