p3/preprocess/Esophageal_Cancer/code/GSE55857.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE55857"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE55857"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE55857.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE55857.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE55857.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# This is a microRNA dataset (SuperSeries) studying small non-coding RNAs
# MicroRNA data is not suitable for our gene expression analysis
is_gene_available = False

# 2. Clinical Data Variables Analysis
# 2.1 Data Availability
# Trait (cancer status) is available in row 1 as "tissue" field
# Age and gender are not recorded
trait_row = 1
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert tissue type to binary cancer status"""
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].lower().strip()
    if "tumor" in value:
        return 1
    elif "normal" in value:
        return 0
    return None

def convert_age(value):
    """Convert age value - not used"""
    return None

def convert_gender(value):
    """Convert gender value - not used"""
    return None

# 3. Save Metadata
# trait_row is not None, so trait data is available
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. Extract Clinical Features
# Since trait_row is not None, we extract clinical features
clinical_features = geo_select_clinical_features(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 features
preview_results = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_results)

# Save clinical features
clinical_features.to_csv(out_clinical_data_file)