df_weight_signaling, df_weight_metabolic_signaling = tfm_data.def_load_weight_pathways( ) df_paper_9437, df_signaling, df_metabolic_signaling = tfm_data.def_load_dataset( ['cell_type'] + list(df_weight_signaling.index.values), ['cell_type'] + list(df_weight_metabolic_signaling.index.values), row_scaling=TYPE_OF_SCALING) df_weight_ppi_tf_signaling, df_weight_ppi_tf_metabolic_signaling = tfm_data.def_load_weight_ppi_tf( list(df_weight_signaling.index.values), list(df_weight_metabolic_signaling.index.values)) df_weight_both = pd.concat( [df_weight_ppi_tf_metabolic_signaling, df_weight_metabolic_signaling], axis=1) print('df_weight_both shape , ', df_weight_both.shape) print('Normalization metabolic and signaling data') df_ss = tfm_data.def_dataframe_normalize(df_metabolic_signaling, StandardScaler(), 'cell_type') # DELETE UNUSED DATASET del (df_paper_9437) del (df_signaling) del (df_weight_ppi_tf_signaling) del (df_weight_signaling) del (df_metabolic_signaling) del (df_weight_ppi_tf_metabolic_signaling) # METABOLIC and SIGNALING PATHWAY # EXPERIMENT DATASETS print('the index of experiment dataset, ', list_experiments) for i_experiment in list_experiments:
df_weight_paper_signaling_dense_pathway.shape) df_weight_paper_metabolic_signaling_dense_pathway = df_weight_dense.merge( pd.DataFrame(df_paper.columns[1:]).set_index('Sample').merge( df_weight_metabolic_signaling, left_index=True, right_index=True, how='left').fillna(0), left_index=True, right_index=True, how='inner') print('df_weight_paper_metabolic_signaling_dense_pathway shape , ', df_weight_paper_metabolic_signaling_dense_pathway.shape) print('Normalization paper data - 9437 genes') df_ss = tfm_data.def_dataframe_normalize(df_paper, StandardScaler(), 'cell_type') # df_mms = tfm_data.def_dataframe_normalize(df_paper, MinMaxScaler(), 'cell_type') # # ORIGINAL DATASET (9437 genes) # EXPERIMENT DATASETS array_train_X_ss, array_train_y_ss = [], [] array_test_X_ss, array_test_y_ss = [], [] X_train_ss, X_test_ss, y_train_ss, y_test_ss = tfm_data.def_split_train_test_by_index( dataframe_=df_ss, train_index_=df_ss.index, test_index_=[0], target_feature_=target_) array_train_X_ss.append(np.array(X_train_ss))