Hierarchical Clustering of Enriched Terms
hierarchical_term_clustering(
kappa_mat,
enrichment_res,
num_clusters = NULL,
use_description = FALSE,
clu_method = "average",
plot_hmap = FALSE,
plot_dend = TRUE
)
matrix of kappa statistics (output of create_kappa_matrix
)
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE
) or 'ID'
(if use_description = FALSE
), 'Down_regulated', and 'Up_regulated'.
If use_active_snw_genes = TRUE
, 'non_Signif_Snw_Genes' must also be
provided.
number of clusters to be formed (default = NULL
).
If NULL
, the optimal number of clusters is determined as the number
which yields the highest average silhouette width.
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE
)
the agglomeration method to be used
(default = 'average', see hclust
)
boolean to indicate whether to plot the kappa statistics clustering heatmap or not (default = FALSE)
boolean to indicate whether to plot the clustering dendrogram partitioned into the optimal number of clusters (default = TRUE)
a vector of clusters for each enriched term in the enrichment results.
The function initially performs hierarchical clustering
of the enriched terms in enrichment_res
using the kappa statistics
(defining the distance as 1 - kappa_statistic
). Next,
the clustering dendrogram is cut into k = 2, 3, ..., n - 1 clusters
(where n is the number of terms). The optimal number of clusters is
determined as the k value which yields the highest average silhouette width.
(if num_clusters
not specified)
if (FALSE) {
hierarchical_term_clustering(kappa_mat, enrichment_res)
hierarchical_term_clustering(kappa_mat, enrichment_res, method = 'complete')
}