Cluster Enriched Terms

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

- 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.

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 `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.

```
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
)
```