p3/preprocess/Osteoporosis/code/GSE56814.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE56814"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE56814"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE56814.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE56814.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE56814.csv"
json_path = "./output/preprocess/3/Osteoporosis/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)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# This dataset contains microarray gene expression data (Affymetrix 1.0 ST arrays)
is_gene_available = True

# 2.1 Data Availability
# For trait (BMD status): available in row 1, binary (high vs low BMD)
trait_row = 1

# For gender: all female based on row 0, so not useful for association study
gender_row = None

# For age: not directly available in sample characteristics
age_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert BMD status to binary: high BMD=1, low BMD=0"""
    if isinstance(value, list):
        value = value[0]  # Take the first item if it's a list
    if pd.isna(value):
        return None
    if ':' in str(value):
        value = value.split(':')[1].strip()
    if 'high bmd' in value.lower():
        return 1
    elif 'low bmd' in value.lower():
        return 0
    return None

# Age conversion not needed since age not available
convert_age = None

# Gender conversion not needed since gender not available
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 extracted features
    preview = preview_df(selected_clinical)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save clinical data
    selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The row IDs appear to be numeric identifiers (illumina probe IDs) rather than gene symbols
# These need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Create gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Preview result
print("Shape of gene data after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=trait_biased,
    df=linked_data,
    note=note
)

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