p3/preprocess/Colon_and_Rectal_Cancer/code/GSE46517.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Colon_and_Rectal_Cancer"
cohort = "GSE46517"

# Input paths
in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46517"

# Output paths
out_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/GSE46517.csv"
out_gene_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv"
out_clinical_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/clinical_data/GSE46517.csv"
json_path = "./output/preprocess/3/Colon_and_Rectal_Cancer/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))
# Gene Expression Data Availability
# From Series Info: Affymetrix U133A chip is used, which measures gene expression
is_gene_available = True 

# Trait Data - Convert Primary/Metastatic Melanoma vs Others
trait_row = 0  # Found in key 0: "tissue type: ..."
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.lower()
    if 'metastatic melanoma' in value or 'primary melanoma' in value:
        return 1
    elif 'nevus' in value or 'normal' in value:
        return 0
    return None

# Age Data
age_row = 7  # Found in key 7: "age at time of resection: ..."
def convert_age(value):
    if pd.isna(value):
        return None
    try:
        # Extract years from format like "72y 4m"
        years = float(value.split('y')[0].split(':')[-1].strip())
        return years
    except:
        return None

# Gender Data 
gender_row = 8  # Found in key 8: "gender: ..."
def convert_gender(value):
    if pd.isna(value):
        return None
    value = value.lower().split(':')[-1].strip()
    if value == 'female':
        return 0
    elif value == 'male':  
        return 1
    return None

# Initial Validation
is_trait_available = True
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)

# Clinical Feature Extraction
clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row,
                                               convert_trait, age_row, convert_age,
                                               gender_row, convert_gender)
clinical_preview = preview_df(clinical_features)
print(clinical_preview)

# 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])
# These appear to be Affymetrix probe IDs rather than gene symbols
# The '_at' suffix is characteristic of Affymetrix array probe identifiers
# These will need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# 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))
# Extract mapping between probe IDs and gene symbols from gene annotation data
gene_mapping = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

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

# Preview results 
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. 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)

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

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

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

# 5. 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="Dataset contains gene expression data from melanoma samples including metastatic/primary melanoma vs. nevi/normal"
)

# 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)