p3/preprocess/Lactose_Intolerance/code/GSE136395.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lactose_Intolerance"
cohort = "GSE136395"

# Input paths
in_trait_dir = "../DATA/GEO/Lactose_Intolerance"
in_cohort_dir = "../DATA/GEO/Lactose_Intolerance/GSE136395"

# Output paths
out_data_file = "./output/preprocess/3/Lactose_Intolerance/GSE136395.csv"
out_gene_data_file = "./output/preprocess/3/Lactose_Intolerance/gene_data/GSE136395.csv"
out_clinical_data_file = "./output/preprocess/3/Lactose_Intolerance/clinical_data/GSE136395.csv"
json_path = "./output/preprocess/3/Lactose_Intolerance/cohort_info.json"

# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# Based on background info mentioning "Microarray analysis" and "skeletal muscle biopsies",
# this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability

# Lactose intolerance status cannot be determined from this dataset
trait_row = None

# Age data is available at key 2
age_row = 2

# Gender data is available at key 0 
gender_row = 0

# 2.2 Data Type Conversion Functions

def convert_trait(x):
    return None

def convert_age(x):
    try:
        # Extract value after colon and convert to float
        age = float(x.split(': ')[1])
        # Filter out obviously wrong values (like age=5)
        if age < 18 or age > 120:
            return None
        return age
    except:
        return None

def convert_gender(x):
    try:
        # Extract value after colon
        gender = int(x.split(': ')[1])
        # Convert to match our encoding (female=0, male=1)
        # The dataset uses opposite encoding (female=1, male=0)
        return 1 - gender
    except:
        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. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Based on the gene identifiers shown, they appear to be probe IDs from an array platform
# The format "16650001" etc. are numeric probe IDs rather than standard HGNC gene symbols
# This will require mapping to proper gene symbols

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

# Preview column names and first few values
print("Gene Annotation Preview:")
print(preview_df(gene_annotation))
# Based on the IDs shown in genetic_data and gene_annotation, 
# the 'ID' column contains probe IDs matching genetic_data's index
# and 'gene_assignment' column contains gene symbol information
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Since trait data is not available, create a DataFrame with just gene data
linked_data = gene_data.T  # Transpose to have samples as rows

is_biased = True  # Dataset is biased due to lack of trait information 
note = "Dataset lacks trait (Lactose_Intolerance) information"

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=linked_data,
    note=note
)