p3/preprocess/Mesothelioma/code/GSE68950.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mesothelioma"
cohort = "GSE68950"

# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE68950"

# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE68950.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE68950.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE68950.csv"
json_path = "./output/preprocess/3/Mesothelioma/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
# This dataset contains Affymetrix gene expression data (HT_HG-U133A array)
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait (Mesothelioma) can be inferred from disease state
trait_row = 1
# Age is not available in the characteristics data  
age_row = None
# Gender is not available in the characteristics data
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert disease state to binary indicating if it's mesothelioma"""
    if value is None or ':' not in value:
        return None
    disease = value.split(': ')[1].lower()
    if 'mesothelioma' in disease:
        return 1
    return 0

def convert_age(value: str) -> float:
    """Convert age value to float"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender value to binary"""
    return None

# 3. Save Metadata
# Conduct initial filtering and save cohort information
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. Clinical Feature Extraction
# Extract clinical features since 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 clinical features
preview = preview_df(clinical_features)
print("Clinical features preview:", preview)

# Save 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 the format of gene identifiers (e.g. "1007_s_at", "1053_at"), 
# these appear to be Affymetrix probe IDs rather than standard gene symbols
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))
# Extract gene mapping information from annotation data
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print data shapes and preview
print("Original probe data shape:", genetic_data.shape)
print("Gene data shape:", gene_data.shape)
print("\nFirst few rows of gene data:")
print(gene_data.head())
# 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(clinical_features, 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 explains why data was deemed unusable due to severe trait imbalance
note = "Dataset contains gene expression data from cancer cell lines, but mesothelioma samples are too rare (0.75%) for reliable analysis."
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=True, # Explicitly setting to True since proportion is <10% and count <5
    df=linked_data,
    note=note
)

# 6. Do not save linked data since trait distribution is biased
if is_usable:
    linked_data.to_csv(out_data_file)