# Path Configuration from tools.preprocess import * # Processing context trait = "Rectal_Cancer" cohort = "GSE94104" # Input paths in_trait_dir = "../DATA/GEO/Rectal_Cancer" in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE94104" # Output paths out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE94104.csv" out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE94104.csv" out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE94104.csv" json_path = "./output/preprocess/3/Rectal_Cancer/cohort_info.json" # Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) print("Background Information:") print(background_info) print("\nSample Characteristics:") # Get dictionary of unique values per row unique_values_dict = get_unique_values_by_row(clinical_data) for row, values in unique_values_dict.items(): print(f"\n{row}:") print(values) # 1. Gene Expression Data Availability # The background info mentions RNA and expression beadchip, so gene expression data is likely available is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability trait_row = 2 # Tumour regression grade is available and varies age_row = None # Age not provided gender_row = None # Gender not provided # 2.2 Data Type Conversion Functions def convert_trait(value): if not isinstance(value, str): return None # Extract numeric grade after colon try: grade = int(value.split(': ')[1]) # Convert to binary: grade 1-2 (good response) vs grade 3 (poor response) return 0 if grade <= 2 else 1 except: return None # No age or gender conversion functions needed since data not available convert_age = None convert_gender = None # 3. Save Metadata 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=trait_row is not None ) # 4. Clinical Feature Extraction if trait_row is not None: # Extract available features clinical_features = 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 the extracted features preview = preview_df(clinical_features) print("Preview of clinical features:") print(preview) # Save to CSV clinical_features.to_csv(out_clinical_data_file) # Get gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Examine data structure print("Data structure and head:") print(genetic_data.head()) print("\nShape:", genetic_data.shape) print("\nFirst 20 row IDs (gene/probe identifiers):") print(list(genetic_data.index)[:20]) # Get a few column names to verify sample IDs print("\nFirst 5 column names:") print(list(genetic_data.columns)[:5]) # The gene identifiers start with "ILMN_" which indicates these are Illumina probe IDs # They need to be mapped to standard human gene symbols for downstream analysis requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Display column names and preview data print("Column names:") print(gene_annotation.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_annotation)) # 1. Identify relevant columns # The gene expression data uses "ILMN_" IDs which match the 'ID' column in annotation # The 'Symbol' column contains gene symbols # 2. Get mapping between probe IDs and gene symbols mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # 3. Apply mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Reload clinical data that was processed earlier selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, trait) # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and information saving note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=True, is_biased=trait_biased, df=linked_data, note=note ) # 6. Save linked data only if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)