# Comparing Two pathfindR Results

The function `combine_pathfindR_results()` allows combination of two pathfindR active-subnetwork-oriented enrichment analysis results for investigating common and distinct terms between the groups. Below is an example for comparing results using two different rheumatoid arthritis-related data sets (`example_pathfindR_output` and `example_comparison_output`).

    combined_df <- combine_pathfindR_results(
      result_A = example_pathfindR_output,
      result_B = example_comparison_output,
      plot_common = FALSE
    )
    #> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms

By default, `combine_pathfindR_results()` plots the term-gene graph for the common terms in the combined results. For not plotting the graph, set `plot_common = FALSE`.

The function `combined_results_graph()` can be used to create this graph (using only selected terms etc.) later on. By default, the function creates the graph using all common terms:

    combined_results_graph(combined_df)
    #> Warning: ggrepel: 138 unlabeled data points (too many overlaps). Consider
    #> increasing max.overlaps

By supplying a vector of selected terms to the `selected_terms` arguments, you may plot the term-gene graph for the selected terms:

    combined_results_graph(
      combined_df,
      selected_terms = c("hsa04144", "hsa04141", "hsa04140")
    )

By default, `combined_results_graph()` creates the graph using term IDs. To use term descriptions instead, set `use_description = TRUE`:

    combined_results_graph(
      combined_df,
      use_description = TRUE,
      selected_terms = combined_df$Term_Description[1:4]
    )

For changing the layout of the graph (`"auto"` by default), you may use the `layout` argument.

For changing how the sizes of the term nodes are determined, you may use the `node_size` argument. The options are `"num_genes"` (default) and `"p_val"` for using the number of significant genes in the term and the -log10(p) value of the term, respectively:

    combined_results_graph(
      combined_df,
      selected_terms = c("hsa04144", "hsa04141", "hsa04140"),
      node_size = "p_val"
    )