Data engineering and BI courses are free!
This function takes a model and model_results data, reshapes them into a suitable format for plotting, and returns the resulting data frame, which can be used for further analysis or visualization.
reshape_results(
model = NULL,
model_results = NULL,
model_ontology_col_name = "ontology_id",
ontology_id_colname = "ontology_id",
p_value_type_colname = "eFDR",
p_value_max_threshold = TRUE
)Return detailed and relaxed data.table where model and results are
merged for plotting purposes.
a mulea model, created by the
ora or the gsea functions.
Result data.frame
returned by the run_test function.
Character, specifies the column name in the model that contains ontology IDs. It defines which column in the model should be used for matching ontology IDs. Possible values are 'ontology_id' and 'ontology_name'. The default value is 'ontology_id'.
Character, specifies the column name for ontology IDs in the model results. It indicates which column in the model results contains ontology IDs for merging. Possible values are 'ontology_id' and 'ontology_name'. The default value is 'ontology_id'.
Character, specifies the column name
for the type or raw or adjusted p-value in the result
data.frame returned by the run_test function.
The default value is 'eFDR'.
Logical, indicating whether to apply a p-value threshold when filtering the resulting data. If TRUE, the function filters the data based on a p-value threshold.
plot_graph, plot_barplot,
plot_heatmap
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")
Run the code above in your browser using DataLab