p3/preprocess/Lung_Cancer/code/GSE244647.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE244647"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244647"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244647.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244647.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244647.csv"
json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# Gene expression data availability
is_gene_available = True  # Based on dataset title mentioning NSCLC and HNSCC which indicates gene expression data

# Variable row identification 
trait_row = 1  # 'condition: tumour presence/tumour free' indicates cancer status
age_row = 5  # 'age: XX' contains age information
gender_row = 4  # 'Sex: Male/Female' contains gender information

# Conversion functions
def convert_trait(value: str) -> int:
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'tumour presence' in value:
        return 1
    elif 'tumour free' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> int:
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# Save metadata
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
)

# Extract clinical features since trait_row is available
selected_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 clinical data
print(preview_df(selected_clinical_df))

# Save clinical features
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Based on the format TC0100006437.hg.1 which appears to be probe IDs from a microarray platform 
# rather than standard human gene symbols, gene mapping will be required
requires_gene_mapping = True
# Detect miRNA dataset and handle appropriately
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=True,  # We already know trait data exists from Step 2
    note="Dataset contains miRNA measurements instead of gene expression data"
)
print("WARNING: This dataset contains miRNA measurements and is not suitable for gene expression analysis.")
print("Preprocessing pipeline will be terminated for this dataset.")