plot_clustering_overall_stability: Clustering Method Overall Stability Boxplot

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Clustering Method Overall Stability Boxplot

Description

Display EC consistency across clustering methods by summarising the distribution of the EC consistency for each number of clusters.

Usage

plot_clustering_overall_stability(
  clust_object,
  value_type = c("k", "resolution"),
  summary_function = stats::median
)

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.
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.

Value

A ggplot2 object with the EC consistency distributions grouped by the clustering methods. Higher consistency indicates a more stable clustering.

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_overall_stability(clust_diff_obj)

Core-Bioinformatics/ClustAssess documentation built on Nov. 4, 2024, 1:05 p.m.