Cluster Enriched Terms

Cluster Enriched Terms

cluster_enriched_terms(
  enrichment_res,
  method = "hierarchical",
  plot_clusters_graph = TRUE,
  use_description = FALSE,
  use_active_snw_genes = FALSE,
  ...
)

Arguments

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.

method

Either 'hierarchical' or 'fuzzy'. Details of clustering are provided in the corresponding functions hierarchical_term_clustering, and fuzzy_term_clustering

plot_clusters_graph

boolean value indicate whether or not to plot the graph diagram of clustering results (default = TRUE)

use_description

Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default = FALSE)

use_active_snw_genes

boolean to indicate whether or not to use non-input active subnetwork genes in the calculation of kappa statistics (default = FALSE, i.e. only use affected genes)

...

additional arguments for hierarchical_term_clustering, fuzzy_term_clustering and cluster_graph_vis. See documentation of these functions for more details.

Value

a data frame of clustering results. For 'hierarchical', the cluster assignments (Cluster) and whether the term is representative of its cluster (Status) is added as columns. For 'fuzzy', terms that are in multiple clusters are provided for each cluster. The cluster assignments (Cluster) and whether the term is representative of its cluster (Status) is added as columns.

See also

See hierarchical_term_clustering for hierarchical clustering of enriched terms. See fuzzy_term_clustering for fuzzy clustering of enriched terms. See cluster_graph_vis for graph visualization of clustering.

Examples

example_clustered <- cluster_enriched_terms(
  example_pathfindR_output[1:3, ],
  plot_clusters_graph = FALSE
)
#> The maximum average silhouette width was 0.15 for k = 2 
#> 

example_clustered <- cluster_enriched_terms(
  example_pathfindR_output[1:3, ],
  method = 'fuzzy', plot_clusters_graph = FALSE
)