p3/preprocess/Substance_Use_Disorder/code/GSE103580.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Substance_Use_Disorder"
cohort = "GSE103580"

# Input paths
in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE103580"

# Output paths
out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE103580.csv"
out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE103580.csv"
out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE103580.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
# Based on the background information, this is a transcriptome profiling study 
# examining gene expression in liver tissues using NanoString platform
is_gene_available = True

# 2. Variable Analysis
# 2.1 Data Availability

# Trait (alcoholic liver disease stage) is available in row 0
trait_row = 0

# Age and gender data not available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions 
def convert_trait(value: str) -> int:
    """Convert alcoholic liver disease stage to binary:
    0 for mild cases (steatosis, mild hepatitis)
    1 for severe cases (cirrhosis)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'cirrhosis' in value:
        return 1
    elif 'steatosis' in value or 'mild' in value:
        return 0
    return None

# No age conversion needed
convert_age = None

# No gender conversion needed  
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
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 extracted features
    preview = preview_df(selected_clinical_df)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save to CSV
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_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])
# The identifiers like "11715100_at" are probe IDs from Affymetrix microarrays
# They need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Get gene mapping data by extracting ID and Gene Symbol columns
# 'ID' contains probe IDs matching gene expression data, 'Gene Symbol' contains gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Map probe data to gene expression data 
gene_data = apply_gene_mapping(expression_df=genetic_data, mapping_df=gene_mapping)

# Preview the mapped gene data
print("Gene data shape:", gene_data.shape)
preview = preview_df(gene_data)
print("\nGene data preview:")
print(preview)

# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)