--- a
+++ b/examples/example_with_dummy_data.py
@@ -0,0 +1,122 @@
+from os.path import abspath
+from os.path import split
+
+from simdeep.simdeep_boosting import SimDeepBoosting
+
+
+def test_instance():
+    """
+    example of SimDeepBoosting
+    """
+    PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])
+
+    #Input file
+    TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv'}
+    SURVIVAL_TSV = 'survival_dummy.tsv'
+
+    # Optional metadata FILE
+    OPTIONAL_METADATA = "metadata_dummy.tsv"
+
+    # Subsetting training set with only males from metadata:
+    SUBSET_TRAINING_WITH_META = {'stage': ['I', 'II', 'III']}
+
+    PROJECT_NAME = 'TestProject'
+    SEED = 3
+    nb_it = 5 # Number of models to be built
+    nb_threads = 2 # Number of processes to be used to fit individual survival models
+
+    ################ AUTOENCODER PARAMETERS ################
+    EPOCHS = 10
+    ## Additional parameters for the autoencoders can be defined, see config.py file for details
+    #########################################################
+
+    ################ ADDITIONAL PARAMETERS ##################
+    # PATH_TO_SAVE_MODEL = '/home/username/deepprog'
+    # PVALUE_THRESHOLD = 0.01
+    # NB_SELECTED_FEATURES = 10
+    # STACK_MULTI_OMIC = False
+    #########################################################
+
+    # IT is possible to define a custom normalisation
+    # from sklearn.preprocessing import RobustScaler
+    # norm = {
+    #         'CUSTOM': RobustScaler,
+    # }
+
+    boosting = SimDeepBoosting(
+        nb_threads=nb_threads,
+        nb_it=nb_it,
+        split_n_fold=3,
+        survival_tsv=SURVIVAL_TSV,
+        training_tsv=TRAINING_TSV,
+        # metadata_tsv=OPTIONAL_METADATA, # optional
+        path_data=PATH_DATA,
+        project_name=PROJECT_NAME,
+        path_results=PATH_DATA,
+        use_r_packages=False, # to use R functions from the survival and survcomp packages
+        epochs=EPOCHS,
+        seed=SEED,
+        # normalization=norm,
+        cluster_method='coxPH',
+        metadata_usage='labels',
+        use_autoencoders=True,
+        feature_surv_analysis=True,
+        feature_selection_usage="lasso",
+        # subset_training_with_meta=SUBSET_TRAINING_WITH_META,
+        # stack_multi_omic=True,
+        # path_to_save_model=PATH_TO_SAVE_MODEL,
+        # pvalue_threshold=PVALUE_THRESHOLD,
+        # nb_selected_features=NB_SELECTED_FEATURES,
+    )
+
+    boosting.fit()
+    boosting.predict_labels_on_full_dataset()
+
+    boosting.save_models_classes()
+    boosting.save_cv_models_classes()
+
+    boosting.compute_clusters_consistency_for_full_labels()
+    boosting.evalutate_cluster_performance()
+    boosting.collect_cindex_for_test_fold()
+    boosting.collect_cindex_for_full_dataset()
+
+    boosting.compute_feature_scores_per_cluster()
+    boosting.compute_survival_feature_scores_per_cluster(pval_thres=0.10)
+    boosting.write_feature_score_per_cluster()
+    boosting.collect_number_of_features_per_omic()
+    boosting.compute_pvalue_for_merged_test_fold()
+
+    boosting.load_new_test_dataset(
+        tsv_dict={'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training
+        path_survival_file='survival_dummy.tsv', # Optional survival file of the test set for computing validation log-rank pvalue
+        fname_key='dummy', # Name of the test test to be used
+    )
+
+    boosting.predict_labels_on_test_dataset()
+    boosting.save_test_models_classes()
+
+    boosting.compute_c_indexes_for_test_dataset()
+    boosting.compute_clusters_consistency_for_test_labels()
+
+    # Experimental method to plot the test dataset amongst the class kernel densities
+    boosting.plot_supervised_kernel_for_test_sets()
+    boosting.plot_supervised_predicted_labels_for_test_sets()
+
+    boosting.load_new_test_dataset(
+        tsv_dict={'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.
+        path_survival_file='survival_dummy.tsv', # Survival file of the test set
+        fname_key='dummy_METH', # Name of the second test test
+        metadata_file="metadata_dummy.tsv" # Optional metadata
+    )
+
+    boosting.predict_labels_on_test_dataset()
+    boosting.compute_c_indexes_for_test_dataset()
+    boosting.compute_clusters_consistency_for_test_labels()
+
+    # Experimental method to plot the test dataset amongst the class kernel densities
+    boosting.plot_supervised_kernel_for_test_sets()
+    boosting.plot_supervised_predicted_labels_for_test_sets()
+
+
+if __name__ == '__main__':
+    test_instance()