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from tools.preprocess import * |
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trait = "Melanoma" |
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tcga_root_dir = "../DATA/TCGA" |
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out_data_file = "./output/preprocess/3/Melanoma/TCGA.csv" |
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out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/TCGA.csv" |
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out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/TCGA.csv" |
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json_path = "./output/preprocess/3/Melanoma/cohort_info.json" |
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cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") |
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clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) |
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clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') |
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genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') |
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print("Clinical data columns:") |
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print(clinical_df.columns.tolist()) |
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is_gene_available = True |
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is_trait_available = True |
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validate_and_save_cohort_info( |
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is_final=False, |
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cohort="TCGA", |
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info_path=json_path, |
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is_gene_available=is_gene_available, |
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is_trait_available=is_trait_available |
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) |
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candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] |
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candidate_gender_cols = ['gender'] |
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clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM")) |
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clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) |
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age_preview = preview_df(clinical_df[candidate_age_cols]) |
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print("Age columns preview:") |
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print(age_preview) |
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gender_preview = preview_df(clinical_df[candidate_gender_cols]) |
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print("\nGender columns preview:") |
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print(gender_preview) |
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trait = "SKCM" |
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cohort_dir = os.path.join(tcga_root_dir, trait) |
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clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir) |
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candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"] |
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candidate_gender_cols = ["gender"] |
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age_preview = {} |
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if len(candidate_age_cols) > 0: |
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clinical_data = pd.read_csv(clinical_file_path, index_col=0) |
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for col in candidate_age_cols: |
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if col in clinical_data.columns: |
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age_preview[col] = clinical_data[col].head().tolist() |
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print("Age Column Preview:", age_preview) |
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gender_preview = {} |
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if len(candidate_gender_cols) > 0: |
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if 'clinical_data' not in locals(): |
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clinical_data = pd.read_csv(clinical_file_path, index_col=0) |
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for col in candidate_gender_cols: |
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if col in clinical_data.columns: |
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gender_preview[col] = clinical_data[col].head().tolist() |
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print("Gender Column Preview:", 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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cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") |
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clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) |
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clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') |
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genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') |
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selected_clinical_df = tcga_select_clinical_features( |
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clinical_df=clinical_df, |
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trait=trait, |
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age_col=age_col, |
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gender_col=gender_col |
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) |
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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( |
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selected_clinical_df, |
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normalized_gene_df.T, |
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left_index=True, |
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right_index=True |
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) |
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linked_data = handle_missing_values(linked_data, trait) |
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is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait) |
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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=is_biased, |
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df=cleaned_data, |
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note="This dataset contains TCGA melanoma data with normalized gene expression values" |
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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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cleaned_data.to_csv(out_data_file) |