p3/preprocess/Head_and_Neck_Cancer/code/GSE151181.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE151181"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE151181"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE151181.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE151181.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE151181.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
# Based on background info, this is a gene + miRNA expression study
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait row: RAI response status is recorded at row 4, binary trait
trait_row = 4

# Age/Gender data not explicitly available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert RAI response status to binary
    Refractory (resistant) = 1, Avid (responsive) = 0"""
    if not isinstance(x, str):
        return None
    x = x.split(": ")[-1].strip().lower()
    if x == "refractory":
        return 1
    elif x == "avid":
        return 0
    return None

def convert_age(x):
    """Placeholder since age not available"""
    return None

def convert_gender(x):
    """Placeholder since gender not available"""
    return None

# 3. Save Initial Metadata
is_usable = 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
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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save clinical data
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
probe_ids = ['23064070', '23064071', '23064072', '23064073', '23064074']

# These are Agilent probe IDs that need to be mapped to human gene symbols
requires_gene_mapping = True
# List all files in the cohort directory
files = os.listdir(in_cohort_dir)
print("All files in directory:")
for f in files:
    print(f)
print()

# Since this is a SuperSeries (as stated in background info), look for subseries files
subseries_files = [f for f in files if 'series' in f.lower()]
gene_annotation = None

for file in subseries_files:
    file_path = os.path.join(in_cohort_dir, file)
    temp_annot = get_gene_annotation(file_path)
    
    # Check if this annotation contains gene data (not miRNA)
    if 'GENE_SYMBOL' in temp_annot.columns and \
       any(not str(x).startswith('hsa-miR') for x in temp_annot['GENE_SYMBOL'].dropna()):
        gene_annotation = temp_annot
        break

if gene_annotation is not None:
    # Preview column names and first 20 values
    preview_dict = preview_df(gene_annotation, n=20)
    print("Column names and preview values:")
    for col, values in preview_dict.items():
        print(f"\n{col}:")
        print(values)
else:
    print("No gene annotation data found in any file. This dataset may only contain miRNA data.")