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Runs fast Louvain cluster on the data (with multiple seeds)

rs_fast_cluster_sc_grid.Rd

[Experimental] Runs first k-means clustering, followed by a kNN detection on the centroids to then run Louvain clustering with several seeds (based on the original one) on the graph and propagate the membership back to the original data. Returns additional metrics around cluster stability and community conductance.

Usage

rs_fast_cluster_sc_grid(
  embd,
  km_type,
  resolutions,
  n_centroids,
  fc_params,
  snn,
  return_kmeans,
  no_seeds,
  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.

no_seeds

Integer. Number of additional seeds to use. Should be >=2.

seed

Integer. For reproducibility.

verbose

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

Value

A list with the following elements:

  • membership - A list with memberships (one integer vector per resolution, from the seed with the best conductance) and stats (list with mean_ari, median_ari, mean_conductance, median_conductance and mean_n_comms, one value 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.