fastcluster {scDblFinder} | R Documentation

## 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 = 20,
      ndims = NULL,
      nfeatures = 1000,
      returnType = c("clusters", "preclusters", "metacells", "graph"),
      BPPARAM = SerialParam()
    )

### 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')
`returnType` | See return.
`BPPARAM` | 'BiocParallel' BPPARAM for multithreading.

### 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)

[Package scDblFinder version 1.4.0 Index]