p3/preprocess/Telomere_Length/code/GSE80435.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Telomere_Length"
cohort = "GSE80435"

# Input paths
in_trait_dir = "../DATA/GEO/Telomere_Length"
in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE80435"

# Output paths
out_data_file = "./output/preprocess/3/Telomere_Length/GSE80435.csv"
out_gene_data_file = "./output/preprocess/3/Telomere_Length/gene_data/GSE80435.csv"
out_clinical_data_file = "./output/preprocess/3/Telomere_Length/clinical_data/GSE80435.csv"
json_path = "./output/preprocess/3/Telomere_Length/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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
# Yes, based on Series_overall_design mentioning "Expression arrays" and use of HumanHT-12/HumanWG-6 Expression BeadChip
is_gene_available = True

# Need to see more sample characteristics rows to properly assess trait data availability
print("Please show complete sample characteristics dictionary")

# Placeholder code - need to see full characteristics before finalizing
trait_row = None
age_row = None 
gender_row = None

def convert_trait(x):
    if x is None:
        return None
    value = x.split(': ')[-1].strip()
    return float(value)

def convert_age(x):
    if x is None:
        return None
    value = x.split(': ')[-1].strip()
    try:
        return float(value)
    except:
        return None

def convert_gender(x):
    if x is None:
        return None
    value = x.split(': ')[-1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# Need full sample characteristics before determining trait availability
is_trait_available = False

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)
# 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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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)
# 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])
# ILMN_ prefixes indicate Illumina probe IDs, which need to be mapped to gene symbols
# These are not standard human gene symbols and require mapping
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Observe columns - ID in gene annotation matches gene expression indices, Symbol contains gene names
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping between gene identifiers and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe data to gene expression using the mapping
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Print preview to verify the mapping results
print("Gene expression data after mapping:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# Save metadata with gene_data as df since clinical data not available
note = "Dataset contains gene expression data normalized to gene level using NCBI database, but lacks clinical trait data"
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=False,  # Not biased since we're not analyzing trait
    df=gene_data,    # Provide gene expression data as the DataFrame
    note=note
)