p3/preprocess/Cystic_Fibrosis/code/GSE139038.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE139038"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE139038"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE139038.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE139038.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE139038.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on background info mentioning "gene expression studies using microarray"

# 2.1 Data Availability
trait_row = 3  # Cancer stage indicates disease status
age_row = 0    # Age data available
gender_row = 1  # Gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not x or pd.isna(x):
        return None
    val = x.split(': ')[-1]
    if val in ['Apparent normal', 'Paired normal']:
        return 0  # Normal
    return 1  # Cancer

def convert_age(x):
    if not x or pd.isna(x):
        return None
    try:
        return int(x.split(': ')[-1])
    except:
        return None

def convert_gender(x):
    if not x or pd.isna(x):
        return None
    val = x.split(': ')[-1].lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return 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
if trait_row is not None:
    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)
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Review the gene identifiers - these appear to be probe IDs rather than gene symbols
# They follow a pattern like "10_10_1" which is typical for microarray probe IDs
# This indicates we need to map these to proper gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify mapping between probe IDs ('ID') and gene symbols ('Gene_Symbol')
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene_Symbol')

# 2. Apply gene mapping to convert probe measurements to gene expression levels
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the gene expression data
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values 
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Cell line study comparing deltaF508 CFTR mutant with wildtype CFTR in cystic fibrosis bronchial epithelial cells"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)