Clustering Method per value Stability Boxplot

Display EC consistency across clustering methods, calculated for each value of the resolution parameter or the number of clusters.

Usage

plot_clustering_per_value_stability(
  clust_object,
  value_type = c("k", "resolution")
)

Arguments

clust_object

An object returned by the assess_clustering_stability method.

value_type

A string that specifies the type of value that was used for grouping the partitions and calculating the ECC score. It can be either k or resolution. Defaults to k.

Value

A ggplot2 object with the EC consistency distributions grouped by the clustering methods. Higher consistency indicates a more stable clustering. The X axis is decided by the value_type parameter.

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_clustering_per_value_stability(clust_diff_obj)