p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE131027.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE131027"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE131027"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE131027.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE131027.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
# Since we have gene mutation data but no explicit gene expression matrix shown,
# this dataset likely contains pure mutation data rather than expression data
is_gene_available = False

# 2. Variable Availability and Data Type Conversion
# 2.1 Cancer type is recorded in row 1, we can use it to identify kidney cancer cases
trait_row = 1

# Age and gender are not recorded in the sample characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon 
    if ':' in str(x):
        value = str(x).split(':')[1].strip().lower()
        # Check if it's kidney cancer
        if 'renal cell carcinoma' in value:
            return 1
        else:
            return 0
    return None

def convert_age(x):
    return None

def convert_gender(x): 
    return None

# 3. Initial Filtering and 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:
    # Extract clinical features
    clinical_df = geo_select_clinical_features(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 data
    preview = preview_df(clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save to CSV
    clinical_df.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 are probe IDs from the Affymetrix human microarray platform
# They need to be mapped to human gene symbols for analysis
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))
# 1. Looking at gene annotations, 'ID' matches probe IDs in expression data, and 'Gene Symbol' has corresponding gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to convert probe expression to gene expression 
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 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_df, 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 = judge_binary_variable_biased(linked_data, trait)
    if "Age" in linked_data.columns:
        if judge_continuous_variable_biased(linked_data, "Age"):
            linked_data = linked_data.drop(columns="Age")
    if "Gender" in linked_data.columns:
        if judge_binary_variable_biased(linked_data, "Gender"):
            linked_data = linked_data.drop(columns="Gender")

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
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)