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from tools.preprocess import * |
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trait = "Breast_Cancer" |
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tcga_root_dir = "../DATA/TCGA" |
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out_data_file = "./output/preprocess/3/Breast_Cancer/TCGA.csv" |
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out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/TCGA.csv" |
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out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/TCGA.csv" |
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json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json" |
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cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Breast_Cancer_(BRCA)') |
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clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) |
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clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) |
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genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0) |
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print("Clinical data columns:") |
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print(clinical_df.columns.tolist()) |
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candidate_age_cols = ["Age_at_Initial_Pathologic_Diagnosis_nature2012", "age_at_initial_pathologic_diagnosis"] |
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candidate_gender_cols = ["Gender_nature2012", "gender"] |
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clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "BRCA")) |
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clinical_df = pd.read_csv(clinical_file_path, index_col=0) |
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age_preview = clinical_df[candidate_age_cols].head(5).to_dict('list') |
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print("Age columns preview:") |
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print(age_preview) |
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gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict('list') |
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print("\nGender columns preview:") |
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print(gender_preview) |
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age_col = "age_at_initial_pathologic_diagnosis" |
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gender_col = "gender" |
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print(f"Selected age column: {age_col}") |
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print(f"Selected gender column: {gender_col}") |
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age_col = "age_at_initial_pathologic_diagnosis" |
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gender_col = "gender" |
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cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Breast_Cancer_(BRCA)') |
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clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) |
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clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) |
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genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0) |
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clinical_data = tcga_select_clinical_features(clinical_df, trait="Breast_Cancer", |
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age_col=age_col, |
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gender_col=gender_col) |
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normalized_gene_df = normalize_gene_symbols_in_index(genetic_df) |
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os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) |
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normalized_gene_df.to_csv(out_gene_data_file) |
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linked_data = pd.merge(clinical_data, normalized_gene_df.T, left_index=True, right_index=True) |
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linked_data = handle_missing_values(linked_data, trait_col="Breast_Cancer") |
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trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait="Breast_Cancer") |
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note = "Data contains TCGA breast cancer samples with normalized gene expression." |
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is_usable = validate_and_save_cohort_info( |
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is_final=True, |
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cohort="TCGA", |
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info_path=json_path, |
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is_gene_available=True, |
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is_trait_available=True, |
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is_biased=trait_biased, |
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df=linked_data, |
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note=note |
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) |
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if is_usable: |
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os.makedirs(os.path.dirname(out_data_file), exist_ok=True) |
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linked_data.to_csv(out_data_file) |