p3/preprocess/Esophageal_Cancer/code/GSE107754.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE107754"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE107754.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE107754.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
# The background info mentions "whole human genome gene expression microarrays"
is_gene_available = True

# 2.1 Data Availability
# Trait (cancer) info is in key 2 under "tissue: ..."
trait_row = 2
# Age is not available in the sample characteristics
age_row = None  
# Gender is in key 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tissue type to binary indicating if it's esophageal cancer"""
    if not value or ':' not in value:
        return None
    tissue = value.split(':')[1].strip().lower()
    # Return 1 for esophageal cancer, 0 for other cancers
    return 1 if 'esophagus cancer' in tissue else 0

def convert_age(value: str) -> float:
    """Convert age string to float"""
    # No age data available
    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].strip().lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        return 1
    return None

# 3. Save initial validation info
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 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,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # 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])
# These identifiers are Agilent probe IDs, not HGNC gene symbols
# They follow the typical Agilent format "A_23_P######" 
# Therefore mapping to gene symbols is required
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)
# 1. The 'ID' column in gene_annotation matches the probe IDs in genetic_data
# The 'GENE_SYMBOL' column contains the corresponding gene symbols
probe_col = 'ID'
symbol_col = 'GENE_SYMBOL'

# 2. Get gene mapping dataframe 
gene_mapping = get_gene_mapping(gene_annotation, probe_col, symbol_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# 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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, 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.")