• The required packages are installed as a new conda environment including both R and Python dependencies with the following command:

conda create --name envbase -f requirements_conda.yml

• The missing R packages can be found in the "requirements_r.rda" file and can be downloaded using the following commands:

load("requirements_r.rda")

for (count in 1:length(installed_packages)) {
    install.packages(installed_packages[count])
}

⚠️ For reticulate, if asked for default python virtual environment, the answer should be no to take the default conda environment into consideration

• The permimp package can be downloaded with the following commands:

install.packages('permimp', repos=NULL, type='source')

• The sandbox package can be downloaded going inside "code_dcrt" with:

* python setup.py build_ext --inplace
* pip install -e 

• For the 3 first experiments, compute_simulations is used along with plot_simulations_all:

  • Set N_SIMULATIONS to 1:100 to perform the 100 runs.

  • Set N_CPU according to the reserved resources (parallel) or 1 (serial).

  • For the first experiment:

    • Set DEBUG to FALSE.
    • Uncomment both permfit and cpi.
    • n_samples is set to 300 and n_featues is set to 100
    • Uncomment all the rho values.
    • Set prob_sim_data to regression_perm.
    • In stat_knockoff, uncomment (lasso_cv).
    • The output csv file simulation_results_blocks_100_Mi_dnn_dnn_py_300:100 is found in results/results_csv.

  • For the second experiment:

    • Set DEBUG to FALSE.
    • Uncomment both permfit and cpi.
    • Set n_samples to n_samples = `if`(!DEBUG, seq(100, 1000, by = 100), 10L) (comment line 84 and uncomment line 85).
    • Set n_features to 50
    • In prob_sim_data, comment regression_perm and uncomment all the rest.
    • In stat_knockoff, uncomment (lasso_cv).
    • The output csv file simulation_results_blocks_100_dnn_dnn_py_perm_100--1000 is found in results/results_csv.

  • For the third experiment:

    • Uncomment all methods.
    • Set n_samples to 1000 and n_features to 50.
    • In prob_sim_data, comment regression_perm and uncomment all the rest.
    • In stat_knockoff, uncomment (lasso_cv, bart and deep).
    • The output csv file simulation_results_blocks_100_allMethods_pred_final is found in results/results_csv.

  • For the forth experiment, we move to the ukbb folder:

    • The data are the public data from UK Biobank that needs to sign an agreement before using it (Any personal data are already removed).
    • In the process scripts, change method to permfit_dnn or cpi_dnn to process the data and explore the importance of the variables using one of the methods.
    • The corresponding results per method are found in Results_variables folder.

• For the section D:

  • Set DEBUG to FALSE.
  • Uncomment both cpi and loco_dnn (The last item uncommitted shouldn't be followed by a comma).
  • Set n_samples to 1000, n_features to 50 and rho to 0.8.
  • In prob_sim_data, uncomment regression.

• The output csv file simulation_results_blocks_100_CPI_LOCO_DNN is found in results/results_csv.

• For the section M:

  • We use compute_simulations_py.

  • Large scale simulation:

    • The script can be launched with the following command:

      python -u compute_simulations_py.py --n 10000 --p 50 --nsig 20 --nblocks 10 --intra 0.8 --conditional 1 --f 1 --s 100 --njobs 1
      

      • --n stands for the number of samples
      • --p stands for the number of variables
      • --nsig stands for the number of significant variables randomly chosen
      • --nblocks stands for the number of blocks/groups in the data structure
      • --intra stands for the intra correlation inside the groups
      • --conditional stands for the use of CPI (1) or PI (0)
      • --f stands for the first point of the range (Default 1)
      • --s stands for the step-size i.e. range size (Default 100)
      • --njobs stands for the serial/parallel implementation under Joblib (Default 1)
      • The csv output file simulation_results_blocks_100_n_10000_p_50_cpi_permfit is found in results/results_csv.

  • UK Biobank semi-simulation:

    • The filename should be changed to the corresponding UKBB data (not publicly available).
    • The script can be launched with the following command:

      python -u compute_simulations_py.py --nsig 115 --conditional 1 --f 1 --s 100 --njobs 1
      python -u compute_simulations_py.py --nsig 115 --conditional 0 --f 1 --s 100 --njobs 1
      

      • --nsig stands for the number of significant variables randomly chosen
      • --conditional stands for the use of CPI (1) or PI (0)
      • --f stands for the first point of the range (Default 1)
      • --s stands for the step-size i.e. range size (Default 100)
      • --njobs stands for the serial/parallel implementation under Joblib (Default 1)
      • The csv output file simulation_results_blocks_100_UKBB_single is found in results/results_csv.

• For the section N:

  • The Cam-CAN data is not publicly available, thus we provide the script process_age_prediction_CamCAN in order to compute the degree of significance for each frequency band.
  • The output csv file Result_single_FREQ_all_imp_outer_10_inner is found in camcan.

• We move to the plot_simulations_all:

• For the first experiment with simulation_results_blocks_100_Mi_dnn_dnn_py_300:100 as input:

  • Change source (at line 2) to source("utils/plot_methods_all_Mi.R").
  • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
  • Set run_plot_auc, run_plot_type1error, run_plot_power and run_time one by one to TRUE.
  • Set run_plot_combine and run_all_methods to FALSE.
  • Uncomment (Permfit-DNN and CPI-DNN).
  • The output csv files AUC_blocks_100_Mi_dnn_dnn_py_300:100, power_blocks_100_Mi_dnn_dnn_py_300:100, type1error_blocks_100_Mi_dnn_dnn_py_300:100 and time_bars_blocks_100_Mi_dnn_dnn_py_300:100 are found in results/results_csv.

• For the second experiment with simulation_results_blocks_100_dnn_dnn_py_perm_100--1000 as input:

  • Change source (at line 2) to source("utils/plot_methods_all_increasing_combine.R").
  • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
  • Set run_plot_combine to TRUE.
  • Set run_all_methods to FALSE.
  • Set run_plot_auc, run_plot_type1error, run_plot_power and run_time one by one to FALSE.
  • Uncomment (Permfit-DNN and CPI-DNN).
  • The output csv files AUC_blocks_100_dnn_dnn_py_perm_100--1000 and type1error_blocks_100_dnn_dnn_py_perm_100--1000 are found in results/results_csv.

• For the third experiment with simulation_results_blocks_100_allMethods_pred_final as input:

  • Change source (at line 2) to source("utils/plot_methods_all.R").
  • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
  • Set run_plot_auc, run_plot_type1error, run_plot_power one by one to TRUE.
  • Set run_plot_combine and run_time to FALSE.
  • Set run_all_methods and with_pval to TRUE.
  • Uncomment (Marg, d0CRT, Permfit-DNN, CPI-DNN, CPI-RF, lazyvi, cpi_knockoff, loco and Strobl).
  • The output csv files AUC_blocks_100_allMethods_pred_imp_final_withPval, power_blocks_100_allMethods_pred_imp_final`` and type1error_blocks_100_allMethods_pred_imp_finalare found inresults/results_csv```.

• For the supplementary experiments:

  • For the section D with simulation_results_blocks_100_CPI_LOCO_DNN as input:

    • Change source (at line 2) to source("utils/plot_methods_all.R").
    • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
    • Set run_plot_auc, run_plot_type1error, run_plot_power and run_time one by one to TRUE.
    • Set run_plot_combine and run_all_methods to FALSE.
    • Uncomment (LOCO-DNN and CPI-DNN).
    • The output csv files AUC_blocks_100_CPI_LOCO_DNN, power_blocks_100_CPI_LOCO_DNN, type1error_blocks_100_CPI_LOCO_DNN and time_bars_blocks_100_CPI_LOCO_DNN are found in results/results_csv.

  • For the section I with simulation_results_blocks_100_allMethods_pred_final as input:

    • Change source (at line 2) to source("utils/plot_methods_all.R").
    • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
    • Set run_plot_auc to TRUE.
    • Set run_plot_type1error, run_plot_power, run_time``````run_plot_combine and with_pval to FALSE.
    • Set run_all_methods to TRUE.
    • Uncomment (Knockoff_bart, Knockoff_lasso, Shap, SAGE, MDI, BART, Knockoff_deep, Knockoff_path and Knockoff_lasso).
    • The output csv file AUC_blocks_100_allMethods_pred_imp_final_withoutPval is found in results/results_csv.

  • For the section K with simulation_results_blocks_100_allMethods_pred_final as input:

    • Change source (at line 2) to source("utils/plot_methods_all.R").
    • Set run_time to TRUE and the rest to FALSE.
    • Uncomment all the methods.
    • The output csv file time_bars_blocks_100_allMethods_pred_imp_final is found in results/results_csv.

  • For the section M:

    • Change source (at line 2) to source("utils/plot_methods_all.R").
    • Set run_plot_auc, run_plot_type1error, run_plot_power and run_time one by one to TRUE.
    • Set run_plot_combine, run_all_methods and with_pval to FALSE.
    • Uncomment (Permfit-DNN, CPI-DNN).
    • Large scale simulation with simulation_results_blocks_100_n_10000_p_50_cpi_permfit as input:
      • Set nb_relevant to 20 and N_CPU to the number of dedicated resources.
      • The output csv files are found in results/results_csv under [AUC-type1error-power-time_bars]_blocks_100_groups_CPI_n_10000_p_50_cpi_permfit.
    • UK Biobank semi simulation:
      • Set nb_relevant to 115 and N_CPU to the number of dedicated resources.
      • The output csv files are found in results/results_csv under [AUC-type1error-power-time_bars]_blocks_100_UKBB_single.

• We move to the visualization with 4 notebooks plot_figure_simulations, plot_figure_simulations_2, plot_figure_simulations_3 plot_ukbb_results and plot_freqRes:
  • plot_figure_simulations for the plots in the main text.
  • plot_figure_simulations_2 and plot_figure_simulations_3 for the plots in the supplement.
  • plot_ukbb_results for the plot of the forth experiment.
  • plot_freqRes for the Cam-CAN corresponding plot.