fastcluster {scDblFinder} | R Documentation |
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.
fastcluster( x, k = NULL, rdname = "PCA", nstart = 3, iter.max = 20, ndims = NULL, nfeatures = 1000, returnType = c("clusters", "preclusters", "metacells", "graph"), BPPARAM = SerialParam() )
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. |
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.
sce <- mockDoubletSCE() sce$cluster <- fastcluster(sce)