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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE66258"
# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE66258"
# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE66258.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE66258.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE66258.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
# Based on the series description, this is a microRNA dataset, not suitable for gene expression analysis
is_gene_available = False
# 2.1 Data Availability
# From sample characteristics:
# - trait: Row 0 shows all samples are ESCC tumor tissue
# - age: Not available
# - gender: Not available
trait_row = 0
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert ESCC tumor status to binary"""
if 'esophageal squamous cell carcinoma' in value.lower():
return 1
return None
def convert_age(value: str) -> float:
"""Convert age to float"""
return None # Not used since age data not available
def convert_gender(value: str) -> int:
"""Convert gender to binary"""
return None # Not used since gender data not available
# 3. Save metadata
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 since 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
)
# Preview and save
print("Clinical data preview:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file) |