p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE119958.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE119958"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE119958"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE119958.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE119958.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE119958.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))
# 1. Gene Expression Data Availability
is_gene_available = True  # Affymetrix HTA 2.0 arrays for mRNA expression

# 2. Variable Availability and Row IDs
trait_row = 1  # APOL1 genotype information in row 1

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

# 2.2 Conversion Functions
def convert_trait(value):
    """
    Convert APOL1 genotype to binary:
    0: Low risk (G0G0)
    1: High risk (G1G0, G2G0, G1G1, G1G2)
    """
    if not value or ':' not in value:
        return None
    genotype = value.split(': ')[1].strip()
    if 'G0G0' in genotype:
        return 0
    elif any(g in genotype for g in ['G1G0', 'G2G0', 'G1G1', 'G1G2']):
        return 1
    return None

def convert_age(value):
    return None  # Not available

def convert_gender(value):
    return None  # Not available

# 3. Save metadata for initial filtering
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:
    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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:", preview)
    
    # Save to CSV
    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())
# Looking at gene identifiers like 'TC01000001.hg.1', these are probe IDs from Affymetrix array
# and not standard human gene symbols. They need to be mapped.
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Identify ID and gene columns for mapping
# ID column contains same identifiers (e.g. TC01000001.hg.1) as gene expression data
# gene_assignment column contains gene symbols and details

# Process mapping between probe IDs and gene symbols 
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Apply mapping to convert probe measurements to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save genetic data
gene_data.to_csv(out_gene_data_file)
# 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 = "Dataset from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes."
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)