p3/preprocess/Osteoporosis/code/GSE224330.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE224330"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE224330"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE224330.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE224330.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE224330.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
# Based on background info mentioning "whole-genome transcriptomics" and "gene expression profiling"
is_gene_available = True

# 2.1 Data Availability
# For trait - look at comorbidity field which includes 'osteoporosis' 
trait_row = 3

# For age - available in field 1
age_row = 1

# For gender - available in field 2  
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[1].strip().lower()
    # Binary: 1 if has osteoporosis, 0 if not
    if value == 'osteoporosis':
        return 1
    elif value in ['none', 'hypothyroidism', 'schizoaffective disorder', 'arthrosis']:
        return 0
    return None

def convert_age(value):
    if pd.isna(value):
        return None
    # Extract numeric age value
    try:
        age = int(value.split(': ')[1].strip('y'))
        return age
    except:
        return None

def convert_gender(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return 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. Extract Clinical Features
if trait_row is not None:
    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(clinical_df)
    print("Clinical data preview:")
    print(preview)
    
    # Save to CSV
    clinical_df.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])
# Looking at the gene identifiers (A_19_P...), these are Agilent microarray probe IDs, not human gene symbols
# They need to be mapped to official gene symbols for standardization and interpretation
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))
# 1. Identify mapping columns
# From looking at the data:
# - Gene expression data uses identifiers like 'A_19_P00315452'
# - In gene annotation, 'ID' column has the same format
# - 'GENE_SYMBOL' column contains the target gene symbols

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Save gene data
gene_data.to_csv(out_gene_data_file)

# Preview results
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of mapped gene data:")
print(preview_df(gene_data))
# 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)