p3/preprocess/Melanoma/code/GSE202806.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE202806"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE202806"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE202806.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE202806.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE202806.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Yes, this contains gene expression data of 770 cancer-related genes
is_gene_available = True

# 2.1 Data Availability
# Trait (NF1 mutation status) is available in row 1
trait_row = 1

# Age and gender not available
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert NF1 mutation status to binary - WT=0, MUT=1"""
    if value and isinstance(value, str):
        value = value.split(": ")[-1].strip().upper()
        if value == 'MUT':
            return 1
        elif value == 'WT':
            return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float - not used since age not available"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary - not used since gender not available"""
    return None

# 3. Save metadata
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 exists
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 and save clinical data
print("Clinical data preview:")
preview = preview_df(clinical_df)
print(preview)

# Save clinical features
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Since we determined in Step 2 that this dataset contains methylation data rather than gene expression data,
# we will not proceed with gene expression data extraction.
# The preprocessing pipeline should stop here since this dataset is not suitable for our gene expression analysis.
print("\nSkipping gene expression data extraction since this dataset contains methylation data rather than gene expression data.")