p3/preprocess/Lung_Cancer/code/GSE249568.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE249568"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE249568"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE249568.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE249568.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE249568.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")
is_gene_available = True  # Based on the background info mentioning "Cancer Transcriptome Atlas" expression data

# For trait: resectable lung cancer pre/post treatment
trait_row = 0  # Found in row 0 with "tissue: NSCLC" 
def convert_trait(value: str) -> Optional[int]:
    if not isinstance(value, str):
        return None
    if ":" not in value:
        return None
    value = value.split(":")[-1].strip().lower()
    if "nsclc" in value:  # NSCLC indicates the presence of lung cancer
        return 1 
    return None

# Age not available in sample characteristics
age_row = None
convert_age = None

# Gender not available in sample characteristics
gender_row = None 
convert_gender = None

# Save metadata about dataset usability
is_trait_available = trait_row is not None
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=is_trait_available)

# Extract clinical features since trait data is available
if trait_row is not None:
    clinical_features = geo_select_clinical_features(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
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.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 gene identifiers observed in the data (e.g. A2M, ABCB1, ABCF1, ABL1),
# these appear to be standard human gene symbols and don't need mapping
requires_gene_mapping = False
# Work directly with gene data since symbols are already standardized
gene_data = pd.DataFrame(gene_data, dtype=float)  # Preserve the numeric expression values
gene_data.index = gene_data.index.astype(str)  # Ensure string index for consistent joining
gene_data.to_csv(out_gene_data_file)

# Load clinical data from previous steps
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Record cohort information
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Contains gene expression data with standard gene symbols and clinical data. All samples are lung cancer cases."
)

# Save data if usable
if is_usable:
    linked_data.to_csv(out_data_file)