# Path Configuration from tools.preprocess import * # Processing context trait = "Bladder_Cancer" cohort = "GSE222073" # Input paths in_trait_dir = "../DATA/GEO/Bladder_Cancer" in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE222073" # Output paths out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE222073.csv" out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE222073.csv" out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE222073.csv" json_path = "./output/preprocess/3/Bladder_Cancer/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 # Yes, this appears to be a gene expression dataset based on mentions of RNA subtypes and labeling kits is_gene_available = True # 2.1 Data Availability # Trait: Available from various metastasis indicators (rm-* fields) # Will use rm-bone as representative of metastasis trait_row = 11 # Age and Gender not available in sample characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert bone metastasis status to binary (0: no, 1: yes)""" if not isinstance(value, str): return None value = value.lower() if 'rm-bone:' not in value: return None value = value.split('rm-bone:')[1].strip() if value == 'yes': return 1 elif value == 'no': return 0 return None def convert_age(value: str) -> float: """Placeholder for age conversion""" return None def convert_gender(value: str) -> int: """Placeholder for gender conversion""" return None # 3. Save Initial Metadata is_trait_available = trait_row is not None 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. Clinical Feature Extraction if trait_row is not None: selected_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 the data preview = preview_df(selected_clinical_df) print("Preview of clinical data:") print(preview) # Save clinical data selected_clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Inspect a snippet of raw file to verify identifier format import gzip with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: lines = [] for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Get the next 5 lines after the marker for _ in range(5): lines.append(next(f).strip()) break print("\nFirst few lines after matrix marker in raw file:") for line in lines: print(line) # Looking at the gene identifiers, we see a mix of symbols like 'A2M', 'A4GALT' which are human gene symbols, # and some numbered identifiers like '1-Mar', '2-Mar' etc. # The presence of 'Mar' suggests these might be month-related probe identifiers that need mapping. requires_gene_mapping = True # Get file paths using library function soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # First locate the platform table section in SOFT file import gzip table_begin = None table_end = None with gzip.open(soft_file, 'rt', encoding='utf-8') as f: for i, line in enumerate(f): if '!platform_table_begin' in line.lower(): table_begin = i elif '!platform_table_end' in line.lower() and table_begin is not None: table_end = i break # Read annotation data between markers using skiprows and nrows import pandas as pd if table_begin is not None and table_end is not None: gene_annotation = pd.read_csv(soft_file, compression='gzip', skiprows=table_begin+1, nrows=table_end-table_begin-1, sep='\t') else: # Fallback to original method if markers not found gene_annotation = get_gene_annotation(soft_file) # Preview gene annotation data print("Gene annotation preview:") print(preview_df(gene_annotation)) # Display all column names print("\nAll column names in annotation data:") print(gene_annotation.columns.tolist()) # In this dataset, the probe ID "A2M" in the gene expression data should be matched to the gene symbol in 'ORF' column gene_mapping = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='ORF') # Convert probe-level measurements to gene expression data gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping) # Save the gene expression data gene_data.to_csv(out_gene_data_file) # Preview results print("Shape of mapped gene expression data:", gene_data.shape) print("\nFirst few rows of mapped data:") print(gene_data.head()) # 1. Normalize gene symbols and save normalized gene data # Remove "-mRNA" suffix from gene symbols before normalization gene_data.index = gene_data.index.str.replace('-mRNA', '') gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data and trait # First get selected clinical features using the extraction function from previous step selected_clinical = 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 ) # Debug data structures before linking print("\nPre-linking data shapes:") print("Clinical data shape:", selected_clinical.shape) print("Gene data shape:", gene_data.shape) print("\nClinical data preview:") print(selected_clinical.head()) # Transpose gene data to match clinical data orientation gene_data_t = gene_data.T linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features and remove them if needed is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate data quality and save metadata 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=is_biased, df=linked_data, note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)." ) # 6. Save linked data if usable if is_usable: linked_data.to_csv(out_data_file)