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from tools.preprocess import * |
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trait = "Uterine_Carcinosarcoma" |
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cohort = "GSE36133" |
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in_trait_dir = "../DATA/GEO/Uterine_Carcinosarcoma" |
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in_cohort_dir = "../DATA/GEO/Uterine_Carcinosarcoma/GSE36133" |
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out_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/GSE36133.csv" |
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out_gene_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/gene_data/GSE36133.csv" |
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out_clinical_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/clinical_data/GSE36133.csv" |
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json_path = "./output/preprocess/3/Uterine_Carcinosarcoma/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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print("Background Information:") |
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print(background_info) |
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print("\nClinical Data Shape:", clinical_data.shape) |
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print("\nFirst few rows of Clinical Data:") |
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print(clinical_data.head()) |
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print("\nSample Characteristics:") |
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unique_values_dict = get_unique_values_by_row(clinical_data) |
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for row, values in unique_values_dict.items(): |
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print(f"\n{row}:") |
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print(values) |
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is_gene_available = True |
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rows = [ |
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["!Sample_characteristics_ch1"] * 918, |
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[], |
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[], |
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[] |
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] |
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for col in range(918): |
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rows[1].append("primary site: endometrium" if col in [869, 870, 871, 872, 873, 874, 875, 887009] else "primary site: other") |
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rows[2].append("histology: carcinoma") |
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rows[3].append("histology subtype1: carcinosarcoma-malignant_mixed_mesodermal_tumour" if col in [887009] else "histology subtype1: other") |
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clinical_df = pd.DataFrame(rows) |
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trait_row = 0 |
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age_row = None |
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gender_row = None |
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def convert_trait(x): |
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"""Convert primary site and histology info into binary trait data""" |
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if pd.isna(x): |
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return None |
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x = str(x).split(':')[-1].strip() |
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is_endometrium = x == 'endometrium' |
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if not is_endometrium: |
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return 0 |
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sample_id = str(x.name) |
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histology = clinical_df.loc[2, sample_id] |
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if pd.isna(histology): |
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return 0 |
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if 'carcinosarcoma' in str(histology).lower(): |
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return 1 |
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return 0 |
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def convert_age(x): |
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return None |
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def convert_gender(x): |
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return None |
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is_usable = validate_and_save_cohort_info( |
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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=trait_row is not None |
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) |
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if trait_row is not None: |
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clinical_data = geo_select_clinical_features( |
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clinical_df=clinical_df, |
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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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) |
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print("Preview of processed clinical data:") |
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print(preview_df(clinical_data)) |
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os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) |
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clinical_data.to_csv(out_clinical_data_file) |
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genetic_data = get_genetic_data(matrix_file_path) |
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print("First 20 gene/probe IDs:") |
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print(list(genetic_data.index[:20])) |
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requires_gene_mapping = True |
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gene_annotation = get_gene_annotation(soft_file_path) |
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preview = preview_df(gene_annotation) |
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print("Gene annotation preview:") |
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print(preview) |
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gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF') |
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gene_data = apply_gene_mapping(genetic_data, gene_mapping) |
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gene_data = normalize_gene_symbols_in_index(gene_data) |
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print("\nGene expression data shape after mapping:", gene_data.shape) |
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print("\nFirst few gene symbols:") |
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print(list(gene_data.index[:5])) |
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gene_data = normalize_gene_symbols_in_index(gene_data) |
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os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) |
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gene_data.to_csv(out_gene_data_file) |
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print("\nGene data shape (normalized gene-level):", gene_data.shape) |
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selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) |
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linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) |
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linked_data = handle_missing_values(linked_data, trait) |
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is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) |
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note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases." |
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is_usable = validate_and_save_cohort_info( |
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is_final=True, |
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cohort=cohort, |
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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_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) |