plot_lollipop {mulea}

Plot Lollipop

Description

Plots lollipop plot of p-values.

Usage

plot_lollipop(
  reshaped_results,
  ontology_id_colname = "ontology_id",
  selected_rows_to_plot = NULL,
  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.

selected_rows_to_plot

A numeric vector specifying which rows of the reshaped results data frame should be included in the plot. Default is NULL. frame should be included in the plot?

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

Create a customized lollipop plot of p-values, facilitating visual exploration and analysis of statistical significance within ontology categories.

Value

Returns a lollipop 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 lollipop
plot_lollipop(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")