# Path Configuration from tools.preprocess import * # Processing context trait = "Psoriasis" cohort = "GSE178228" # Input paths in_trait_dir = "../DATA/GEO/Psoriasis" in_cohort_dir = "../DATA/GEO/Psoriasis/GSE178228" # Output paths out_data_file = "./output/preprocess/3/Psoriasis/GSE178228.csv" out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE178228.csv" out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE178228.csv" json_path = "./output/preprocess/3/Psoriasis/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data using specified prefixes background_info, clinical_data = get_background_and_clinical_data( matrix_file, prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'] ) # 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 # From the background info we can see this is a study measuring gene expression in skin samples at different time points is_gene_available = True # 2. Variable Availability and Data Type Conversion # For trait, we can use pasi (Psoriasis Area and Severity Index) scores from Feature 2 trait_row = 2 def convert_trait(x): # Extract numeric PASI value after colon try: return float(x.split(': ')[1]) except: return None # Age and gender not provided in sample characteristics age_row = None gender_row = None convert_age = None convert_gender = None # 3. Save 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 # Since trait_row is not None, we extract clinical features 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 extracted clinical data preview_df(selected_clinical_df) # Save clinical data selected_clinical_df.to_csv(out_clinical_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # 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) # Based on the probe IDs like "2824546_st", this appears to be Affymetrix microarray data # that needs to be mapped to gene symbols requires_gene_mapping = True # Extract gene annotation data gene_metadata = get_gene_annotation(soft_file) # Preview the annotation data print("Column names:", gene_metadata.columns.tolist()) print("\nFirst few rows preview:") print(preview_df(gene_metadata)) # Looking at the gene expression data and annotation data: # Gene expression data uses IDs like "2824546_st" # Gene symbols are embedded in the 'gene_assignment' field and need to be extracted using extract_human_gene_symbols # Get mapping between probe IDs and gene symbols mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(gene_data, mapping_data) gene_data = normalize_gene_symbols_in_index(gene_data) # Save gene data gene_data.to_csv(out_gene_data_file) # Print info about the number of genes print(f"Number of original probes: {len(gene_metadata)}") print(f"Number of probe-gene mappings: {len(mapping_data)}") print(f"Final number of unique genes: {len(gene_data)}") # 1. Get raw genetic data gene_data = get_genetic_data(matrix_file) print(f"Initial probes: {len(gene_data)}") # 2. Get mapping between probe IDs and gene symbols mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='probeset_id', gene_col='gene_assignment') print(f"Mappings found: {len(mapping_data)}") # 3. Apply mapping and normalize gene_data = apply_gene_mapping(gene_data, mapping_data) print(f"Genes after mapping: {len(gene_data)}") gene_data = normalize_gene_symbols_in_index(gene_data) print(f"Genes after normalization: {len(gene_data)}") gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data # Clinical data is available with PASI scores as trait values clinical_data = selected_clinical_df linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate 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 with PASI scores as trait values from skin biopsies." ) # 6. Save if usable if is_usable: linked_data.to_csv(out_data_file) # Looking at the gene expression data and gene annotation data: # Gene expression data has IDs like "2824546_st" # Gene annotation dictionary has column 'gene_assignment' that contains gene symbols # Prepare annotation data with correct column names and no duplicates gene_metadata = gene_metadata.rename(columns={'probeset_id': 'ID'}) gene_metadata = gene_metadata.drop_duplicates(subset=['ID']) # Get gene mapping from annotation data # Use 'ID' as identifier column and 'gene_assignment' as gene symbol column mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(gene_data, mapping_data) # Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) # Save gene data gene_data.to_csv(out_gene_data_file) # Print info about the number of genes print(f"Number of original probes: {len(gene_metadata)}") print(f"Number of probe-gene mappings: {len(mapping_data)}") print(f"Final number of unique genes: {len(gene_data)}")