Plots heatmap of enriched terms and obtained p-values.
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
)Returns a ggplot2 heatmap.
data.table in relaxed form, obtained as the output
of the reshape_results function. The data source for generating the
barplot.
Character, specifies the column name that contains ontology IDs in the input data.
Character, specifying the column name that contains ontology elements or terms in the input data. Default: 'element_id_in_ontology'.
Character, specifies the column name for p-values in the input data. Default is 'eFDR'.
Numeric, representing the maximum p-value threshold for filtering data. Default is 0.05.
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.
reshape_results
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")
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