p3/preprocess/Obesity/code/GSE181339.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE181339"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE181339"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE181339.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE181339.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE181339.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
# Yes - this is a microarray experiment with PBMC RNA samples
is_gene_available = True

# 2.1 Data Availability
# The trait (obesity) can be inferred from group field (row 1)
trait_row = 1

# Age data is available in row 2
age_row = 2

# Gender data is available in row 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert obesity status to binary (0: Normal weight, 1: Overweight/Obese)"""
    if not value or ':' not in value:
        return None
    group = value.split(':')[1].strip()
    if group == 'NW':
        return 0
    elif group == 'OW/OB':
        return 1
    elif group == 'MONW': # Metabolically obese normal-weight should be labeled as obese
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to continuous values"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0: Female, 1: Male)"""
    if not value or ':' not in value:
        return None
    gender = value.split(':')[1].strip().lower()
    if gender == 'woman':
        return 0
    elif gender == 'man':
        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. Clinical Feature Extraction
if trait_row is not None:
    # Extract clinical features 
    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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save to CSV
    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)
# Based on the output shown, the gene identifiers are numeric IDs (e.g. '7', '8', '15', etc)
# These are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'GENE_SYMBOL' column: Contains gene symbol information")
# 1. Get probe-to-gene mapping from annotation data 
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 2. Apply mapping to get gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_df)

# 3. Preview the result
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head())
# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data.index = gene_data.index.str.replace('-mRNA', '')
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)