mulea (version 1.1.0)

run_test: Run enrichment analysis procedure

Description

This is a generic function that chooses an enrichment analysis procedure based on the model class and runs the analysis.

Usage

run_test(model)
# S4 method for ora
run_test(model)

Value

Results in form of data.frame. Structure of data.frame depends on object processed by this generic method. In the case of run_test was used with the model generated by the ora function the returned data.frame contains the following columns:

1. 'ontology_id': Identifiers of the ontology elements.

2. 'ontology_name': Names of the ontology elements.

3. 'nr_common_with_tested_elements': Number of common elements between the ontology element and the vector defined by the element_names parameter of the ora function.

4. 'nr_common_with_background_elements': Number of common elements between the ontology element and the vector defined by the background_element_names parameter of the ora function.

5. 'p_value': The raw p-value of the overrepresentation analysis.

6. The adjusted p-value. The column named based on the p_value_adjustment_method parameter of the ora function, e.g. 'eFDR'

In the case of run_test was used with the model generated by the gsea function the returned data.frame contains the following columns:

1. 'ontology_id': Identifiers of the ontology elements.

2. 'ontology_name': Names of the ontology elements.

3. 'nr_common_with_tested_elements': Number of common elements between the ontology element and the vector defined by the element_names parameter of the gsea function.

4. 'p_value': The raw p-value of the gene set enrichment analysis.

5. 'adjusted_p_value': The adjusted p-value.

run_test method for ora object. Returns the results of the overrepresentation analysis.

Arguments

model

Object of S4 class representing the mulea test.

Methods (by class)

• run_test(ora): ora test.

Details

The function requires the definition of a model. Models currently implemented in mulea include Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA). These models must be defined through their specific functions which are provided in this package.

See Also

gsea, ora

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)

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)