Graph Visualization of Clustered Enriched Terms

cluster_graph_vis(
  clu_obj,
  kappa_mat,
  enrichment_res,
  kappa_threshold = 0.35,
  use_description = FALSE,
  vertex.label.cex = 0.7,
  vertex.size.scaling = 2.5
)

Arguments

clu_obj: clustering result (either a matrix obtained via hierarchical_term_clustering or fuzzy_term_clustering `fuzzy_term_clustering` or a vector obtained via `hierarchical_term_clustering`)
kappa_mat: matrix of kappa statistics (output of create_kappa_matrix)
enrichment_res: 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.
kappa_threshold: threshold for kappa statistics, defining strong relation (default = 0.35)
use_description: Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default = FALSE)
vertex.label.cex: font size for vertex labels; it is interpreted as a multiplication factor of some device-dependent base font size (default = 0.7)
vertex.size.scaling: scaling factor for the node size (default = 2.5)

Value

Plots a graph diagram of clustering results. Each node is an enriched term from `enrichment_res`. Size of node corresponds to -log(lowest_p). Thickness of the edges between nodes correspond to the kappa statistic between the two terms. Color of each node corresponds to distinct clusters. For fuzzy clustering, if a term is in multiple clusters, multiple colors are utilized.

Examples

if (FALSE) {
cluster_graph_vis(clu_obj, kappa_mat, enrichment_res)
}