p3/preprocess/Epilepsy/code/GSE74571.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE74571"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE74571"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE74571.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE74571.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE74571.csv"
json_path = "./output/preprocess/3/Epilepsy/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # The background info suggests this is a gene expression study of GBM cells

# 2. Variable Availability and Data Type Conversion
# 2.1 Row numbers for each variable
trait_row = 0  # Cell/tissue type indicates normal vs tumor samples
age_row = None  # No age information available
gender_row = None  # No gender information available

# 2.2 Conversion functions
def convert_trait(x: str) -> int:
    """Convert tissue type to binary: 1 for GBM/tumor, 0 for normal"""
    if pd.isna(x):
        return None
    value = x.split(": ")[1].lower() if ": " in x else x.lower()
    if "gbm" in value or "glioblastoma" in value:
        return 1
    elif "normal" in value or "control" in value:
        return 0
    return None

# Age and gender conversion functions not needed since data unavailable
convert_age = None
convert_gender = 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
    )
    
    # Preview the processed clinical data
    print("Preview of processed clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# ILMN identifiers are Illumina probe IDs and need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Get gene mapping dataframe from annotation
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the mapped gene expression data
print("Preview of gene expression data after mapping probes to genes:")
print("\nDataFrame shape:", gene_data.shape)
print("\nFirst few genes:")
print(gene_data.index[:5])
print("\nSample preview:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
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, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)