p3/preprocess/Breast_Cancer/code/GSE249377.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE249377"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE249377"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE249377.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE249377.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE249377.csv"
json_path = "./output/preprocess/3/Breast_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")
# 1. Gene Expression Data Availability
# This is a transcriptomics dataset using MCF7 breast cancer cell line
is_gene_available = True

# 2.1 Variable availability - look at unique values under each feature ID
trait_row = 2  # Treatment status available in Feature 2
age_row = None  # No age information
gender_row = None  # No gender information - cell line data only

# 2.2 Data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    """Convert treatment data to binary: treated (1) vs untreated (0)"""
    if value is None or 'NA' in value:
        return None
    if 'untreated' in value:
        return 0
    if 'exposure' in value:
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    """Not used since age data unavailable"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Not used since gender data unavailable"""
    return None

# 3. Save metadata about data availability
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. Clinical feature extraction and save
if trait_row is not None:
    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 extracted features
    preview = preview_df(selected_clinical_df)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Debug: Print section around matrix begin marker
print("Examining data format:")
with gzip.open(matrix_file, 'rt') as f:
    found = False
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            found = True
            print("Found marker at line:", i)
            print("\nHeader line:")
            header = next(f).strip()
            print(header)
            print("\nFirst few data lines:")
            for _ in range(4):  # Print 4 data lines
                print(next(f).strip())
            break
    if not found:
        print("Matrix begin marker not found")

# Now extract gene expression data with better error handling
def get_gene_data_debug(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
    skip_rows = 0
    with gzip.open(file_path, 'rt') as file:
        for i, line in enumerate(file):
            if marker in line:
                skip_rows = i
                break
    
    # Read the data starting right after the marker line
    genetic_data = pd.read_csv(file_path, compression='gzip', skiprows=skip_rows+1, 
                           sep='\t', index_col=0)
    return genetic_data

gene_data = get_gene_data_debug(matrix_file)

# Print information about loaded data
print("\nShape 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].tolist())
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene data using library function
gene_data = get_genetic_data(matrix_file)

# Print debug info
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].tolist())
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Try direct read with error handling and verification
try:
    # Use the library function but verify output
    gene_data = get_genetic_data(matrix_file)
    
    # Verify we got data
    if gene_data.empty:
        print("WARNING: No gene expression data loaded!")
    else:
        print(f"\nSuccessfully loaded gene expression data with shape: {gene_data.shape}")
        print("\nFirst 5 rows:")
        print(gene_data.head())
        print("\nFirst 20 gene/probe identifiers:")
        print(gene_data.index[:20].tolist())
        
    # Save gene data
    gene_data.to_csv(out_gene_data_file)

except Exception as e:
    print(f"Error reading gene data: {e}")
    print("\nAttempting to examine file content:")
    with gzip.open(matrix_file, 'rt') as f:
        # Read a bit more after the header
        header = False
        for i, line in enumerate(f):
            if "!series_matrix_table_begin" in line:
                header = True
                print("\nFound data start marker")
                continue
            if header:
                print(f"Line after marker {i}: {line[:100]}...")
                if i > 85:  # Print a few lines after marker
                    break
# First examine if file contains platform/probe information
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    has_platform = False
    gene_annotation_lines = []
    for i, line in enumerate(f):
        line = line.strip()
        if 'platform' in line.lower():
            print(f"Platform line: {line}")
            has_platform = True
        # Collect lines between !platform_table_begin and !platform_table_end
        if has_platform and "!platform_table_begin" in line:
            next(f)  # Skip header line
            for data_line in f:
                if "!platform_table_end" in data_line:
                    break
                gene_annotation_lines.append(data_line)
            break

# Convert collected lines to dataframe if any found
if gene_annotation_lines:
    # Join lines and create dataframe
    annotation_text = ''.join(gene_annotation_lines)
    gene_metadata = pd.read_csv(io.StringIO(annotation_text), delimiter='\t')
    
    print("\nGene annotation dataframe shape:", gene_metadata.shape)
    print("\nColumn names:", gene_metadata.columns.tolist())
    print("\nFirst few rows:")
    print(gene_metadata.head())
else:
    print("\nNo gene annotation data found in platform section")
    # Create empty dataframe as placeholder
    gene_metadata = pd.DataFrame()