Seurat (version 2.3.4)

BuildSNN: SNN Graph Construction

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

BuildSNN(object, genes.use = NULL, reduction.type = "pca",
  dims.use = NULL, k.param = 10, plot.SNN = FALSE, prune.SNN = 1/15,
  print.output = TRUE, distance.matrix = NULL, force.recalc = FALSE,
  filename = NULL, save.SNN = TRUE, nn.eps = 0)

Arguments

object

Seurat object

genes.use

A vector of gene names to use in construction of SNN graph if building directly based on expression data rather than a dimensionally reduced representation (i.e. PCs).

reduction.type

Name of dimensional reduction technique to use in construction of SNN graph. (e.g. "pca", "ica")

dims.use

A vector of the dimensions to use in construction of the SNN graph (e.g. To use the first 10 PCs, pass 1:10)

k.param

Defines k for the k-nearest neighbor algorithm

plot.SNN

Plot the SNN 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).

print.output

Whether or not to print output to the console

distance.matrix

Build SNN from distance matrix (experimental)

force.recalc

Force recalculation of SNN.

filename

Write SNN directly to file named here as an edge list compatible with FindClusters

save.SNN

Default behavior is to store the SNN in object@snn. Setting to FALSE can be used together with a provided filename to only write the SNN out as an edge file to disk.

nn.eps

Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search

Value

Returns the object with object@snn filled

Examples

# NOT RUN {
pbmc_small
# Compute an SNN on the gene expression level
pbmc_small <- BuildSNN(pbmc_small, genes.use = [e-mail])

# More commonly, we build the SNN on a dimensionally reduced form of the data
# such as the first 10 principle components.

pbmc_small <- BuildSNN(pbmc_small, reduction.type = "pca", dims.use = 1:10)

# }

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