# Path Configuration from tools.preprocess import * # Processing context trait = "Pancreatic_Cancer" cohort = "GSE157494" # Input paths in_trait_dir = "../DATA/GEO/Pancreatic_Cancer" in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494" # Output paths out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE157494.csv" out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE157494.csv" out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE157494.csv" json_path = "./output/preprocess/3/Pancreatic_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 = filter_content_by_prefix(matrix_file, prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'], unselect=False, source_type='file', return_df_a=False, return_df_b=True, transpose=True) # 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") # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = filter_content_by_prefix( matrix_file, prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], prefixes_b=['!Sample_characteristics_ch'], unselect=False, source_type='file', return_df_a=False, return_df_b=True ) # 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 - the series summary mentions gene expression profiling with Affymetrix Gene Chip is_gene_available = True # 2. Variable Availability and Data Type Conversion # Sample Characteristics output is empty, indicating no clinical data available trait_row = None age_row = None gender_row = None def convert_trait(x): return None def convert_age(x): return None def convert_gender(x): return None # 3. Save metadata # Initial filtering - save info that this dataset has gene data but no clinical data validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=False) # 4. Clinical Feature Extraction # Skip since trait_row is None (no clinical data available) # 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 IDs (e.g. 1007_s_at, 1053_at), these are Affymetrix probe IDs # from HG-U133_Plus_2 array platform, not gene symbols. # They need to be mapped to human gene symbols for standardized analysis requires_gene_mapping = True # Get file paths using library function soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Preview gene annotation data print("Gene annotation columns and example values:") print(preview_df(gene_annotation)) # Looking at gene_data index ['1007_s_at', '1053_at', '117_at'...] and # gene_annotation dictionary preview, 'ID' column contains probe IDs matching gene_data index, # and 'Gene Symbol' column contains the gene symbols we need # Create mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') # Convert probe-level measurements to gene expression data gene_data = apply_gene_mapping(gene_data, gene_mapping) # Normalize gene symbols to standard format using synonym dictionary gene_data = normalize_gene_symbols_in_index(gene_data) # First get the gene expression data again gene_data = get_genetic_data(matrix_file) gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') gene_data = apply_gene_mapping(gene_data, gene_mapping) gene_data = normalize_gene_symbols_in_index(gene_data) # Save normalized gene data gene_data.to_csv(out_gene_data_file) # Update cohort info - dataset unusable due to lack of clinical data is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=None, df=None, note="Gene expression data available but no clinical annotations/controls present." ) # Get file paths using library function soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Preview gene annotation data print("Gene annotation columns and example values:") print(preview_df(gene_annotation)) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get gene annotation first gene_annotation = get_gene_annotation(soft_file) # Get gene expression data gene_data = get_genetic_data(matrix_file) # Create mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') # Convert probe-level measurements to gene expression data gene_data = apply_gene_mapping(gene_data, gene_mapping) # Print dimensions of result for verification print(f"\nShape of gene expression data after mapping: {gene_data.shape}") # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get gene annotation and expression data gene_annotation = get_gene_annotation(soft_file) gene_data = get_genetic_data(matrix_file) # Create mapping and apply it gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') gene_data = apply_gene_mapping(gene_data, gene_mapping) # Normalize gene symbols and save gene data gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # Update cohort info - dataset unusable due to lack of clinical data is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=None, df=None, note="Gene expression data available but no clinical annotations/controls present." )