output/preprocess/Cervical_Cancer/code/GSE138079.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE138079"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138079"

# Output paths
out_data_file = "./output/z2/preprocess/Cervical_Cancer/GSE138079.csv"
out_gene_data_file = "./output/z2/preprocess/Cervical_Cancer/gene_data/GSE138079.csv"
out_clinical_data_file = "./output/z2/preprocess/Cervical_Cancer/clinical_data/GSE138079.csv"
json_path = "./output/z2/preprocess/Cervical_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# Decision rationale:
# - Gene expression: YES (Agilent mRNA microarray on human keratinocyte cell lines)
# - Trait (Cervical_Cancer): NOT available (in vitro HPV-transformed keratinocyte cell lines; no human case/control)
# - Age/Gender: NOT available (cell lines; no subject-level age/gender; any implied sex would be constant and unusable)

# Set availability flags and row indices
is_gene_available = True
trait_row = None
age_row = None
gender_row = None
is_trait_available = trait_row is not None

# Converters (defined for interface completeness; not used since trait_row is None)
def convert_trait(x):
    if x is None:
        return None
    # extract value after colon if present
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    v = val.strip().lower()
    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
        return None
    # Generic heuristic mapping: cancer/tumor/carcinoma -> 1; normal/control/healthy -> 0
    if any(k in v for k in ['cancer', 'tumor', 'carcinoma', 'malignant', 'case']):
        return 1
    if any(k in v for k in ['normal', 'control', 'healthy', 'benign']):
        return 0
    # Not confidently mappable
    return None

def convert_age(x):
    if x is None:
        return None
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    v = val.strip().lower()
    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
        return None
    # Extract first numeric token as age (in years) if present
    import re
    m = re.search(r'(\d+(\.\d+)?)', v)
    if not m:
        return None
    try:
        return float(m.group(1))
    except Exception:
        return None

def convert_gender(x):
    if x is None:
        return None
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    v = val.strip().lower()
    if v in {'', 'na', 'n/a', 'none', 'unknown', 'missing'}:
        return None
    # Map female->0, male->1
    if any(k in v for k in ['female', 'f']):
        return 0
    if any(k in v for k in ['male', 'm']):
        return 1
    return None

# Save metadata (initial filtering)
_ = 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
)

# Since trait_row is None, skip clinical feature extraction.