p3/preprocess/Esophageal_Cancer/code/GSE131027.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE131027"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE131027"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE131027.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE131027.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on background info showing expression features analysis

# 2. Variable Availability and Type Conversion
# 2.1 Data Availability
trait_row = 1  # Cancer type is recorded in row 1
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert cancer type to binary for esophageal cancer"""
    if pd.isna(value) or ':' not in value:
        return None
    cancer_type = value.split(': ')[1].lower()
    # Match variations of esophageal cancer spelling
    if 'oesophageal' in cancer_type or 'esophageal' in cancer_type:
        return 1
    return 0

def convert_age(value: str) -> float:
    return None  # Not used since age data unavailable

def convert_gender(value: str) -> int:
    return None  # Not used since gender data unavailable

# 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
    )
    
    # Preview the processed clinical features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# Based on the probe IDs shown (e.g., '1007_s_at', '1053_at'), these are Affymetrix probe IDs
# and not human gene symbols. They need to be mapped to standard gene symbols for analysis.
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# The gene identifiers are in the 'ID' column of gene annotation data, which matches 
# the probe IDs in gene expression data. Gene symbols are in the 'Gene Symbol' column.
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply the gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Preview the first few rows and columns of the gene data
print("\nPreview of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)

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

# 4. Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
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=note
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")