p3/preprocess/Breast_Cancer/code/GSE270721.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE270721"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE270721"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE270721.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE270721.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE270721.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
# Yes, HTA 2.0 microarrays were used for transcriptome analysis
is_gene_available = True

# 2.1 Data Availability
# Trait (Breast Cancer): Not directly available in characteristics but can be inferred from series info
# No direct trait row since all samples are breast cancer
trait_row = None

# Age available in row 2  
age_row = 2

# Gender not explicitly available and cannot be reliably inferred
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait_row is None
    return None

def convert_age(x):
    # Extract numeric value after colon
    if 'not available' in x.lower():
        return None
    try:
        age = float(x.split(':')[1].strip())
        return age
    except:
        return None

def convert_gender(x):
    # Not used since gender_row is None
    return None

# 3. Save 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. Clinical Feature Extraction
# Skip since trait_row is None and clinical data would be uninformative
# 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)
# Review gene identifiers - these appear to be Affymetrix probesets from transcriptome array
# Format TC#######.hg.1 indicates these need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# 1. Identify columns for gene mapping
# ID column contains probe identifiers that match gene expression data
# gene_assignment column contains gene symbols

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

# 3. Apply gene mapping to convert probe measurements to gene expression data 
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview the mapped data
print("\nShape after mapping to genes:", gene_data.shape)
print("\nFirst few mapped gene expression values:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Since trait data is not available, record this in initial filtering 
# without proceeding to full data validation
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False
)