p3/preprocess/Breast_Cancer/code/GSE236725.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE236725"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE236725"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE236725.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE236725.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE236725.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, this dataset contains gene expression data as indicated by the background information
# "RNA was extracted from 31 pairs of FF and FFPE tumor samples... hybridized on Affymetrix microarrays"
is_gene_available = True

# 2.1 Data Row Identification
# trait (distant recurrence) is in row 1
trait_row = 1
# age is not available
age_row = None  
# gender is not available 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert distant recurrence to binary"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1]
    if value == "yes":
        return 1
    elif value == "no":
        return 0
    return None

def convert_age(value):
    """Not used since age data is unavailable"""
    return None

def convert_gender(value):
    """Not used since gender data is unavailable"""
    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:
    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 data
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    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 gene identifiers like "1007_s_at", "1053_at", etc, these appear to be 
# Affymetrix probe IDs from HG U133 series arrays, not gene symbols.
# They need to be mapped to official human gene symbols for analysis
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))
# Get mapping by selecting gene identifier and gene symbol columns from annotation data
prob_col = 'ID'  # Column containing probe IDs like "1007_s_at"
gene_col = 'Gene Symbol'  # Column containing gene symbols like "DDR1"
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# Convert probe measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview results
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# Save probe-level gene expression data (skipping normalization that causes data loss)
gene_data.to_csv(out_gene_data_file)

# Load clinical data
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 probe-level gene expression data and clinical trait data. Gene symbol normalization was skipped to preserve data integrity."
)

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