clusKNN: A wrapper function for 'Seurat"s 'FindNeighbors' and...

Description Usage Arguments Details Value Author(s) Examples

View source: R/clusKNN.R

Description

A wrapper function for Seurat's FindNeighbors and FindClusters.

Usage

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clusKNN(
  indata,
  reducedDim = "UMAP",
  dimColnames = c("UMAP1", "UMAP2"),
  clusterAssignName = "Cluster",
  distance.matrix = FALSE,
  k.param = 20,
  compute.SNN = TRUE,
  prune.SNN = 1/15,
  nn.method = "rann",
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  force.recalc = FALSE,
  modularity.fxn = 1,
  initial.membership = NULL,
  weights = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = "matrix",
  algorithm = 1,
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  overwrite = FALSE
)

Arguments

indata

A data-frame or matrix, or SingleCellExperiment object. If a SingleCellExperiment object, the cell-to-cluster assignments will be added as a new column, specified by clusterAssignName, to the input object's metadata; if a data-frame or matrix, only the cluster assignment vector is returned.

reducedDim

A reduced dimensional component stored within indata. e.g., PCA or UMAP.

dimColnames

The column names of the dimensions to use.

clusterAssignName

The new column name in the metadata that will contain the determined cell-to-cluster assignments.

distance.matrix

Refer to ?Seurat::FindNeighbors.

k.param

Refer to ?Seurat::FindNeighbors.

compute.SNN

Refer to ?Seurat::FindNeighbors.

prune.SNN

Refer to ?Seurat::FindNeighbors.

nn.method

Refer to ?Seurat::FindNeighbors.

annoy.metric

Refer to ?Seurat::FindNeighbors.

nn.eps

Refer to ?Seurat::FindNeighbors.

verbose

Refer to ?Seurat::FindNeighbors.

force.recalc

Refer to ?Seurat::FindNeighbors.

modularity.fxn

Refer to ?Seurat::FindClusters.

initial.membership

Refer to ?Seurat::FindClusters.

weights

Refer to ?Seurat::FindClusters.

node.sizes

Refer to ?Seurat::FindClusters.

resolution

Refer to ?Seurat::FindClusters.

method

Refer to ?Seurat::FindClusters.

algorithm

Refer to ?Seurat::FindClusters.

n.start

Refer to ?Seurat::FindClusters.

n.iter

Refer to ?Seurat::FindClusters.

random.seed

Refer to ?Seurat::FindClusters.

group.singletons

Refer to ?Seurat::FindClusters.

temp.file.location

Refer to ?Seurat::FindClusters.

edge.file.name

Refer to ?Seurat::FindClusters.

overwrite

When the input object is a SingleCellExperiment, enabling this will result in the overwriting, with the new cluster assignments, of any column in your metadata that has the same name as clusterAssignName.

Details

A wrapper function for Seurat's FindNeighbors and FindClusters.

Value

A SingleCellExperiment or numeric object.

Author(s)

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>

Examples

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# create random data that follows a negative binomial
mat <- jitter(matrix(
  MASS::rnegbin(rexp(1000, rate=.1), theta = 4.5),
  ncol = 20))
colnames(mat) <- paste0('CD', 1:ncol(mat))
rownames(mat) <- paste0('cell', 1:nrow(mat))

clusKNN(mat)

scDataviz documentation built on Nov. 8, 2020, 4:58 p.m.