plot_heatmap: Plot Heatmap

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

Plot Heatmap

Description

Plots heatmap of enriched terms and obtained p-values.

Usage

plot_heatmap(
  reshaped_results,
  ontology_id_colname = "ontology_id",
  ontology_element_colname = "element_id_in_ontology",
  p_value_type_colname = "eFDR",
  p_value_max_threshold = 0.05
)

Arguments

reshaped_results	data.table in relaxed form, obtained as the output of the reshape_results function. The data source for generating the barplot.
ontology_id_colname	Character, specifies the column name that contains ontology IDs in the input data.
ontology_element_colname	Character, specifying the column name that contains ontology elements or terms in the input data. Default: 'element_id_in_ontology'.
p_value_type_colname	Character, specifies the column name for p-values in the input data. Default is 'eFDR'.
p_value_max_threshold	Numeric, representing the maximum p-value threshold for filtering data. Default is 0.05.

Details

The plot_heatmap function provides a convenient way to create a ggplot2 heatmap illustrating the significance of enriched terms within ontology categories based on their associated p-values.

Value

Returns a ggplot2 heatmap.

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 heatmap                                        
plot_heatmap(reshaped_results = ora_reshaped_results,
    # the column containing the names we wish to plot
    ontology_id_colname = "ontology_id",
    # column that indicates the significance values
    p_value_type_colname = "eFDR")

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