p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE94321.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE94321"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE94321"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE94321.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE94321.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE94321.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
# Based on background info "[human mRNA]", this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# From sample characteristics, row 0 contains tissue info which can indicate trait
trait_row = 0  
age_row = None # Age data not available
gender_row = None # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait values to binary (0: control, 1: case)"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    # RMC (Renal Medullary Carcinoma) is a type of kidney cancer
    if value == 'rmc':
        return 1
    # Other tissue types are not kidney cancer
    elif value in ['rt', 'es', 'uc']:
        return 0
    return None

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

def convert_gender(value: str) -> Optional[int]:
    return None

# 3. Save Metadata
is_usable = 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. 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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # 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 identifiers appear to be probe/sequence IDs from a microarray platform
# The '_at' suffix is characteristic of Affymetrix probe IDs
# They need to be mapped to official 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))
# Extract mapping information from annotation data
# The 'ID' column matches the gene identifiers in expression data (microarray probe IDs)
# The Description column contains gene names that can be mapped to gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Description')

# Convert probe-level data to gene expression data by mapping probe IDs to gene symbols
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the resulting gene expression data 
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index[:5]))
# 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 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)