plot_graph: Plot Graph (Network)

In mulea: Enrichment Analysis using Multiple Ontologies and False Discovery Rate

Plot Graph (Network)

Description

Plots graph representation of enrichment results.

Usage

plot_graph(
  reshaped_results,
  ontology_id_colname = "ontology_id",
  ontology_element_colname = "element_id_in_ontology",
  shared_elements_min_threshold = 0,
  p_value_type_colname = "eFDR",
  p_value_max_threshold = 0.05
)

Arguments

reshaped_results	Character, the input data.table containing the reshaped results.
ontology_id_colname	Character, the name of the column in the reshaped results that contains ontology identifiers or names. Default value is 'ontology_id'.
ontology_element_colname	Character, the name of the column in the reshaped results that contains element identifiers within the ontology. Default value is 'element_id_in_ontology'.
shared_elements_min_threshold	Numeric, threshold specifying the minimum number of shared elements required between two ontologies to consider them connected by an edge on the graph. Default value is 0.
p_value_type_colname	Character, the name of the column in the reshaped results that contains the type of p-values associated with the ontology elements. Default value is 'eFDR'.
p_value_max_threshold	Numeric, a threshold value for filtering rows in the reshaped results based on the p-values. Rows with p-values greater than this threshold will be filtered out. Default value is 0.05.

Details

This function generates a graph (network) visualization of the enriched ontology entries. On the plot each node represents an ontology entry below a given p-value threshold, and is coloured based on its significance level. A connection (edge) is drawn between two nodes if they share at least one common element (gene) belonging to the target set – in the case of ORA results – or all analysed elements – in the case of GSEA results.

Value

Returns a graph plot.

See Also

reshape_results

Examples

library(mulea)

# loading and filtering the example ontology from a GMT file
tf_gmt <- read_gmt(file = system.file(package="mulea", "extdata", 
    "Transcription_factor_RegulonDB_Escherichia_coli_GeneSymbol.gmt"))
tf_gmt_filtered <- filter_ontology(gmt = tf_gmt, min_nr_of_elements = 3, 
    max_nr_of_elements = 400)

# loading the example data
sign_genes <- readLines(system.file(package = "mulea", "extdata", 
    "target_set.txt"))
background_genes <- readLines(system.file(
    package="mulea", "extdata", "background_set.txt"))

# creating the ORA model
ora_model <- ora(gmt = tf_gmt_filtered,
    # the test set variable
    element_names = sign_genes, 
    # the background set variable
    background_element_names = background_genes, 
    # the p-value adjustment method
    p_value_adjustment_method = "eFDR", 
    # the number of permutations
    number_of_permutations = 10000,
    # the number of processor threads to use
    nthreads = 2)
# running the ORA
ora_results <- run_test(ora_model)

# reshaping results for visualisation
ora_reshaped_results <- reshape_results(model = ora_model,
    model_results = ora_results,
    # choosing which column to use for the indication of significance
    p_value_type_colname = "eFDR")

# Plot graph
plot_graph(reshaped_results = ora_reshaped_results,
    # the column containing the names we wish to plot
    ontology_id_colname = "ontology_id",
    # upper threshold for the value indicating the significance
    p_value_max_threshold = 0.05,
    # column that indicates the significance values
    p_value_type_colname = "eFDR")

mulea documentation built on Sept. 30, 2024, 9:44 a.m.