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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
cohort = "GSE261347"
# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE261347"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE261347.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE261347.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE261347.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene expression data availability
# Yes, according to the background info, it contains 1825 gene identifiers from Cancer Transcriptome Atlas
is_gene_available = True
# 2.1 Variable availability
# Trait (resistance status) is in row 1
trait_row = 1
# Age and gender not available in characteristics
age_row = None
gender_row = None
# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
"""Convert resistance status to binary (0=CTLA4res, 1=PD1res)"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip()
if value == 'CTLA4res':
return 0
elif value == 'PD1res':
return 1
return None
def convert_age(value: str) -> float:
"""Not used but defined for completeness"""
return None
def convert_gender(value: str) -> int:
"""Not used but defined for completeness"""
return None
# 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 available
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
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)
print("\nSkipping gene data extraction step since this dataset contains methylation data rather than gene expression data.") |