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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE104006"
# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE104006"
# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE104006.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE104006.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE104006.csv"
json_path = "./output/preprocess/3/Thyroid_Cancer/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
is_gene_available = True # Based on Series title mentioning "gene expression profiling"
# 2.1 Data Availability
trait_row = 1 # Histology field contains tumor status
age_row = 2 # Age information is available
gender_row = 3 # Sex information is available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert histology to binary: 1 for tumor types, 0 for non-neoplastic"""
if not isinstance(value, str):
return None
value = value.split(': ')[-1].strip()
if value == 'Non-neoplastic_thyroid':
return 0
elif value in ['PDTC', 'PTC', 'PDTC+PTC', 'PTC+PDTC', 'PTC_lymph_node_metastasis']:
return 1
return None
def convert_age(value):
"""Convert age to continuous numeric value"""
if not isinstance(value, str):
return None
try:
return float(value.split(': ')[-1])
except:
return None
def convert_gender(value):
"""Convert gender to binary: 0 for female, 1 for male"""
if not isinstance(value, str):
return None
value = value.split(': ')[-1].strip()
if value == 'F':
return 0
elif value == 'M':
return 1
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. Clinical Feature Extraction
# Since trait_row is not None, we extract clinical features
selected_clinical = 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 extracted features
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("\nFirst 20 row IDs:")
print(list(genetic_data.index)[:20])
# Print basic data info
print("\nData preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Update is_gene_available since this is miRNA data
is_gene_available = False
# Save updated 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)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file) |