p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE127136.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE127136"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE127136"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE127136.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE127136.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE127136.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
# This is single-cell RNA-seq data, not miRNA or methylation data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# For trait (cancer vs normal), we can use disease state field (row 1)
trait_row = 1

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

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if ':' in str(x):
        value = str(x).split(':')[1].strip().lower()
        if 'kidney cancer' in value:
            return 1  # cancer samples
        elif 'normal' in value:
            return 0  # normal samples (paracancerous tissues)
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save Metadata
# Validate and save cohort info (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. Clinical Feature Extraction
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
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save clinical data
    selected_clinical.to_csv(out_clinical_data_file)
# Since we've discovered the gene expression data is in a different database,
# we need to abort the gene data extraction attempt
print("Gene expression data for this series is stored in the Genome Sequence Archive for Human database")
print("(https://bigd.big.ac.cn/gsa-human/) under accession PRJCA003506")
print("The data is not available in GEO matrix format required by our pipeline")

# Set genetic data to empty dataframe to indicate extraction failed
genetic_data = pd.DataFrame()