p3/preprocess/Longevity/code/GSE44147.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Longevity"
cohort = "GSE44147"

# Input paths
in_trait_dir = "../DATA/GEO/Longevity"
in_cohort_dir = "../DATA/GEO/Longevity/GSE44147"

# Output paths
out_data_file = "./output/preprocess/3/Longevity/GSE44147.csv"
out_gene_data_file = "./output/preprocess/3/Longevity/gene_data/GSE44147.csv"
out_clinical_data_file = "./output/preprocess/3/Longevity/clinical_data/GSE44147.csv"
json_path = "./output/preprocess/3/Longevity/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# This dataset uses Affymetrix Mouse Gene 1.0 ST Arrays for gene expression profiling
is_gene_available = True

# 2.1 Data Availability
# Trait (long-lived) cannot be determined since all samples are C57BL/6 mice
trait_row = None

# Age is available in row 2 with different age values
age_row = 2

# Gender information is not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None

def convert_age(x):
    # Extract numeric value
    value = x.split(': ')[1].split(' ')[0]
    try:
        return float(value)  # Convert to days as continuous variable
    except:
        return None
        
def convert_gender(x):
    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. Skip Clinical Feature Extraction since trait_row is None
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# These appear to be probe IDs from an array platform, not standard gene symbols
# They are numerical identifiers that will need mapping to gene symbols
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))
# 1&2. Extract probe-to-gene mapping columns and create mapping dataframe
# 'ID' column matches probe IDs in expression data, and 'gene_assignment' has gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

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

# 2-4. Skip linking and processing since no trait data available 

# 5. Final validation
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=None,
    df=None,
    note="Mouse gene expression data from different ages, not suitable for studying human traits."
)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-4. Skip clinical data processing since trait data is not available
# Create minimal DataFrame since dataset is not usable without trait data
minimal_df = pd.DataFrame()

# 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=True,
    is_trait_available=False,
    is_biased=True,  # No trait data means dataset is biased/unusable
    df=minimal_df,
    note="Mouse gene expression data measuring age effects. Not suitable for studying human traits."
)