p3/preprocess/Epilepsy/code/GSE29796.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE29796"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE29796"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE29796.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE29796.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE29796.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
# Based on the background information, this is a transcriptional profiling study
# that compares gene expression between normal and tumor cells
is_gene_available = True

# 2.1 Data Availability
# trait_row = 1: "pathology" contains epilepsy status
# age_row = None: no age information available
# gender_row = None: no gender information available
trait_row = 1
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert pathology information to binary epilepsy status"""
    if not isinstance(value, str):
        return None
    # Extract value after colon
    if ":" in value:
        value = value.split(":")[1].strip().lower()
    # Epilepsy = 1, all other pathologies = 0
    if value == "epilepsy":
        return 1
    elif value in ["oligodendroglioma", "astrocytoma", "glioblastoma", 
                   "oligoastrocytoma", "glioblastoma, small cell",
                   "anaplastic astrocytoma", "gliosarcoma", 
                   "anaplastic oligoastrocytoma", "ganglioglioma",
                   "anaplastic oligodendroglioma"]:
        return 0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    return 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. 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 processed clinical data
    preview_df(selected_clinical)
    
    # Save to CSV
    selected_clinical.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])
# Based on the ID format (e.g. '1007_s_at', '1053_at'), these appear to be Affymetrix probe IDs
# rather than standard human gene symbols. They need to be mapped to 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))
# 1. Gene identifiers are stored in 'ID' column and gene symbols in 'Gene Symbol' column
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

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

# Preview the mapped gene data
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
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, 'Epilepsy') 

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

# 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="Dataset contains gene expression data comparing epilepsy and other brain conditions"
)

# 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)