p3/preprocess/Liver_cirrhosis/code/GSE182060.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE182060"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182060"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE182060.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE182060.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE182060.csv"
json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes, this dataset contains gene expression data from liver biopsy samples
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Since this is a cohort studying fibrosis progression in liver biopsy samples,
# we can infer from the 'time_point' field whether a sample is at baseline (control)
# or follow-up (case with disease progression)
trait_row = 2  # 'time_point' field
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert time_point to binary: Baseline=0, Follow-up=1"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'Baseline':
        return 0
    elif value == 'Follow-up':
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    """Placeholder - age data not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Placeholder - gender data not available"""
    return None

# 3. Save Metadata 
# Initial filtering - only check data availability
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
# Since trait data is available (trait_row is not None), extract clinical features
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 clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4. Check for biased features and remove biased demographic ones
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
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="All subjects are male according to series summary. Age information not available."
)

# 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)