Clustering Method Stability Facet Plot — plot_clustering_difference

Display the distribution of the EC consistency for each clustering method and each resolution value on a given embedding The `all` field of the object returned by the `get_clustering_difference_object` method is used.

plot_clustering_difference_facet(
  clustering_difference_object,
  embedding,
  low_limit = 0,
  high_limit = 1,
  grid = TRUE
)

Arguments

clustering_difference_object: An object returned by the `get_clustering_difference_object` method.
embedding: An embedding (only the first two dimensions will be used for visualization).
low_limit: The lowest value of ECC that will be displayed on the embedding.
high_limit: The highest value of ECC that will be displayed on the embedding.
grid: Boolean value indicating whether the facet should be a grid (where each row is associated with a resolution value and each column with a clustering method) or a wrap.

Value

A ggplot2 object.

Examples

set.seed(2021)
# create an artificial expression matrix
expr_matrix = matrix(c(runif(250*10), runif(250*10, min = 5, max = 7)), nrow = 500)
rownames(expr_matrix) = as.character(1:500)

pca_embedding = irlba::irlba(expr_matrix, nv = 2)
pca_embedding = pca_embedding$u %*% diag(pca_embedding$d)
rownames(pca_embedding) = as.character(1:500)

adj_matrix = Seurat::FindNeighbors(pca_embedding,
    k.param = 10,
    nn.method = "rann",
    verbose = FALSE,
    compute.SNN = FALSE)$nn
clust_diff_obj = get_clustering_difference(graph_adjacency_matrix = adj_matrix,
    resolution = c(0.5, 1),
    n_repetitions = 10,
    algorithm = 1:2,
    verbose = FALSE)
plot_clustering_difference_facet(clust_diff_obj,pca_embedding)