p3/preprocess/Cardiovascular_Disease/code/GSE273225.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE273225"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE273225"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE273225.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE273225.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE273225.csv"
json_path = "./output/preprocess/3/Cardiovascular_Disease/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
# Based on Series_overall_design, this is an immune gene expression study using nCounter technology
is_gene_available = True

# 2.1 Data Availability
# For trait, we can use rewarming ischemia time as a continuous trait
trait_row = 12 

# Age is available in donor age field 
age_row = 3

# Gender is available in donor sex field
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> float:
    # Extract rewarming ischemia time as continuous value
    try:
        return float(value.split(": ")[1]) 
    except:
        return None

def convert_age(value: str) -> float:
    # Extract age as continuous value
    try:
        return float(value.split(": ")[1])
    except:
        return None
        
def convert_gender(value: str) -> int:
    # Convert gender to binary (0=female, 1=male)
    try:
        gender = value.split(": ")[1].lower()
        if gender == "female":
            return 0
        elif gender == "male":
            return 1
        return None
    except:
        return None

# 3. Save initial metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, is_gene_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
    )
    
    # Preview output
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_df.index)[:20])
# The identifiers appear to be standard human HGNC gene symbols
# Evidence:
# 1. ABCB1 is official symbol for ATP Binding Cassette Subfamily B Member 1
# 2. ABCF1 is official symbol for ATP Binding Cassette Subfamily F Member 1  
# 3. ABL1 is official symbol for ABL Proto-Oncogene 1
# 4. B2M is official symbol for Beta-2-Microglobulin
# 5. Follows standard HGNC nomenclature format

requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_df = normalize_gene_symbols_in_index(genetic_df)
genetic_df.to_csv(out_gene_data_file)

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

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

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

# 5. Final validation and save cohort info
note = "Clinical data contains continuous trait (rewarming ischemia time) with age and gender information from lung transplant donors."
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 if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)