p3/preprocess/Substance_Use_Disorder/code/GSE116833.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Substance_Use_Disorder"
cohort = "GSE116833"

# Input paths
in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE116833"

# Output paths
out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE116833.csv"
out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE116833.csv"
out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE116833.csv"
json_path = "./output/preprocess/3/Substance_Use_Disorder/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
is_gene_available = True  # The background info shows it used HumanHT-12 v4.0 Gene Expression BeadChip

# 2.1 Data Availability 
# trait info can be found in key 0 showing anhedonia levels
trait_row = 0  
# age info found in key 2
age_row = 2  
# gender info found in key 1
gender_row = 1

# 2.2 Data Type Conversion
def convert_trait(x):
    """Convert anhedonia level to binary: high=1, low=0"""
    if not isinstance(x, str):
        return None
    val = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'high' in val:
        return 1
    elif 'low' in val:
        return 0
    return None

def convert_age(x):
    """Convert age to continuous numeric value"""
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    """Convert gender to binary: female=0, male=1"""
    if not isinstance(x, str):
        return None
    val = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'female' in val:
        return 0
    elif 'male' in val:
        return 1
    return None

# 3. Save Metadata
# Initial filtering based on trait and gene data availability
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
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         age_row, convert_age,
                                         gender_row, convert_gender)

# Preview the extracted features
preview_result = preview_df(clinical_df)

# Save clinical features to CSV
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])
# ILMN_ prefix indicates these are Illumina array probe IDs, not gene symbols
# These need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# First inspect where platform data begins
print("Looking for platform data section:")
with gzip.open(soft_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if "!Platform_table_begin" in line or "^PLATFORM" in line:
            print(f"Found platform marker at line {i}:")
            print(line.strip())
            # Print next few lines to see format
            for _ in range(5):
                print(next(f).strip())
            break

print("\nExtracting gene annotations...")
# Use library function to extract annotations
gene_annotation = get_gene_annotation(soft_file_path)

# Extract ID and Symbol columns which we need for mapping
mapping_df = gene_annotation[['ID', 'Symbol']].copy()

print("\nMapping data preview:")
preview = preview_df(mapping_df)
print(preview)

print("\nShape of mapping data:", mapping_df.shape)
print("Number of non-null Symbols:", mapping_df['Symbol'].count())
# Prepare mapping dataframe with correct column names
mapping_df = mapping_df.rename(columns={'Symbol': 'Gene'})

# Map probe IDs to gene symbols using predefined function 
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize gene symbols to their official HGNC names and combine rows with same gene symbol
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Print shape and preview after mapping
print("Shape after mapping:", gene_data.shape)
print("\nPreview of first few rows after mapping:")
print(preview_df(gene_data))
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)