Compute a shared nearest-neighbour graph for a MatisseObject

Runs FindNeighbors on the embedded Seurat object and returns the updated MatisseObject. Typically called after RunPCA.MatisseObject and before FindClusters.MatisseObject.

Usage

# S3 method for class 'MatisseObject'
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,
  ...
)

Arguments

object: A MatisseObject.
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
features: Features to use as input for building the (S)NN; used only when dims is NULL
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 search using RANN; default of 0.0 implies exact nearest neighbor search
verbose: Whether or not to print output to the console
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.
l2.norm: Take L2Norm of the data
cache.index: Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE)
...: Additional arguments forwarded to FindNeighbors (e.g. dims, reduction).

Value

The updated MatisseObject with neighbour graph stored inside the embedded Seurat object.

Usage

Arguments

Value

See also