p3/preprocess/Allergies/code/GSE230164.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE230164"

# Output paths
out_data_file = "./output/preprocess/3/Allergies/GSE230164.csv"
out_gene_data_file = "./output/preprocess/3/Allergies/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/preprocess/3/Allergies/clinical_data/GSE230164.csv"
json_path = "./output/preprocess/3/Allergies/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
# Based on title mentioning "gene expression profiling", likely contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Find rows containing each variable
gender_row = 0  # Gender data is in row 0
trait_row = None  # No reliable allergy status data available
age_row = None  # No age data found

# 2.2 Data Type Conversion Functions
def convert_gender(x):
    if x is None:
        return None
    val = x.split(': ')[-1].lower()
    if 'female' in val:
        return 0
    elif 'male' in val:
        return 1
    return None
    
def convert_trait(x):
    # Placeholder function since trait data not available
    return None

def convert_age(x):
    # Placeholder function since age data not available 
    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
# Skip since trait data not available (trait_row is None)
# 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 "ILMN" prefix in gene identifiers indicates these are Illumina probe IDs
# These need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'Symbol'):")
print(gene_annotation[['ID', 'Symbol']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'Symbol' column: Gene symbols")
# Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Convert probe expression data to gene expression data using mapping
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the first few rows of mapped gene expression data
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. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Determine if features are biased
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort info
    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_trait_biased,
        df=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data only if usable AND trait is not biased
    if is_usable and not is_trait_biased:
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )