p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE106757.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE106757"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE106757"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE106757.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE106757.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE106757.csv"
json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/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)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# Gene Expression Data Availability
is_gene_available = True  # Yes, the study involves transcriptional analysis of monocytes

# Variable Availability and Data Type Conversion 
trait_row = 0  # The trait (disease state) is in row 0
age_row = None  # Age not available
gender_row = None  # Gender not available

def convert_trait(value: str) -> Optional[int]:
    """Convert disease state to binary: 0 for healthy, 1 for renal cell carcinoma"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'healthy' in value:
        return 0
    elif 'renal cell carcinoma' in value or 'rcc' in value:
        return 1
    return None

convert_age = None  # Age data not available
convert_gender = None  # Gender data not available

# Initial validation and saving 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
)

# Clinical feature extraction 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
)

# Preview and save clinical features
print("Clinical Features Preview:")
print(preview_df(clinical_features))

# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be standard HGNC gene symbols mixed with some older identifiers
# Most identifiers like A1BG, A2M, AAAS are valid HGNC symbols
# However, some like 7A5, AAA1 are likely older or alternative identifiers
# Therefore mapping to current HGNC symbols would be beneficial for standardization
requires_gene_mapping = True
# First inspect if there's a platform section in the SOFT file
with gzip.open(soft_file_path, 'rt') as f:
    # Search for lines containing platform annotation
    for line in f:
        if line.startswith('^PLATFORM'):
            print("Found platform section:")
            # Print next 20 lines to understand the structure
            print('\n'.join([next(f).strip() for _ in range(20)]))
            break

# Then extract gene annotation using the library function
gene_metadata = get_gene_annotation(soft_file_path)
print("\nGene annotation data shape:", gene_metadata.shape)
print("\nColumns:", gene_metadata.columns.tolist())
print("\nPreview:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Since annotation mapping failed, directly normalize the gene symbols
gene_data = normalize_gene_symbols_in_index(genetic_data)

# Print preview of gene data
print("Gene data shape:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10].tolist())
# 1. Normalize gene symbols
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 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "This dataset contains gene expression data from blood monocyte subsets comparing renal cell carcinoma patients with healthy donors."
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 the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)