p3/preprocess/Rectal_Cancer/code/GSE133057.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE133057"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE133057"

# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE133057.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE133057.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE133057.csv"
json_path = "./output/preprocess/3/Rectal_Cancer/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, from background info this is transcriptomic analysis of rectal cancer biopsies
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# AJCC score is a measure of tumor regression (phenotypic trait)
trait_row = 1  
gender_row = 2
age_row = 5

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert AJCC score to binary where 0,1 are good response (0) and 2,3 are poor response (1)"""
    if not value or ':' not in value:
        return None
    score = int(value.split(': ')[1])
    if score in [0, 1]:
        return 0  # good response
    elif score in [2, 3]:
        return 1  # poor response
    return None

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

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if not value or ':' not in value:
        return None
    gender = value.split(': ')[1].lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        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:
    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)
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    clinical_features.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])
# Based on "ILMN_" prefix in gene identifiers, these appear to be Illumina probe IDs, not gene symbols
# They will need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. From observation:
# - Gene expression data uses 'ILMN_' prefixed IDs
# - In gene annotation, 'ID' column stores these identifiers, and 'Symbol' column stores gene symbols

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# 3. Apply mapping to convert probe level data to gene level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview and save the gene expression data
print("\nGene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))

gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

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

# 3. Handle missing values systematically  
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 = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
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 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)