p3/preprocess/Liver_cirrhosis/code/GSE85550.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE85550"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE85550"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE85550.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE85550.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE85550.csv"
json_path = "./output/preprocess/3/Liver_cirrhosis/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
is_gene_available = True  # Based on study title, this is a molecular signature study likely containing gene expression data

# 2.1 Data Availability 
trait_row = 2  # Time point can indicate disease progression state
age_row = None  # Age information not available
gender_row = None  # Gender information not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if value is None:
        return None
    value = value.split(': ')[-1].strip()
    return 1 if value == 'Follow-up' else 0  # Follow-up represents more advanced disease state
    
def convert_age(value):
    # Not needed since age data unavailable
    return None

def convert_gender(value):
    # Not needed since gender data unavailable
    return None

# 3. Save Initial Validation Results
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True  # trait_row is available
)

# 4. Clinical Feature Extraction
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_df(clinical_df)
clinical_df.to_csv(out_clinical_data_file)
# 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 standard human gene symbols (e.g. AARS, ABLIM1, ACOT2 etc.)
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Judge if features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Save cohort information 
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="Expression array data of NASH-HCC patients and NASH controls. No age/gender information available."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)