p3/preprocess/Hepatitis/code/GSE168049.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE168049"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE168049"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE168049.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE168049.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE168049.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 - Yes, as it's about mRNA in PBMCs
is_gene_available = True

# 2.1 Data Availability
trait_row = 4  # survival state
age_row = 3    # age is available 
gender_row = 2 # gender is available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert survival state to binary (0=dead, 1=survival)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'survivial' in value:  # Note: there's a typo in the data
        return 1
    elif 'dead' in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to float"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0=female, 1=male)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    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. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = 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 the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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)
# The gene identifiers look like Agilent microarray probe IDs (starting with 'A_19_P')
# rather than standard human gene symbols (like BRCA1, TP53, etc.)
# These probe IDs will need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# 1. Identify columns
prob_col = 'ID'  # Column storing Agilent probe IDs (A_19_P...)
gene_col = 'GENE_SYMBOL'  # Column storing standard gene symbols

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Preview results
print("Shape after gene mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Since gene normalization is failing, work with the probe-level expression data
gene_data = gene_data.astype(float)  # Ensure numeric values
gene_data.index = gene_data.index.astype(str)  # Convert index to strings for consistency
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data from previous steps
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 3. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 5. Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Record 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=is_biased,
    df=linked_data,
    note="Contains probe-level expression data (gene normalization skipped) and clinical data."
)

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