p3/preprocess/Hepatitis/code/GSE97475.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE97475"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE97475"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE97475.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE97475.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE97475.csv"
json_path = "./output/preprocess/3/Hepatitis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on series title, this seems to be a study involving immune profiling with RNA samples
# The cell types listed include RNA samples, so gene expression data should be available
is_gene_available = True

# 2. Clinical Feature Information

# 2.1 Data Availability
# Based on sample characteristics:
# - age data is available in subjects.demographics.age (row 81)
# - gender data is in subjects.demographics.sex (row 118)
# - no explicit hepatitis data/values found

age_row = 81
gender_row = 118
trait_row = None  # No explicit hepatitis status found

# 2.2 Data Type Conversion Functions
def convert_age(value):
    try:
        # Extract numeric value after colon
        age = float(value.split(': ')[1])
        return age
    except (ValueError, IndexError):
        return None

def convert_gender(value):
    try:
        gender = value.split(': ')[1].strip().lower()
        if gender == 'female':
            return 0
        elif gender == 'male':
            return 1
        return None
    except (ValueError, IndexError):
        return None

def convert_trait(value):
    # Not used since trait data not available
    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=False  # No hepatitis status data found
)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
requires_gene_mapping = False
# Since trait data is unavailable (trait_row=None), we cannot use this dataset
# Simply record cohort information indicating unusable due to missing 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=None,
    df=None,
    note="Dataset contains gene expression data but lacks hepatitis trait information."
)