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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE47727"
# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE47727"
# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE47727.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE47727.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE47727.csv"
json_path = "./output/preprocess/3/Atrial_Fibrillation/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
# Based on the background info mentioning "gene expression" and "HumanHT-12", this is gene expression data
is_gene_available = True
# 2.1 Data Row Identification
# From characteristics dictionary:
# For trait: no direct AF status, all are controls based on background info
trait_row = None
# Age is in row 0
age_row = 0
# Gender is in row 1
gender_row = 1
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# All samples are controls
return 0
def convert_age(x):
# Extract numeric age from strings like "age (yrs): 67"
try:
age = int(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
# Convert gender strings to binary (female=0, male=1)
gender = x.split(': ')[1].lower()
if gender == 'female':
return 0
elif gender == 'male':
return 1
return None
# 3. Save initial metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False # trait_row is None
)
# 4. Extract clinical features
clinical_df = 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("\nPreview of extracted clinical features:")
print(preview_df(clinical_df))
# Save to CSV
clinical_df.to_csv(out_clinical_data_file) |