mulea (version 1.0.1)

ora-class: An S4 class to represent a set based tests in mulea.

Description

An S4 class to represent a set based tests in mulea.

Value

ora object. This object represents the result of the overrepresentation test in mulea.

Slots

method

The overrepresentation (ora) method. Possible values: "Hypergeometric", "SetBasedEnrichment".

gmt

A data.frame representing the ontology GMT.

element_names

A vector of elements names (gene or protein names or identifiers) representing the target set to analyse. For example differentially expressed genes.

background_element_names

A vector of elements names (gene or protein names or identifiers) representing all the elements involved in the previous analyses For example all genes that were measured in differential expression analysis.

p_value_adjustment_method

A character string representing the type of the p-value adjustment method. Possible values:

• 'eFDR': empirical false discovery rate correction method

• all method options from stats::p.adjust documentation.

number_of_permutations

A numeric value representing the number of permutations used to calculate the eFDR values. Default value is 10000.

nthreads

Number of processor's threads to use in calculations.

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)