p3/preprocess/Acute_Myeloid_Leukemia/code/GSE235070.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE235070"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE235070"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE235070.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/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 the background info, this appears to be a SuperSeries about AML patients
# Cannot determine if it contains gene expression data based on limited info
is_gene_available = False

# 2.1 Data Availability
# From sample characteristics:
# Row 0 contains disease state info which indicates AML trait
trait_row = 0 

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

# 2.2. Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon and convert to binary
    # 'patient with AML' indicates positive case (1)
    if pd.isna(x):
        return None
    value = x.split(': ')[-1].strip().lower()
    if 'aml' in value:
        return 1
    return None

convert_age = None
convert_gender = 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. Clinical Feature Extraction
# Since trait_row is not None, we extract clinical features
clinical_df = 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 and save clinical data
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)