p3/preprocess/Obesity/code/GSE281144.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE281144"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE281144"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE281144.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE281144.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE281144.csv"
json_path = "./output/preprocess/3/Obesity/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on the series summary mentioning "gene expression (GE) determined by microarray"
is_gene_available = True

# 2.1 Data Row Identifiers
# Trait (diabetes status) is in row 1
trait_row = 1  
# No age data available 
age_row = None
# Gender data in row 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert diabetes status to binary (0: Control, 1: Diabetic)"""
    if not isinstance(value, str):
        return None
    value = value.lower()
    if 'diabetic' in value:
        return 1 
    elif 'control' in value:
        return 0
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0: Female, 1: Male)"""
    if not isinstance(value, str):
        return None
    value = value.lower()
    if ':' in value:
        value = value.split(':')[1].strip()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save Metadata
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. Extract Clinical Features
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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the processed clinical data
    preview = preview_df(clinical_features)
    print("Preview of processed clinical data:", preview)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the identifiers ending in '_st', these are from an Affymetrix microarray platform
# and need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation by reading the SOFT file and skipping header lines
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    lines = []
    for line in f:
        if line.startswith('!platform_table_begin'):
            next(f)  # Skip the header line
            for data_line in f:
                if data_line.startswith('!platform_table_end'):
                    break
                lines.append(data_line)
            break

# Convert to DataFrame
gene_annotation = pd.read_csv(io.StringIO(''.join(lines)), sep='\t')

# Preview columns and content
print("Gene annotation shape:", gene_annotation.shape)
print("\nColumns in annotation data:")
print(gene_annotation.columns.tolist())

# Print example rows showing probe ID and gene symbol columns 
print("\nFirst 5 rows of key mapping columns:")
if 'ID' in gene_annotation.columns and 'Gene Symbol' in gene_annotation.columns:
    print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
else:
    # Show all columns for the first few rows to identify mapping information
    print(gene_annotation.head().to_string())
# Create clean probe ID column
gene_annotation['ID'] = gene_annotation.iloc[:, 0].str.split('.').str[0] + '_st'

# Extract gene symbols from annotation strings
def extract_genes(annotation):
    if pd.isna(annotation):
        return []
    parts = str(annotation).split(' // ')
    # Gene symbols typically appear after accession IDs
    symbols = [parts[i] for i in range(1, len(parts), 3) if i < len(parts)]
    return symbols

# Create mapping dataframe with probe IDs and gene symbols
mapping_data = pd.DataFrame({
    'ID': gene_annotation['ID'],
    'Gene': gene_annotation.iloc[:, 7].apply(extract_genes)
})

# Apply mapping using library function
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# Create clean probe ID column
gene_annotation['ID'] = gene_annotation.iloc[:, 0].str.split('.').str[0] + '_st'

# Create mapping dataframe with probe IDs and gene symbols using extract_human_gene_symbols
mapping_data = pd.DataFrame({
    'ID': gene_annotation['ID'], 
    'Gene': gene_annotation.iloc[:, 7].apply(extract_human_gene_symbols)
})

# Apply mapping using library function
gene_data = apply_gene_mapping(gene_data, mapping_data)

# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
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)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for biased features and remove them if needed 
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
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=is_biased,
    df=linked_data,
    note="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
)

# 6. 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)