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from tools.preprocess import * |
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trait = "Liver_cirrhosis" |
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cohort = "GSE182065" |
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in_trait_dir = "../DATA/GEO/Liver_cirrhosis" |
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in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182065" |
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out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE182065.csv" |
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out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE182065.csv" |
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out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE182065.csv" |
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json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json" |
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soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) |
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background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) |
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unique_values_dict = get_unique_values_by_row(clinical_data) |
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print("Dataset Background Information:") |
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print("-" * 80) |
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print(background_info) |
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print("\nSample Characteristics:") |
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print("-" * 80) |
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print(json.dumps(unique_values_dict, indent=2)) |
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is_gene_available = True |
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trait_row = 1 |
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age_row = None |
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gender_row = None |
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def convert_trait(value: str) -> Optional[int]: |
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"""Convert sample group info to binary trait value""" |
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if not isinstance(value, str): |
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return None |
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value = value.lower().split(": ")[-1] |
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if "baseline" in value: |
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return 1 |
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elif "vehicle control" in value: |
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return 1 |
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elif "compound treatment" in value: |
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return 1 |
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return None |
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def convert_age(value: str) -> Optional[float]: |
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"""Convert age info to float years""" |
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return None |
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def convert_gender(value: str) -> Optional[int]: |
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"""Convert gender info to binary""" |
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return None |
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is_trait_available = trait_row is not None |
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validate_and_save_cohort_info(is_final=False, |
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cohort=cohort, |
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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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clinical_df = geo_select_clinical_features(clinical_data, |
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trait=trait, |
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trait_row=trait_row, |
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convert_trait=convert_trait, |
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age_row=age_row, |
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convert_age=convert_age, |
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gender_row=gender_row, |
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convert_gender=convert_gender) |
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preview_dict = preview_df(clinical_df) |
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print("Preview of processed clinical data:") |
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print(preview_dict) |
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os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) |
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clinical_df.to_csv(out_clinical_data_file) |