create_term_gene_graph: Create Term-Gene Graph

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

Create Term-Gene Graph

Description

Create Term-Gene Graph

Usage

create_term_gene_graph(
  result_df,
  genes_df = NULL,
  order_by = "lowest_p",
  term_size = "num_genes",
  term_fill = NULL,
  num_terms = 10,
  use_description = FALSE,
  use_edge_weights = FALSE
)

Arguments

result_df	A dataframe of pathfindR results that must contain the following columns: Term_Description Description of the enriched term (necessary if use_description = TRUE) ID ID of the enriched term (necessary if use_description = FALSE) lowest_p the lowest adjusted-p value of the given term over all iterations Up_regulated the up-regulated genes in the input involved in the given term's gene set, comma-separated Down_regulated the down-regulated genes in the input involved in the given term's gene set, comma-separated
genes_df	(optional) the input data that was used with run_pathfindR (default: NULL). It must be a data frame with at least 2 columns: Gene.Symbol (required) logFC (required)
order_by	Argument to order the 'result_df', this influences the 'num_terms' displayed (default: 'lowest_p').
term_size	Argument to indicate whether to use number of significant genes ('num_genes')
term_fill	Argument to indicate by what column to fill the term nodes (e.g. term_fill = "Fold_Enrichment") (default: NULL).
num_terms	Number of top enriched terms to use while creating the graph. Set to NULL to use all enriched terms (default = 10, i.e. top 10 terms)
use_description	Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default: FALSE)
use_edge_weights	Boolean argument to indicate whether genes are weighted by their term interactions, similar to an Up-Set plot but in graph context (default = FALSE). or the -log10(lowest p value) ('p_val') for adjusting the term node sizes (default: 'num_genes')

Details

This function constructs an igraph object from pathfindR output, creating a network that connects enriched biological terms to their involved genes. By default, the graph connects term nodes to up-regulated genes and down-regulated genes. The size of term nodes can be adjusted by either the number of significant genes (‘term_size = ’num_genes'') or by the statistical significance (‘term_size = ’p_val'', using -log10(lowest p value)).

When 'genes_df' is provided, gene nodes contain values and not mere up/down binary values, allowing visualization of expression direction and magnitude. When 'term_fill' is supplied, term nodes obtain values enabling simultaneous visualization of pathway enrichment strength.

Setting 'use_edge_weights = TRUE' highlights hub genes by weighting edges based on how many terms a gene participates in, similar to an Up-Set plot but in a graph context. The 'num_terms' parameter controls how many top enriched terms are included (default: top 10), and 'order_by' determines the ordering criterion for term selection. The resulting igraph object can be visualized using create_term_gene_plot.

Value

A igraph object

Examples

# Normal gene-term with up/down regulated genes
g <- create_term_gene_graph(
  result_df = example_pathfindR_output
)
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  num_terms = 5
)
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_size = "p_val"
)

# Coloring the term nodes
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_fill = "Fold_Enrichment"
)

# Adding edge weights
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_fill = "Fold_Enrichment",
  use_edge_weights = TRUE
)

pathfindR documentation built on July 2, 2026, 1:06 a.m.