# Path Configuration from tools.preprocess import * # Processing context trait = "Endometriosis" cohort = "GSE111974" # Input paths in_trait_dir = "../DATA/GEO/Endometriosis" in_cohort_dir = "../DATA/GEO/Endometriosis/GSE111974" # Output paths out_data_file = "./output/preprocess/3/Endometriosis/GSE111974.csv" out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE111974.csv" out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE111974.csv" json_path = "./output/preprocess/3/Endometriosis/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Based on series title and summary mentioning "RNA expression", and focusing on endometrial tissue is_gene_available = True # 2. Trait, Age, Gender Data Analysis # 2.1 Data Availability # trait row: We can infer endometriosis status from being in RIF vs control group trait_row = 0 # Age and gender not explicitly available in sample characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): """Convert RIF/Control status to binary From background: RIF group = cases, Fertile control = controls""" if not isinstance(value, str): return None value = value.lower().split(": ")[-1] if "endometrial tissue" in value: # Here we can't determine case/control status from this field alone return None return None def convert_age(value): return None # Age data not available def convert_gender(value): return None # Gender data not available # 3. Save Initial Metadata is_trait_available = trait_row is not None is_usable = validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available ) # 4. Extract Clinical Features (skip since trait conversion gives None) if trait_row is not None: clinical_df = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) # Preview results print(preview_df(clinical_df)) # Save to CSV clinical_df.to_csv(out_clinical_data_file) # 1. Gene Expression Data: No gene expression data found is_gene_available = False # 2. Variable availability and conversion trait_row = None # No trait information available age_row = None # No age information available gender_row = None # No gender information available # No conversion functions needed since no data is available def convert_trait(x): return None def convert_age(x): return None def convert_gender(x): return None # 3. Save metadata validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=(trait_row is not None) ) # 4. Skip clinical feature extraction since trait_row is None