p3/preprocess/Breast_Cancer/code/GSE153316.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE153316"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE153316"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE153316.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE153316.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE153316.csv"
json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on series title/summary mentioning "gene expression profiles", this dataset contains gene expression data
is_gene_available = True 

# 2. Data Availability and Type Conversion
# Trait (Breast Cancer): All samples are from breast cancer patients (see subject status)
# Since all are cancer patients, there is no variance in trait values
trait_row = None

# Age data is available in row 2
age_row = 2

def convert_age(value):
    if not value or ':' not in value:
        return None
    age = value.split(':')[1].strip()
    try:
        return float(age)
    except:
        return None

# Gender: Not explicitly stated but all patients underwent mastectomy, 
# which is a surgery primarily for female breast cancer patients
gender_row = None

# We found that trait data is not available (trait_row is None), indicating this dataset is not usable
# Save this information
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False
)

# Since trait_row is None, we skip the clinical feature extraction step
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These identifiers are from Affymetrix array probes starting with "AFFX-" prefix,
# not standard human gene symbols. They need to be mapped to gene symbols.

requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract available platform IDs to see what annotation is present
platform_info, _ = filter_content_by_prefix(
    soft_file_path,
    prefixes_a=["!Platform_title"], 
    source_type='file',
    return_df_a=False
)

print("\nPlatform Information:")
print(platform_info)

# Since we found a mismatch between probe IDs in expression data ("AFFX-" format) 
# and annotation data, we need to record this as a data quality issue
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,
    is_trait_available=False,
    note="Gene annotation data does not match probe IDs in expression data"
)