Relationship Between the Number of Clusters and the Number of Unique Partitions

For each configuration provided in clust_object, display how many different partitions with the same number of clusters can be obtained by changing the seed.

plot_k_n_partitions(
  clust_object,
  colour_information = c("ecc", "freq_part"),
  dodge_width = 0.3,
  pt_size_range = c(1.5, 4),
  summary_function = stats::median,
  y_step = 5
)

Arguments

clust_object

An object returned by the assess_clustering_stability method.

colour_information

String that specifies the information type that will be illustrated using gradient colour: either freq_part for the frequency of the most common partition or ecc for the Element-Centric Consistency of the partitions obtained when the the number of clusters is fixed. Defaults to ecc.

dodge_width

Used for adjusting the distance between the boxplots representing a clustering method. Defaults to 0.3.

pt_size_range

Indicates the minimum and the maximum size a point on the plot can have. Defaults to c(1.5, 4).

summary_function

The function that will be used to summarize the distribution of the ECC values obtained for each number of clusters. Defaults to median.

y_step

The step used for the y-axis. Defaults to 5.

Value

A ggplot2 object. The color gradient suggests the frequency of the most common partition relative to the total number of appearances of that specific number of clusters or the Element-Centric Consistency of the partitions. The size illustrates the frequency of the partitions with k clusters relative to the total number of partitions. The shape of the points indicates the clustering method.

Examples

set.seed(2024)
# create an artificial PCA embedding
pca_embedding <- matrix(runif(100 * 30), nrow = 100)
rownames(pca_embedding) <- paste0("cell_", seq_len(nrow(pca_embedding)))
colnames(pca_embedding) <- paste0("PC_", 1:30)


adj_matrix <- getNNmatrix(
    RANN::nn2(pca_embedding, k = 10)$nn.idx,
    10,
    0,
    -1
)$nn
rownames(adj_matrix) <- paste0("cell_", seq_len(nrow(adj_matrix)))
colnames(adj_matrix) <- paste0("cell_", seq_len(ncol(adj_matrix)))

# alternatively, the adj_matrix can be calculated
# using the `Seurat::FindNeighbors` function.

clust_diff_obj <- assess_clustering_stability(
    graph_adjacency_matrix = adj_matrix,
    resolution = c(0.5, 1),
    n_repetitions = 10,
    clustering_algorithm = 1:2,
    verbose = FALSE
)
plot_k_n_partitions(clust_diff_obj)