p3/preprocess/Adrenocortical_Cancer/code/GSE49278.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE49278"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"

# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE49278.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE49278.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Yes - Affymetrix Human Gene 2.0 ST Array data mentioned in background info
is_gene_available = True

# 2. Variable Availability and Row Identification
trait_row = 2  # Cell type row contains ACC info
age_row = 0    # Age data available 
gender_row = 1 # Gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert cell type to binary where ACC=1"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip().lower()
    if 'adrenocortical carcinoma' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to continuous numeric value"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip()
    try:
        return float(value)
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary where F=0, M=1"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip().upper()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

# 3. Save initial 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 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 extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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)
# The identifiers appear to be numeric probe IDs (16650001, etc)
# which are not human gene symbols and will need to be mapped
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# First inspect the SOFT file contents to understand the annotation format
import gzip
print("Inspecting SOFT file for gene mapping information:")
pattern = None
with gzip.open(soft_file, 'rt') as f:
    for i, line in enumerate(f):
        if i < 100:  # Check first 100 lines
            if "gene_assignment" in line or "gene_symbol" in line:
                print(f"\nFound gene mapping pattern in line: {line.strip()}")
                pattern = line
            elif "transcript_id" in line or "mrna_assignment" in line:
                print(f"\nFound alternative mapping pattern in line: {line.strip()}")
                pattern = line
        else:
            break

# Based on file inspection, extract gene annotation with appropriate prefixes
gene_annotation = get_gene_annotation(soft_file, prefixes=['#', '!platform_table_begin', '!platform_table_end'])

# Preview gene annotation data structure
print("\nGene annotation shape:", gene_annotation.shape)
print("\nAvailable columns:")
print(gene_annotation.columns.tolist())

# Display a few rows of relevant mapping columns 
mapping_cols = [col for col in gene_annotation.columns if 'gene' in col.lower() 
                or 'symbol' in col.lower() 
                or 'transcript' in col.lower()
                or col == 'ID']
if mapping_cols:
    print("\nPreview of mapping-related columns:")
    print(gene_annotation[mapping_cols].head())
else:
    print("\nNo obvious gene mapping columns found. Displaying first row:")
    print(gene_annotation.iloc[0])