p3/preprocess/Schizophrenia/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

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

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

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE273630.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE273630.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
# Based on the Series summary, this is a custom panel for dopamine-regulated inflammatory genes
# So gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait (Schizophrenia) - Not available as participants are explicitly excluded if they have schizophrenia
trait_row = None

# Age - Not extractable from characteristics dict, but background info states all participants are 35-44 years old
# Since age range is narrow and constant for all subjects, consider it not useful/available
age_row = None

# Gender - Not in characteristics dict, but background info states all participants are males
# Since gender is constant for all subjects, consider it not useful/available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait data not available
    return None

def convert_age(x):
    # Not used since age data not available
    return None 

def convert_gender(x):
    # Not used since gender data not available
    return None

# 3. Save Metadata
# Initial filtering - validate and save cohort info 
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)
)

# 4. Clinical Feature Extraction
# Skip since trait_row is None, indicating clinical data is not available
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 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])
# Looking at the gene IDs, they appear to be proper HGNC gene symbols
# e.g. ABAT, ABL1, ACAA1 which are well-known human gene symbols

requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
print("Gene data shape after normalization:", genetic_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
genetic_data.to_csv(out_gene_data_file)

# 2-4. Skip linking and missing value handling since no clinical data available

# 5. Final validation - dataset cannot be used for schizophrenia study
# Note that background info explicitly states schizophrenia patients were excluded
note = "This dataset contains gene expression data but lacks information about schizophrenia status. The study explicitly excluded patients with schizophrenia in their exclusion criteria."
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,  # Biased since schizophrenia patients were explicitly excluded
    df=genetic_data.T,  # Pass transposed gene expression data as samples should be in rows
    note=note
)

# 6. Do not save linked data as the dataset is not usable for trait analysis