p3/preprocess/Cardiovascular_Disease/code/GSE256539.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE256539"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE256539"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE256539.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE256539.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE256539.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 background info, this is a gene expression dataset using GeoMx Nanostring platform
is_gene_available = True

# 2.1 Data Availability
# Based on background info:
# - Trait (PAH vs Control) can be inferred from sample IDs - all start with either control ('CC') or PAH patterns
trait_row = 0 
# Age and gender are not provided in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(x: str) -> int:
    """Convert sample ID to binary trait (0: Control, 1: PAH)"""
    if not isinstance(x, str):
        return None
    # Extract sample ID after colon and strip whitespace
    sample_id = x.split(':')[-1].strip()
    # CC indicates control samples
    if sample_id.startswith('CC'):
        return 0
    # All other patterns (AH,UA,BA,VA,UC) are PAH samples
    elif any(sample_id.startswith(x) for x in ['AH','UA','BA','VA','UC']):
        return 1
    return None

# Age and gender conversion functions not needed since data unavailable
convert_age = None
convert_gender = None

# 3. Save Initial 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. Extract Clinical Features
if trait_row is not None:
    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 processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    clinical_df.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])
# These appear to be standard human gene symbols (HUGO nomenclature)
# For example:
# A2M - Alpha-2-Macroglobulin 
# AAAS - Aladin WD Repeat Nucleoporin
# AACS - Acetoacetyl-CoA Synthetase
# ABCC1 - ATP Binding Cassette Subfamily C Member 1

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_df, 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 = "Gene expression data from pulmonary vascular lesions in IPAH patients compared to control pulmonary arteries. Contains trait (PAH vs Control) data but lacks age and gender information."
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)