p3/preprocess/Hepatitis/code/GSE45032.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE45032"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE45032"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE45032.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE45032.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE45032.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 and summary, this is a gene expression microarray study
is_gene_available = True

# 2. Data Type Conversion Functions
def convert_trait(value):
    # Binary: 0 for CHC, 1 for HCC 
    if not value or ':' not in value:
        return None
    value = value.split(': ')[1].lower()
    if 'hepatitis' in value or 'chc' in value:
        return 0
    elif 'carcinoma' in value or 'hcc' in value:
        return 1
    return None

def convert_age(value):
    # Continuous
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value):
    # Binary: 0 for female, 1 for male
    if not value or ':' not in value:
        return None
    value = value.split(': ')[1].lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 2.1 Data Row Identification
trait_row = 0  # cell type field contains trait info
age_row = 3    # age(yrs) field
gender_row = 2 # gender field

# 3. Save Initial Metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Extract Clinical Features
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
    preview_result = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview_result)
    
    # 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)
# Looking at identifiers, which are just numbers starting from 1
# These are not human gene symbols and will need to be mapped
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))
# Extract gene mapping - using 'ID' as identifier column and 'GeneName' as gene symbol column
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GeneName')

# Drop control probes based on GeneName being control types
control_names = ['GE_BrightCorner', 'DarkCorner']
mapping_data = mapping_data[~mapping_data['Gene'].isin(control_names)]

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

# Preview the result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Since gene symbol normalization failed, we'll work with probe-level expression data
# Save the probe-level expression data
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data and link with probe-level expression data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

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

# 5. Record cohort information with probe-level data note
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 numerical probe-level expression data (gene symbol normalization was skipped) and clinical data."
)

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