p3/preprocess/Mesothelioma/code/GSE117668.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mesothelioma"
cohort = "GSE117668"

# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE117668"

# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE117668.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE117668.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE117668.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
# Yes, this is a microarray study of gene expression data
is_gene_available = True

# 2.1 Data Availability
# For trait (mesothelioma status), available in row 1 (diagnosis)
trait_row = 1

# Age and gender not available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert diagnosis to binary: 1 for mesothelioma, 0 for healthy"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'mesothelioma' in value:
        return 1
    elif 'healthy' in value:
        return 0
    return None

convert_age = None
convert_gender = None

# 3. Save metadata about data availability
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:
    selected_clinical = 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 data
    preview = preview_df(selected_clinical)
    print("Preview of clinical data:")
    print(preview)
    
    # Save to CSV
    selected_clinical.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])
# Looking at the gene identifiers, we see probe names like "100009613_at", "10000_at", etc
# These are Affymetrix probe IDs, not standard human gene symbols 
# We need to map these probe IDs to gene symbols before further 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. Identify mapping columns:
# 'ID' in annotation matches probe IDs in expression data (e.g., "100009613_at")
# 'Description' contains gene names/descriptions

# 2. Get gene mapping dataframe
# The ID column already matches between annotation and expression data
mapping_data = gene_annotation[['ID', 'Description']].copy()

# Since Description contains full gene names, extract just the gene symbols
def extract_first_word(text):
    """Extract the first word before any special characters or spaces"""
    if isinstance(text, str):
        return text.split()[0]
    return None

mapping_data['Gene'] = mapping_data['Description'].apply(extract_first_word)
mapping_data = mapping_data[['ID', 'Gene']]

# 3. Convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview results
print("Gene mapping preview:")
print(preview_df(mapping_data))

print("\nGene expression data preview:")
print(preview_df(gene_data))
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, genetic_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 healthy cells and mesothelioma cell lines, suitable for case-control 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=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)