visualize_active_subnetworks: Visualize Active Subnetworks

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

Visualize Active Subnetworks

Description

Visualize Active Subnetworks

Usage

visualize_active_subnetworks(
  active_snw_path,
  genes_df,
  pin_name_path = "Biogrid",
  num_snws,
  layout = "stress",
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02,
  ...
)

Arguments

active_snw_path	path to the output of an Active Subnetwork Search
genes_df	the input data that was used with run_pathfindR. It must be a data frame with 3 columns: Gene Symbol (Gene Symbol) Change value, e.g. log(fold change) (optional) p value, e.g. adjusted p value associated with differential expression The change values in this data frame are used to color the affected genes
pin_name_path	Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
num_snws	number of top subnetworks to be visualized (leave blank if you want to visualize all subnetworks)
layout	The type of layout to create (see ggraph for details. Default = 'stress')
score_quan_thr	active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
sig_gene_thr	threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
...	additional arguments for input_processing

Value

a list of ggplot objects of graph visualizations of identified active subnetworks. Green nodes are down-regulated genes, reds are up-regulated genes and yellows are non-input genes

Examples

path2snw_list <- system.file(
  'extdata/resultActiveSubnetworkSearch.txt',
  package = 'pathfindR'
)
# visualize top 2 active subnetworks
g_list <- visualize_active_subnetworks(
  active_snw_path = path2snw_list,
  genes_df = example_pathfindR_input[1:10, ],
  pin_name_path = 'KEGG',
  num_snws = 2
)

pathfindR documentation built on Oct. 9, 2023, 1:07 a.m.