p3/preprocess/Depression/code/GSE201332.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE201332"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE201332"

# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE201332.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE201332.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE201332.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))
# 1. Gene Expression Data Availability
# Yes, this dataset contains transcriptional profiling data from whole blood samples
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait (Depression) data is in row 1 ("subject status")
trait_row = 1
# Age data is in row 3
age_row = 3  
# Gender data is in row 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert MDD status to binary: 0 for control, 1 for MDD"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'mdd' in value or 'depression' in value:
        return 1
    elif 'healthy' in value or 'control' in value:
        return 0
    return None

def convert_age(value):
    """Convert age to continuous numeric value"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    # Extract numeric value before 'y'
    try:
        age = int(value.replace('y',''))
        return age
    except:
        return None

def convert_gender(value):
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save Metadata
# Trait data is available (trait_row is not None)
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)

# 4. Clinical Feature Extraction
clinical_features = geo_select_clinical_features(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
preview_dict = preview_df(clinical_features)
print("\nPreview of clinical features:")
print(preview_dict)

# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# 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])
# The gene identifiers are simple numeric indices, not human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = pd.read_csv(soft_file, compression='gzip', delimiter='\t', skiprows=163, nrows=54675)

# Filter out control probes and probes without gene info
gene_metadata = gene_metadata[~gene_metadata['Name'].str.contains('Control|control|Corner', na=False)]
gene_metadata = gene_metadata[~gene_metadata['Gene Symbol'].isna()]

# Preview filtered annotation data
print("DataFrame shape after filtering:", gene_metadata.shape)
print("\nColumn names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Extract gene annotation data from SOFT file
def get_probe_gene_mapping(file_path):
    rows = []
    with gzip.open(file_path, 'rt') as f:
        in_spot_section = False
        for line in f:
            line = line.strip()
            
            # Identify start of SPOT section which contains probe mappings
            if line.startswith('!Platform_table_begin'):
                in_spot_section = True
                # Skip the header line
                next(f)
                continue
            elif line.startswith('!Platform_table_end'):
                in_spot_section = False
                continue
                
            if in_spot_section and line:
                fields = line.split('\t')
                # Get probe ID and gene name
                rows.append([fields[0], fields[2]])  # ID and GENE_NAME columns
    
    # Convert to DataFrame
    gene_metadata = pd.DataFrame(rows, columns=['ID', 'Gene'])
    # Filter out empty gene names and control probes
    gene_metadata = gene_metadata[
        (gene_metadata['Gene'].notna()) & 
        (gene_metadata['Gene'] != '') &
        (~gene_metadata['Gene'].str.contains('control|Control|Corner', na=False, regex=True))
    ]
    return gene_metadata

# Extract and preview annotation data
gene_metadata = get_probe_gene_mapping(soft_file)

# Preview filtered annotation data
print("DataFrame shape after filtering:", gene_metadata.shape)
print("\nColumn names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Get gene annotation data from SOFT file using direct extraction
def extract_platform_table(file_path):
    platform_data = []
    with gzip.open(file_path, 'rt') as f:
        in_table = False
        for line in f:
            if line.startswith('!Platform_table_begin'):
                headers = next(f).strip().split('\t')
                in_table = True
                continue
            if line.startswith('!Platform_table_end'):
                break
            if in_table and line.strip():
                platform_data.append(line.strip().split('\t'))
    return pd.DataFrame(platform_data, columns=headers)

# Extract gene metadata
gene_metadata = extract_platform_table(soft_file)

# Print column names
print("Column names in gene_metadata:")
print(gene_metadata.columns)
print("\nPreview of gene metadata:")
print(preview_df(gene_metadata))

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') 

# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview results
print("\nGene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
print("\nPreview of gene expression values:")
print(gene_data.head().iloc[:, :5])
# 1. Get gene annotation data from SOFT file 
gene_metadata = get_gene_annotation(soft_file)

# Print available columns to identify correct names
print("Available columns:", gene_metadata.columns)

# 2. Get gene mapping dataframe (using correct column names from gene_metadata)
mapping_df = get_gene_mapping(gene_metadata, prob_col='IDs', gene_col='Gene Symbols')

# 3. Convert probe-level data to gene expression data 
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# 4. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 5. Link clinical and genetic data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)  
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 6. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 7. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 8. Final validation and metadata saving
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="MDD vs healthy controls study"
)

# 9. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))