p3/preprocess/Schizophrenia/code/GSE120342.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE120342"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE120342"

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE120342.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE120342.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE120342.csv"
json_path = "./output/preprocess/3/Schizophrenia/cohort_info.json"

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

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on title mentioning "transcriptomes"

# 2.1 Identify rows containing variables
trait_row = 0  # Disease state information in row 0
age_row = None  # Age not provided 
gender_row = None  # Gender not provided

# 2.2 Data type conversion functions
def convert_trait(value):
    """Convert disease state to binary (0=control, 1=SCZ)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'scz' in value:
        return 1
    elif 'control' in value:
        return 0
    return None  # Exclude BD cases

# Call validate_and_save_cohort_info for initial filtering
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=trait_row is not None
)

# Extract clinical features if trait data is available
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview the processed clinical data
    preview = preview_df(selected_clinical)
    print("Processed clinical data preview:", preview)
    
    # Save clinical data
    selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file and check data type
genetic_data = get_genetic_data(matrix_file_path)
if genetic_data.index[0].startswith('cg'):
    raise ValueError("This appears to be methylation data (CpG sites), not gene expression data")

# Examine data structure 
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:") 
print(list(genetic_data.columns)[:5])