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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE104006"
# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE104006"
# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE104006.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE104006.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE104006.csv"
json_path = "./output/preprocess/3/Head_and_Neck_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. Check gene expression data availability
is_gene_available = True # Background info mentions "gene expression profiling"
# 2.1 Identify data rows
trait_row = 0 # Disease status in row 0
age_row = 2 # Age in row 2
gender_row = 3 # Gender/Sex in row 3
# 2.2 Define conversion functions
def convert_trait(value: str) -> int:
"""Convert disease status to binary where Thyroid_carcinoma=1, Non-neoplastic_thyroid=0"""
if pd.isna(value) or ":" not in value:
return None
value = value.split(": ")[1]
if "carcinoma" in value.lower():
return 1
elif "non-neoplastic" in value.lower():
return 0
return None
def convert_age(value: str) -> float:
"""Convert age to float"""
if pd.isna(value) or ":" not in value:
return None
try:
return float(value.split(": ")[1])
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary where F=0, M=1"""
if pd.isna(value) or ":" not in value:
return None
value = value.split(": ")[1].upper()
if value == 'F':
return 0
elif value == 'M':
return 1
return None
# 3. Save metadata with initial filtering
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
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
)
print("Preview of extracted clinical features:")
print(preview_df(clinical_df))
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Since the previous probe ID inspection revealed this is miRNA data, update metadata
is_gene_available = False
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("Warning: Dataset contains miRNA data rather than gene expression data.")
print("Further genetic data processing will be skipped.")
genetic_data = None |