p3/preprocess/Osteoporosis/code/GSE80614.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE80614"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE80614"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE80614.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE80614.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE80614.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
# The background info mentions "microarray expression profiling" indicating gene expression data
is_gene_available = True

# 2.1 Data Availability
# Trait data not directly available since this is a control/case study comparing 
# osteogenic vs adipogenic differentiation, not diseased vs healthy samples
trait_row = None

# Age data available in key 1
age_row = 1

# Gender data available in key 0 
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not needed since trait data not available
    return None

def convert_age(x):
    # Extract number from string like "age: 19 years" or "age: 19"
    try:
        return float(x.split(': ')[1].split(' ')[0])
    except:
        return None

def convert_gender(x):
    # Convert gender to binary (female=0, male=1)
    try:
        gender = x.split(': ')[1].lower()
        if gender == 'male':
            return 1
        elif gender == 'female': 
            return 0
        return None
    except:
        return None

# 3. Save metadata
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=(trait_row is not None))

# 4. Skip clinical feature extraction since trait_row is None
# 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 gene identifiers start with "ILMN_" indicating these are Illumina probe IDs, not standard gene symbols
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 the relevant columns for mapping
# 'ID' in gene annotation matches the probe IDs (ILMN_*) from gene expression data
# 'Symbol' contains the corresponding gene symbols
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

note = "Gene expression data available but no clinical variables for association studies"
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=False,
    is_biased=True,  # Set to True since no clinical data makes it unusable
    df=genetic_data,  # Pass the gene expression data
    note=note
)