Runs fast Louvain cluster on the data

Runs first k-means clustering, followed by a kNN detection on the centroids to then run Louvain clustering on the graph and propagate the membership back to the original data.

Usage

rs_fast_cluster_sc(
  embd,
  km_type,
  resolutions,
  n_centroids,
  fc_params,
  snn,
  return_kmeans,
  seed,
  verbose
)

Arguments

embd

Numeric matrix. The original embedding.

km_type

String. One of c("kmeans", "minibatch") for the type of k means clustering to run.

resolutions

Numeric vector. The Louvain resolutions to iterate through.

n_centroids

Optional integer. The number of k-means centroids. If not provided, defaults to floor(sqrt(nrow(embd))).

fc_params

Named list. The fast clustering parameters.

snn

Boolean. Shall the kNN graph be additionally transformed into an sNN graph.

return_kmeans

Boolean. Shall the k-means centroid assignments be returned alongside the memberships.

seed

Integer. For reproducibility.

verbose

Integer. 0L - quiet; 1L - normal verbosity; 2L - detailed verbosity.

Value

A list with the following elements:

• membership - List with one integer membership vector per resolution.

• k_means_cluster - Integer vector with the k-means cluster per cell if return_kmeans = TRUE, otherwise NULL.

• centroids - Numerical matrix with the k-means centroids if return_kmeans = TRUE, otherwise NULL.