p3/preprocess/Endometriosis/code/GSE111974.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE111974"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE111974"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE111974.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE111974.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE111974.csv"
json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on series title and summary mentioning "RNA expression", and focusing on endometrial tissue
is_gene_available = True

# 2. Trait, Age, Gender Data Analysis
# 2.1 Data Availability
# trait row: We can infer endometriosis status from being in RIF vs control group
trait_row = 0  

# Age and gender not explicitly available in sample characteristics
age_row = None
gender_row = None 

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert RIF/Control status to binary
    From background: RIF group = cases, Fertile control = controls"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1]
    if "endometrial tissue" in value:
        # Here we can't determine case/control status from this field alone
        return None
    return None

def convert_age(value):
    return None  # Age data not available

def convert_gender(value): 
    return None  # Gender data not available

# 3. Save Initial Metadata
is_trait_available = trait_row is not None
is_usable = 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 (skip since trait conversion gives None)
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 results
    print(preview_df(clinical_df))
    
    # Save to CSV
    clinical_df.to_csv(out_clinical_data_file)
# 1. Gene Expression Data: No gene expression data found
is_gene_available = False

# 2. Variable availability and conversion
trait_row = None  # No trait information available
age_row = None  # No age information available
gender_row = None  # No gender information available

# No conversion functions needed since no data is available
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save 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. Skip clinical feature extraction since trait_row is None