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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
cohort = "GSE244984"
# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE244984"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE244984.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE244984.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE244984.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
# This is a melanoma study examining molecular patterns, likely has gene expression data
is_gene_available = True
# 2.1. Identify Data Availability
# Trait (resistance) is available in row 1
trait_row = 1
# No age or gender data available
age_row = None
gender_row = None
# 2.2. Data Type Conversion Functions
def convert_trait(value: str) -> Optional[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 'CTLA4res' in value:
return 0
elif 'PD1res' in value:
return 1
return None
def convert_age(value: str) -> Optional[float]:
"""Convert age to float"""
if not value or ':' not in value:
return None
try:
age = float(value.split(':')[1].strip())
return age
except:
return None
def convert_gender(value: str) -> Optional[int]:
"""Convert gender to binary (0=female, 1=male)"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'female' in value or 'f' in value:
return 0
elif 'male' in value or 'm' in value:
return 1
return None
# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available)
# 4. Extract Clinical Features
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 data
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))
# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.to_csv(out_clinical_data_file)
# List all files to check for gene expression data
all_files = os.listdir(in_cohort_dir)
print("All files in directory:")
for f in all_files:
print(f)
# Since we found this is methylation data, and no other matrix file contains gene expression,
# we need to revise our earlier assessment
is_gene_available = False
# Save updated metadata with corrected gene availability info
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)
)
print("\nThis dataset contains methylation data rather than gene expression data.") |