p3/preprocess/Hemochromatosis/code/GSE159676.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hemochromatosis"
cohort = "GSE159676"

# Input paths
in_trait_dir = "../DATA/GEO/Hemochromatosis"
in_cohort_dir = "../DATA/GEO/Hemochromatosis/GSE159676"

# Output paths
out_data_file = "./output/preprocess/3/Hemochromatosis/GSE159676.csv"
out_gene_data_file = "./output/preprocess/3/Hemochromatosis/gene_data/GSE159676.csv"
out_clinical_data_file = "./output/preprocess/3/Hemochromatosis/clinical_data/GSE159676.csv"
json_path = "./output/preprocess/3/Hemochromatosis/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on the background information mentioning microarray experiments and gene expression profiling
is_gene_available = True

# 2. Variable Analysis
# 2.1 Data Availability
# Looking at sample characteristics:
# Trait (Hemochromatosis) can be determined from condition in row 0
trait_row = 0
# Age and gender not explicitly available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert condition to binary trait status.
    Returns 1 for Haemochromatosis, 0 for other conditions"""
    if pd.isna(value) or ':' not in value:
        return None
    value = value.split(':', 1)[1].strip().lower()
    if 'haemochromatosis' in value or 'hemochromatosis' in value:
        return 1
    else:
        return 0

convert_age = None
convert_gender = 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
if trait_row is not None:
    selected_clinical = 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 processed clinical data
    preview = preview_df(selected_clinical)
    print("Preview of clinical data:", preview)
    
    # Save clinical data
    selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be probe IDs from a microarray platform, not human gene symbols
# They require mapping to corresponding gene symbols for biological interpretation
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract mapping between probe IDs and gene symbols
# gene_assignment contains gene symbols in format "NM_XXX // SYMBOL // description"
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Handle cases with no gene assignment 
valid_rows = ~mapping_df['Gene'].str.contains('---', na=False)
mapping_df = mapping_df[valid_rows]

# Extract gene symbols from gene_assignment text
mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: x.split('//')[1].strip() if '//' in str(x) else None)

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

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview gene data
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
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
)

# 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 whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from liver tissue samples of patients with various liver conditions including hemochromatosis"
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=note
)

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