p3/preprocess/Hepatitis/code/GSE66843.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE66843"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE66843"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE66843.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE66843.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE66843.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
# Looking at the background info, this is cell line data from Huh7.5.1 cells
# Cell line data typically contains gene expression, so set to True
is_gene_available = True

# 2.1 Data Availability

# Trait (HCV infection) data is available in Feature 1
# Values show clear control vs HCV infected groups
trait_row = 1

# Age is not applicable for cell line data
age_row = None 

# Gender is not applicable for cell line data
gender_row = None

# 2.2 Data Type Conversion Functions

def convert_trait(value: str) -> int:
    """Convert infection status to binary
    Mock infection (control) = 0
    HCV infection = 1
    """
    if not isinstance(value, str):
        return None
    value = value.lower()
    if 'mock' in value or 'control' in value:
        return 0
    elif 'hcv' in value:
        return 1
    return None

# Age and gender conversion functions not needed since data not available
convert_age = None
convert_gender = 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. Extract clinical features
if trait_row is not None:
    selected_clinical = 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 data
    preview = preview_df(selected_clinical)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    selected_clinical.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)
# Based on biomedical knowledge, the identifiers starting with "ILMN_" are 
# Illumina probe IDs, not human gene symbols. These probe IDs need to be mapped
# to human gene symbols for proper analysis.

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. Looking at the data:
# - Gene expression data uses identifiers like 'ILMN_1343291'
# - Gene annotation data has 'ID' column with same ILMN_ format identifiers
# - 'Symbol' column contains gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

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

# Preview result
print("Shape of gene-level expression data:", gene_data.shape)
print("\nFirst few rows of gene-level data:")
print(gene_data.head())
# Skip normalization since we already have valid gene symbols
gene_data.to_csv(out_gene_data_file)

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

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

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

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

# 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 gene-level expression data and clinical data."
)

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