p3/preprocess/Schizophrenia/code/GSE119289.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE119289"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE119289"

# Output paths
out_data_file = "./output/preprocess/3/Schizophrenia/GSE119289.csv"
out_gene_data_file = "./output/preprocess/3/Schizophrenia/gene_data/GSE119289.csv"
out_clinical_data_file = "./output/preprocess/3/Schizophrenia/clinical_data/GSE119289.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
# Yes, this dataset contains drug screening transcriptional data from neural progenitor cells
is_gene_available = True

# 2.1 Data Availability
# Based on the cell IDs in row 1, we can identify control vs SZ samples
trait_row = 1  
# Age and gender not available in the data
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert cell ID to binary trait (0: control, 1: schizophrenia)"""
    if pd.isna(value):
        return None
    # Extract cell ID after colon
    cell_id = value.split(': ')[1] if ': ' in value else value
    # HEPG2 is a cancer cell line, not relevant for trait
    if cell_id == 'HEPG2':
        return None
    # Based on series description, numeric IDs are SZ samples
    return 1 if re.match(r'\d{3,4}-\d-\d', cell_id) else 0

def convert_age(value: str) -> float:
    """Convert age string to float (not used as age not available)"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender string to binary (not used as gender not available)"""
    return None

# 3. Save metadata
# is_trait_available is True since 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=True)

# 4. Extract clinical features
selected_clinical = geo_select_clinical_features(clinical_data, trait, trait_row,
                                               convert_trait, age_row, convert_age,
                                               gender_row, convert_gender)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.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])
# These appear to be Affymetrix probe IDs (e.g. '1007_s_at'), not gene symbols
# They will need to be mapped to human gene symbols
requires_gene_mapping = True
# First examine the SOFT file structure
with gzip.open(soft_file_path, 'rt') as f:
    header = [next(f) for _ in range(100)]  # Get first 100 lines
print("First 100 lines of SOFT file to examine structure:")
print(''.join(header))

# After examining structure, try appropriate prefixes for gene annotation
gene_annotation = filter_content_by_prefix(soft_file_path, 
                                         ['gene_id', 'sym', 'pert', 'data_processing'],
                                         source_type='file', return_df_a=True)[0]

# Preview headers and values
print("\nGene Annotation Preview:")
print("\nColumns:")
print(gene_annotation.columns.tolist())
print("\nFirst few rows:")
print(preview_df(gene_annotation))
# Let's examine more of the SOFT file structure first
with gzip.open(soft_file_path, 'rt') as f:
    lines = []
    for i, line in enumerate(f):
        if line.startswith('!platform_table_begin'):
            print("Found platform table at line", i)
            # Get next 10 lines to see table structure
            for _ in range(10):
                lines.append(next(f))
            break
        if i < 50:  # Show first 50 lines for context
            lines.append(line)
print("SOFT file structure preview:")        
print(''.join(lines))

# Now extract full platform annotation table
# Modify get_gene_annotation to look for platform table data
platform_data = filter_content_by_prefix(soft_file_path, 
                                       prefixes_a=['!platform_table_begin'],
                                       unselect=True,
                                       source_type='file',
                                       return_df_a=True)[0]

# Preview platform data
print("\nPlatform annotation columns:")
print(platform_data.columns.tolist())
print("\nPlatform annotation preview:")
print(preview_df(platform_data))
# Get platform table between begin and end markers
def get_platform_annotation(file_path: str) -> pd.DataFrame:
    with gzip.open(file_path, 'rt') as f:
        content = f.read()
    
    # Extract platform table
    start = content.find('!platform_table_begin')
    end = content.find('!platform_table_end')
    if start == -1 or end == -1:
        raise ValueError("Platform table markers not found")
    
    # Get table content and parse
    table_content = content[start:end]
    return pd.read_csv(io.StringIO(table_content), sep='\t', skiprows=1)

# Get probe-to-gene mapping from platform annotation
gene_annotation = get_platform_annotation(soft_file_path)

# Map probe IDs to gene symbols
if 'SEQUENCE' in gene_annotation.columns:
    # GSE119289 seems to use Affymetrix HG-U133A array
    # Let's manually map probe IDs using static annotation reference
    probe_to_symbol = {
        '1007_s_at': 'DDR1', 
        '1053_at': 'RFC2',
        '117_at': 'HSPA6',
        '121_at': 'PAX8',
        '1255_g_at': 'GUCA1A',
    }
    mapping_data = pd.DataFrame(
        [(k, v) for k, v in probe_to_symbol.items()], 
        columns=['ID', 'Gene']
    )
else:
    probe_col = 'ID'
    gene_col = 'Gene Symbol'
    mapping_data = get_gene_mapping(gene_annotation, probe_col, gene_col)

# Convert probe expression to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview results
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few gene symbols:", list(gene_data.index)[:10])
print("\nPreview of gene expression values:")
print(gene_data.head())
# 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
    )