p3/preprocess/Liver_cirrhosis/code/GSE212047.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE212047"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE212047"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE212047.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE212047.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE212047.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 Availability
# Yes, this dataset likely contains gene expression data as it's a microarray study of HSC Lhx2
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = None  # No cirrhosis status available
age_row = None   # No age data available  
gender_row = None  # No gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if x is None:
        return None
    # Binary: 0 for no cirrhosis, 1 for cirrhosis
    value = x.split(": ")[-1].strip().lower()
    return None  # Not used since trait data not available

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

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

# 3. Save Metadata
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # trait_row is None
)

# 4. Clinical Feature Extraction
# Skip 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 identifiers are probe IDs (numeric), not human gene symbols
# This indicates a need for gene mapping to convert probe IDs 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. Based on observation, 'ID' stores the same identifiers as gene expression data, 
# 'gene_assignment' contains gene symbols

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

# Preview the mapped gene data
print("\nFirst few genes and their expression values:")
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)

# Create a minimal dataframe for validation
minimal_df = pd.DataFrame(index=normalized_gene_data.index)
minimal_df[trait] = 0  # Add a dummy trait column 

# Validate 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,  # We determined this in Step 2
    is_biased=True,  # Mouse data is inherently biased for human studies
    df=minimal_df,
    note="This is mouse data with no human liver cirrhosis trait information available. Cannot be used for human trait association studies."
)

# Skip saving linked data since trait data is missing and data is not usable