p3/preprocess/Post-Traumatic_Stress_Disorder/code/GSE77164.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Post-Traumatic_Stress_Disorder"
cohort = "GSE77164"

# Input paths
in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE77164"

# Output paths
out_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/GSE77164.csv"
out_gene_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/gene_data/GSE77164.csv"
out_clinical_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/clinical_data/GSE77164.csv"
json_path = "./output/preprocess/3/Post-Traumatic_Stress_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
# From dictionary indices 8-15, we can see there are multiple gene expression data (cd3d, cd3e, cd4, etc.)
is_gene_available = True

# 2.1 Data Availability 
# trait (PTSD) is available in row 6 ('pts: 0', 'pts: 1')
trait_row = 6

# age is available in row 2 with various values
age_row = 2

# gender is available in row 1 ('female: 1', 'female: 0')
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
    """Convert PTSD status to binary (0/1)"""
    if not isinstance(x, str):
        return None
    try:
        # Extract value after colon
        value = x.split(': ')[1]
        return int(value)
    except:
        return None

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

def convert_gender(x: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if not isinstance(x, str):
        return None
    try:
        # Extract value after colon
        value = x.split(': ')[1]
        # In this dataset, female=1, male=0, so we need to flip the values
        return 1 - int(value)  # Convert female:1 to 0, female:0 to 1
    except:
        return None

# 3. Save Metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 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 data
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    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])
requires_gene_mapping = False
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols using data from previous step
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

# 3. Handle missing values systematically  
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 = "Dataset contains gene expression data from child soldiers and civilians in Nepal, with PTSD symptoms and psychological resilience measures. All required features (trait, age, gender) are available with good distributions."
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
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)