p3/preprocess/Schizophrenia/code/GSE145554.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE145554"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE145554"

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE145554.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE145554.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE145554.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 background info, this is a microarray study of mRNA, so gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 0  # Disease state is in row 0
gender_row = 1  # Sex is in row 1 
age_row = 3  # Age is in row 3

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    if 'schizophrenia' in value:
        return 1
    elif 'control' in value:
        return 0
    return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        age = int(x.split(': ')[-1])
        return age
    except:
        return None

# 3. Save metadata about data availability
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
if trait_row is not None:
    clinical_features = 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 the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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])
# The gene identifiers (7892501, 7892502, etc.) appear to be probe IDs from a microarray platform 
# rather than standard human gene symbols. They need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Extract probe-gene mapping
# 'ID' contains the same type of identifiers as in gene expression data
# 'gene_assignment' contains gene symbols and various annotations
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

# Apply the mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols to standard format, using NCBI gene synonym information
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
print("\nSample gene symbols before normalization:", list(gene_data.index)[:5])

try:
    # Verify synonym dictionary
    with open("./metadata/gene_synonym.json", "r") as f:
        synonym_dict = json.load(f)
    print("\nNumber of entries in synonym dictionary:", len(synonym_dict))
    print("Sample entries from synonym dict:", list(synonym_dict.items())[:2])
    
    genetic_data = normalize_gene_symbols_in_index(gene_data)
    print("\nGene data shape after normalization:", genetic_data.shape)
    
    if genetic_data.shape[0] == 0:
        raise ValueError("Gene symbol normalization resulted in empty dataset")
        
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
    genetic_data.to_csv(out_gene_data_file)
    
    # Load clinical data previously processed
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    print("\nClinical data shape:", selected_clinical_df.shape)

    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
    print("\nLinked data shape:", linked_data.shape)

    # 3. Handle missing values systematically  
    if trait in linked_data.columns:
        linked_data = handle_missing_values(linked_data, trait)

        # 4. Check for bias in trait and demographic features
        trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

        # 5. Final validation and information saving
        note = "This dataset studies alcohol dependence in brain tissue samples, containing gene expression data from the prefrontal cortex."
        is_usable = validate_and_save_cohort_info(
            is_final=True,
            cohort=cohort, 
            info_path=json_path,
            is_gene_available=True,
            is_trait_available=True,
            is_biased=trait_biased,
            df=linked_data,
            note=note
        )

        # 6. Save linked data only if usable and not biased
        if is_usable and not trait_biased:
            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
            linked_data.to_csv(out_data_file)
            
except Exception as e:
    print(f"\nError during preprocessing: {str(e)}")
    # Record failure
    note = f"Failed during gene symbol normalization: {str(e)}"
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,  
        is_trait_available=True,
        is_biased=None,
        df=None,
        note=note
    )