p3/preprocess/Allergies/code/GSE84046.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE84046"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE84046"

# Output paths
out_data_file = "./output/preprocess/3/Allergies/GSE84046.csv"
out_gene_data_file = "./output/preprocess/3/Allergies/gene_data/GSE84046.csv"
out_clinical_data_file = "./output/preprocess/3/Allergies/clinical_data/GSE84046.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 background info, this is gene expression data from adipose tissue
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait - protein diet type in Feature 1 
trait_row = 1

def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    if 'high' in value:
        return 1  # High protein diet
    elif 'normal' in value:
        return 0  # Normal protein diet
    return None

# Age - can be calculated from birth date in Feature 5
age_row = 5 

def convert_age(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1]
    try:
        birth_year = int(value.split('-')[0])
        # Study year appears to be around 2013-2014 based on Series info
        study_year = 2014  
        return study_year - birth_year
    except:
        return None

# Gender - Feature 4
gender_row = 4

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

# 3. Save 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. Clinical Feature Extraction
# Since trait_row is not None, we extract clinical features
clinical_df = 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 and save clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# 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 the gene identifiers, they appear to be numerical IDs from Illumina array probes
# These need to be mapped to human gene symbols
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 'gene_assignment'):")
print(gene_annotation[['ID', 'gene_assignment']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'gene_assignment' column: Contains gene information from which symbols can be extracted")
# Get mapping data from annotation, using 'ID' and 'gene_assignment' columns
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

# Convert probe-level measurements to gene-level data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Preview the results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 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 if usable
    if is_usable:
        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)}"
    )