plot_k_resolution_corresp: Correspondence Between Resolution and the Number of Clusters

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Correspondence Between Resolution and the Number of Clusters

Description

For each configuration provided in the clust_object, display what number of clusters appear for different values of the resolution parameters.

Usage

plot_k_resolution_corresp(
  clust_object,
  colour_information = c("ecc", "freq_k"),
  dodge_width = 0.3,
  pt_size_range = c(1.5, 4),
  summary_function = stats::median
)

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.

Value

A ggplot2 object. Different shapes of points indicate different parameter configuration, while the color illustrates the frequency of the most common partition or the Element-Centric Consistency of the partitions. The frequency is calculated as the fraction between the number of total appearances of partitions with a specific number of clusters and resolution value and the number of runs. The size illustrates the frequency of the most common partition with k clusters relative to the partitions obtained with the same resolution value and have k clusters.

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_resolution_corresp(clust_diff_obj)

Core-Bioinformatics/ClustAssess documentation built on Nov. 14, 2024, 6:33 p.m.