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,
...
)

Arguments

object An object Arguments passed to other methods Matrix of data to query against object. If missing, defaults to object. Boolean value of whether the provided matrix is a distance matrix; note, for objects of class dist, this parameter will be set automatically Defines k for the k-nearest neighbor algorithm Return result as Neighbor object. Not used with distance matrix input. also compute the shared nearest neighbor graph 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). Method for nearest neighbor finding. Options include: rann, annoy More trees gives higher precision when using annoy approximate nearest neighbor search Distance metric for annoy. Options include: euclidean, cosine, manhattan, and hamming Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search Whether or not to print output to the console Force recalculation of (S)NN. Take L2Norm of the data Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE) Precomputed index. Useful if querying new data against existing index to avoid recomputing. Features to use as input for building the (S)NN; used only when dims is NULL Reduction to use as input for building the (S)NN Dimensions of reduction to use as input Assay to use in construction of (S)NN; used only when dims is NULL Plot SNN graph on tSNE coordinates Optional naming parameter for stored (S)NN graph (or Neighbor object, if return.neighbor = TRUE). Default is assay.name_(s)nn.

Value

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.

Examples

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)
#>  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