plot_clustering_overall_stability {ClustAssess} | R Documentation |
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
|
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
|
summary_function |
The function that will be used to summarize the
distribution of the ECC values obtained for each number of clusters. Defaults
to |
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)
[Package ClustAssess version 1.1.0 Index]