FindNeighbors.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/clustering.R
\name{FindNeighbors}
\alias{FindNeighbors}
\alias{FindNeighbors.default}
\alias{FindNeighbors.Assay}
\alias{FindNeighbors.dist}
\alias{FindNeighbors.Seurat}
\title{(Shared) Nearest-neighbor graph construction}
\usage{
FindNeighbors(object, ...)

\method{FindNeighbors}{default}(
  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,
  ...
)

\method{FindNeighbors}{Assay}(
  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,
  ...
)

\method{FindNeighbors}{dist}(
  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,
  ...
)

\method{FindNeighbors}{Seurat}(
  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{
\item{object}{An object}

\item{...}{Arguments passed to other methods}

\item{query}{Matrix of data to query against object. If missing, defaults to
object.}

\item{distance.matrix}{Boolean value of whether the provided matrix is a
distance matrix; note, for objects of class \code{dist}, this parameter will
be set automatically}

\item{k.param}{Defines k for the k-nearest neighbor algorithm}

\item{return.neighbor}{Return result as \code{\link{Neighbor}} object. Not
used with distance matrix input.}

\item{compute.SNN}{also compute the shared nearest neighbor graph}

\item{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).}

\item{nn.method}{Method for nearest neighbor finding. Options include: rann,
annoy}

\item{n.trees}{More trees gives higher precision when using annoy approximate
nearest neighbor search}

\item{annoy.metric}{Distance metric for annoy. Options include: euclidean,
cosine, manhattan, and hamming}

\item{nn.eps}{Error bound when performing nearest neighbor seach using RANN;
default of 0.0 implies exact nearest neighbor search}

\item{verbose}{Whether or not to print output to the console}

\item{force.recalc}{Force recalculation of (S)NN.}

\item{l2.norm}{Take L2Norm of the data}

\item{cache.index}{Include cached index in returned Neighbor object
(only relevant if return.neighbor = TRUE)}

\item{index}{Precomputed index. Useful if querying new data against existing
index to avoid recomputing.}

\item{features}{Features to use as input for building the (S)NN; used only when
\code{dims} is \code{NULL}}

\item{reduction}{Reduction to use as input for building the (S)NN}

\item{dims}{Dimensions of reduction to use as input}

\item{assay}{Assay to use in construction of (S)NN; used only when \code{dims}
is \code{NULL}}

\item{do.plot}{Plot SNN graph on tSNE coordinates}

\item{graph.name}{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 \code{\link{Neighbor}} object
with the KNN information or a list of \code{\link{Graph}} objects with
the KNN and SNN depending on the settings of \code{return.neighbor} and
\code{compute.SNN}. When running on a \code{\link{Seurat}} object, this
returns the \code{\link{Seurat}} object with the Graphs or Neighbor objects
stored in their respective slots. Names of the Graph or Neighbor object can
be found with \code{\link{Graphs}} or \code{\link{Neighbors}}.
}
\description{
Computes the \code{k.param} nearest neighbors for a given dataset. Can also
optionally (via \code{compute.SNN}), construct a shared nearest neighbor
graph by calculating the neighborhood overlap (Jaccard index) between every
cell and its \code{k.param} nearest neighbors.
}
\examples{
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)

}
\concept{clustering}