| plot_k_n_partitions {ClustAssess} | R Documentation |
For each configuration provided in partition_obj_list, display how many different partitions with the same number of clusters can be obtained by changing the seed.
plot_k_n_partitions(partition_obj_list, object_names = NULL)
partition_obj_list |
An object or a concatenation of objects returned by the 'merge_resolutions' method. |
object_names |
Custom names that the user could assing to each configuration; if not specified, the plot will use the generated configuration names. |
A ggplot2 object. The color gradient suggests the frequency of the most common partition relative to the total number of appearances of that specific number of clusters.
set.seed(2021) # create an artificial expression matrix expr_matrix = matrix(runif(500*10), nrow = 500) # get the PCA embedding of the data pca_embedding = irlba::irlba(expr_matrix, nv = 2) pca_embedding = pca_embedding$u %*% diag(pca_embedding$d) rownames(pca_embedding) = as.character(1:500) # run the function on the pca embedding resolution_result = get_resolution_importance(embedding = pca_embedding, resolution = c(0.8, 1), n_neigh = c(5, 7), n_repetitions = 5, clustering_method = 1, graph_type = 2, object_name = "name_example") plot_k_n_partitions(resolution_result)