| # Path Configuration | |
| from tools.preprocess import * | |
| # Processing context | |
| trait = "HIV_Resistance" | |
| cohort = "GSE117748" | |
| # Input paths | |
| in_trait_dir = "../DATA/GEO/HIV_Resistance" | |
| in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE117748" | |
| # Output paths | |
| out_data_file = "./output/preprocess/3/HIV_Resistance/GSE117748.csv" | |
| out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE117748.csv" | |
| out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE117748.csv" | |
| json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json" | |
| # Get relevant file paths | |
| soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) | |
| # Extract background info and clinical data from the matrix file | |
| background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) | |
| # Get dictionary of unique values per row in clinical data | |
| unique_values_dict = get_unique_values_by_row(clinical_data) | |
| # Print background info | |
| print("Background Information:") | |
| print("-" * 50) | |
| print(background_info) | |
| print("\n") | |
| # Print clinical data unique values | |
| print("Sample Characteristics:") | |
| print("-" * 50) | |
| for row, values in unique_values_dict.items(): | |
| print(f"{row}:") | |
| print(f" {values}") | |
| print() | |
| # 1. Gene Expression Data Availability | |
| # This is a miRNA study on cell lines (based on the title and sample characteristics) | |
| is_gene_available = False | |
| # 2.1 Data Availability | |
| # From sample characteristics, no human trait, age or gender data available | |
| trait_row = None | |
| age_row = None | |
| gender_row = None | |
| # 2.2 Data Type Conversion functions (not used but defined for completeness) | |
| 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 - initial filtering | |
| validate_and_save_cohort_info( | |
| is_final=False, | |
| cohort=cohort, | |
| info_path=json_path, | |
| is_gene_available=is_gene_available, | |
| is_trait_available=False # trait_row is None | |
| ) | |
| # 4. Clinical Feature Extraction | |
| # Skip since trait_row is None |