p3/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertrophic_Cardiomyopathy"
cohort = "GSE36961"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"

# Output paths
out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/GSE36961.csv"
out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/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)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Series title and summary indicate this is transcriptome profiling data
is_gene_available = True

# 2.1 Data Availability
# Trait is in row 3, gender in row 0, age in row 1
trait_row = 3  # disease state / sample type shows HCM vs control
gender_row = 0  # Sex field
age_row = 1   # age (yrs) field

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (1=case, 0=control)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[-1].lower()
    if "hypertrophic cardiomyopathy" in value or "hcm" in value or "case" in value:
        return 1
    elif "control" in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age value to continuous numeric"""
    if pd.isna(value):
        return None
    try:
        return float(value.split(": ")[-1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[-1].lower()
    if value == "female":
        return 0
    elif value == "male":
        return 1
    return None

# 3. Save Metadata 
# trait_row is not None, so trait data is available
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. Extract Clinical Features
selected_clinical = 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 clinical data
preview_result = preview_df(selected_clinical)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)

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

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."
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=note
)

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