fastcluster: fastcluster

In plger/scDblFinder: scDblFinder

fastcluster

Description

Performs a fast two-step clustering: first clusters using k-means with a very large k, then uses louvain clustering of the k cluster averages and reports back the cluster labels.

Usage

fastcluster(
  x,
  k = NULL,
  rdname = "PCA",
  nstart = 3,
  iter.max = 50,
  ndims = NULL,
  nfeatures = 1000,
  verbose = TRUE,
  returnType = c("clusters", "preclusters", "metacells", "graph"),
  ...
)

Arguments

x	An object of class SCE
k	The number of k-means clusters to use in the primary step (should be much higher than the number of expected clusters). Defaults to 1/10th of the number of cells with a maximum of 3000.
rdname	The name of the dimensionality reduction to use.
nstart	Number of starts for k-means clustering
iter.max	Number of iterations for k-means clustering
ndims	Number of dimensions to use
nfeatures	Number of features to use (ignored if 'rdname' is given and the corresponding dimensional reduction exists in 'sce')
verbose	Logical; whether to output progress messages
returnType	See return.
...	Arguments passed to 'scater::runPCA' (e.g. BPPARAM or BSPARAM) if 'x' does not have 'rdname'.

Value

By default, a vector of cluster labels. If ‘returnType=’preclusters'', returns the k-means pre-clusters. If ‘returnType=’metacells'', returns the metacells aggretated by pre-clusters and the corresponding cell indexes. If ‘returnType=’graph'', returns the graph of (meta-)cells and the corresponding cell indexes.

Examples

sce <- mockDoubletSCE()
sce$cluster <- fastcluster(sce)

plger/scDblFinder documentation built on Oct. 19, 2024, 6:26 a.m.