run_test {mulea}

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 signature 'ora'
run_test(model)

Arguments

model

Object of S4 class representing the mulea 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.

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.

Methods (by class)

• run_test(ora): ora test.

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)