FindNeighbors: SNN Graph Construction

In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

Description

Constructs a Shared Nearest Neighbor (SNN) Graph for a given dataset. We first determine the k-nearest neighbors of each cell. We use this knn graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its k.param nearest neighbors.

Usage

FindNeighbors(object, ...)

## Default S3 method:
FindNeighbors(object, distance.matrix = FALSE,
  k.param = 10, compute.SNN = TRUE, prune.SNN = 1/15, nn.eps = 0,
  verbose = TRUE, force.recalc = FALSE, ...)

## S3 method for class 'Assay'
FindNeighbors(object, features = NULL, k.param = 10,
  compute.SNN = TRUE, prune.SNN = 1/15, nn.eps = 0, verbose = TRUE,
  force.recalc = FALSE, ...)

## S3 method for class 'dist'
FindNeighbors(object, k.param = 10, compute.SNN = TRUE,
  prune.SNN = 1/15, nn.eps = 0, verbose = TRUE,
  force.recalc = FALSE, ...)

## S3 method for class 'Seurat'
FindNeighbors(object, reduction = "pca", dims = 1:10,
  assay = NULL, features = NULL, k.param = 30, compute.SNN = TRUE,
  prune.SNN = 1/15, nn.eps = 0, verbose = TRUE,
  force.recalc = FALSE, do.plot = FALSE, graph.name = NULL, ...)

Arguments

object	An object
...	Arguments passed to other methods
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
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 strigency of pruning (0 — no pruning, 1 — prune everything).
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 SNN.
features	Features to use as input for building the SNN
reduction	Reduction to use as input for building the SNN
dims	Dimensions of reduction to use as input
assay	Assay to use in construction of SNN
do.plot	Plot SNN graph on tSNE coordinates
graph.name	Optional naming parameter for stored SNN graph. Default is assay.name_snn.

Value

Returns the object with object@snn filled

Examples

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)

atakanekiz/Seurat3.0 documentation built on May 26, 2019, 2:33 a.m.