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Work with GCAE from R.
gcaer is not on CRAN yet. To install gcaer:
library(remotes)
install_github("richelbilderbeek/gcaer")
This assumes you have the remotes package installed.
To install GCAE:
library(gcaer)
install_gcae()
Get the GCAE help text:
library(gcaer)
get_gcae_help_text()
Run GCAE:
library(gcaer)
run_gcae("--help")
Instead of using the multiple steps by GenoCAE,
do_gcae_experiment does all of these for you.
Here is an example of a full experiment:
# Create the parameters for the experiment
gcae_experiment_params <- create_gcae_experiment_params(
gcae_options = create_gcae_options(),
gcae_setup = create_test_gcae_setup(
model_id = "M0",
superpops = get_gcaer_filename("gcae_input_files_1_labels.csv"),
pheno_model_id = "p0"
),
analyse_epochs = c(1, 2),
metrics = "f1_score_3,f1_score_5"
)
# Do the experiment
gcae_experiment_results <- do_gcae_experiment(
gcae_experiment_params = gcae_experiment_params
)
# Save the experiment's results
save_gcae_experiment_results(
gcae_experiment_results = gcae_experiment_results,
folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir
)
# Create the plots for the experiment's results
create_plots_from_gcae_experiment_results(
folder_name = gcae_experiment_params$gcae_setup$trainedmodeldir
)
To do the full GCAE workflow, a gcae_setup is needed,
from which the respective gcae_[x] functions are called,
where [x] matches the first GCAE CLI argument (for
example, use gcaer's gcae_train to do the same as run_gcae.py train)
gcae_setup <- create_gcae_setup(
datadir = file.path(get_gcae_folder(), "example_tiny/"),
data = "issue_6_bin",
model_id = "M1",
pheno_model_id = "p2",
superpops = file.path(datadir, "HO_superpopulations")
)
# 2. Train, approx 3 mins
train_filenames <- gcae_train(
gcae_setup = gcae_setup,
epochs = 3,
save_interval = 1
)
# 3. Project
project_filenames <- gcae_project(
gcae_setup = gcae_setup
)
project_results <- parse_project_files(project_filenames)
# 4. Evaluate
evaluate_filenames <- gcae_evaluate(
gcae_setup,
metrics = "f1_score_3,f1_score_5",
epoch = 3
)
evaluate_results <- parse_evaluate_filenames(
evaluate_filenames,
epoch = 3
)