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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE212250"
# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE212250"
# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE212250.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE212250.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE212250.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. Gene Expression Data Availability
# This dataset appears to be ChIP-seq (CUT&RUN) data looking at histone modifications (H3K4me3, H3K27ac)
# It does not contain gene expression data
is_gene_available = False
# 2. Variables Analysis
# 2.1 Data Availability
# Trait data: Cell line info is recorded in row 0
trait_row = 0
# Age data: Not available
age_row = None
# Gender data: Not available
gender_row = None
# 2.2 Data Type Conversion
def convert_trait(x):
"""Convert cell line type to binary (0: Primary, 1: Metastatic)"""
if pd.isna(x):
return None
x = str(x).lower()
# Extract value after colon if present
if ':' in x:
x = x.split(':')[1].strip()
if 'met' in x: # metastatic samples have "Met" in cell line name
return 1
elif 'pri' in x: # primary samples have "Pri" in cell line name
return 0
return None
# Age conversion not needed
convert_age = None
# Gender conversion not needed
convert_gender = 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 available
if trait_row is not None:
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
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save to file
clinical_features.to_csv(out_clinical_data_file) |