plot_clustering_per_value_stability: Clustering Method per value Stability Boxplot

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Clustering Method per value Stability Boxplot

Description

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)

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