p3/preprocess/Asthma/code/GSE188424.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE188424"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE188424.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE188424.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE188424.csv"
json_path = "./output/preprocess/3/Asthma/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Yes, this dataset contains gene expression data from human blood samples,
# not just miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = None  # Not available in characteristics dictionary
gender_row = 0  # Gender data is available at index 0 
age_row = None  # No age data available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Won't be used since trait data is not available
    return None

def convert_gender(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1].strip()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

def convert_age(value):
    # Won't be used since age data is not available
    return None

# 3. Save Metadata - Initial Filtering
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 this step since trait_row is None
# 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)
# These are Illumina probe IDs (starting with ILMN_) and need to be mapped to gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. Identify mapping columns from gene annotation data
# 'ID' column matches the probe IDs (ILMN_) in expression data
# 'Symbol' column contains the gene symbols to map to
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Print shape and preview mapped data
print("\nShape of gene-level expression data:", gene_data.shape)
print("\nPreview of gene-level expression data:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Update cohort info - dataset not usable due to missing trait data
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=False,  # No trait data available
    is_biased=True,  # Dataset unusable without trait data
    df=gene_data,    # Pass gene expression data
    note="Dataset contains gene expression data but lacks trait information needed for association studies."
)