p3/preprocess/Breast_Cancer/code/GSE208101.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE208101"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE208101"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE208101.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE208101.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE208101.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
# Yes, this is gene expression data using Clariom D platform
is_gene_available = True

# 2.1 Data Availability
# Trait: Can use recurrence timing from row 2
trait_row = 2  

# Age: Not available 
age_row = None

# Gender: Available in row 0, but all female so not useful
gender_row = None 

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Convert recurrence timing to binary (early vs not early)
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip().upper()
    if value == 'EARLY':
        return 1
    elif value in ['INTERMEDIATE', 'LATE']:
        return 0
    return None

def convert_age(x):
    return None  # Not used

def convert_gender(x):
    return None  # Not used

# 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 available
if trait_row is not None:
    clinical_features = 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 and save
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Create directory if it doesn't exist
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_features.to_csv(out_clinical_data_file)
# 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]))
# Review gene identifiers - These appear to be platform-specific probe IDs (TC series with .hg.1 suffix)
# rather than standard human gene symbols (like BRCA1, TP53, etc.)
# They will need to be mapped to official 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)
# The 'ID' column in gene_metadata matches the identifiers in genetic_data
# The 'gene_assignment' column contains gene symbols, though in a complex format
prob_col = 'ID'
gene_col = 'gene_assignment'

# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview first few rows
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=trait_biased,
    df=linked_data,
    note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)