# Path Configuration from tools.preprocess import * # Processing context trait = "Mitochondrial_Disorders" cohort = "GSE65399" # Input paths in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders" in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE65399" # Output paths out_data_file = "./output/preprocess/3/Mitochondrial_Disorders/GSE65399.csv" out_gene_data_file = "./output/preprocess/3/Mitochondrial_Disorders/gene_data/GSE65399.csv" out_clinical_data_file = "./output/preprocess/3/Mitochondrial_Disorders/clinical_data/GSE65399.csv" json_path = "./output/preprocess/3/Mitochondrial_Disorders/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 # Background info shows "Gene expression profiles were obtained using the Illumina HT12v4 Gene Expression BeadArray" is_gene_available = True # 2. Variable Availability and Data Type Conversion # From background info, this is a study about FRDA patients # The differentiation/tissue type in row 0 indicates disease state information trait_row = 0 age_row = None # Age not available in characteristics gender_row = None # Gender not available in characteristics def convert_trait(x): if x is None or pd.isna(x): return None val = x.split(': ')[-1].lower() # Samples are neural progenitors or fetal tissues # Neural progenitors are FRDA patient-derived cells if 'neural progenitors' in val: return 1 # FRDA patient elif 'fetal' in val: return 0 # Control tissue return None def convert_age(x): if x is None or pd.isna(x): return None val = x.split(': ')[-1].lower() try: return float(val) except: return None def convert_gender(x): if x is None or pd.isna(x): return None val = x.split(': ')[-1].lower() if 'female' in val or 'f' in val: return 0 elif 'male' in val or 'm' in val: return 1 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. Clinical Feature Extraction # Trait data is available, so extract clinical features clinical_df = geo_select_clinical_features( 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 clinical data print("\nPreview of clinical data:") print(preview_df(clinical_df)) # Save clinical data clinical_df.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 identifiers starting with ILMN_ are Illumina probe IDs, not gene symbols 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)) # Get gene mapping from annotation data mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # Apply gene mapping to convert probe measurements to gene expression values gene_data = apply_gene_mapping(genetic_data, mapping_df) # Save gene expression data gene_data.to_csv(out_gene_data_file) # 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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements" 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)