p3/preprocess/Depression/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE273630"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE273630"

# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE273630.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE273630.csv"
json_path = "./output/preprocess/3/Depression/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# Gene Expression Data Availability
is_gene_available = True  # Based on background info mentioning "digital transcript panel" and "genes"

# Trait, Age and Gender Data Availability 
trait_row = None  # Depression data not available - this is a study on HIV and Methamphetamine use
age_row = None  # Age is constant (35-44 years) based on background info
gender_row = None  # Gender is constant (all males) based on background info

# Convert functions (defined but not used since no data available)
def convert_trait(x):
    if x is None or pd.isna(x):
        return None
    val = str(x).split(':')[-1].strip().lower()
    # Convert depression status to binary
    if 'yes' in val or 'true' in val or 'positive' in val:
        return 1
    elif 'no' in val or 'false' in val or 'negative' in val:
        return 0
    return None

def convert_age(x):
    if x is None or pd.isna(x):  
        return None
    val = str(x).split(':')[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(x):
    if x is None or pd.isna(x):
        return None
    val = str(x).split(':')[-1].strip().lower()
    if 'female' in val or 'f' in val:
        return 0
    elif 'male' in val or 'm' in val:
        return 1
    return None

# Save metadata - initial filtering
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
)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These appear to be standard human gene symbols (e.g. ABAT, ABL1, ACHE, etc.)
# No mapping required - they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols and save
genetic_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_df.to_csv(out_gene_data_file)

# Since trait data is not available, we create a minimal dataframe for validation
minimal_df = pd.DataFrame(index=genetic_df.columns)

# Final validation - dataset not usable due to missing trait data 
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Set to True since we can't use a dataset without trait data
    df=minimal_df,
    note="Dataset focuses on HIV and Methamphetamine use, depression data not available"
)