p3/preprocess/Celiac_Disease/code/GSE106260.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE106260"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE106260"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE106260.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE106260.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE106260.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the SOFT file
background_info, clinical_data = get_background_and_clinical_data(soft_file)

# Get unique values for each feature type in clinical data
unique_values = {}
for _, row in clinical_data.iterrows():
    for cell in row:
        if isinstance(cell, str) and ': ' in cell:
            feature_type = cell.split(': ')[0]
            feature_value = cell.split(': ')[1]
            if feature_type not in unique_values:
                unique_values[feature_type] = set()
            unique_values[feature_type].add(feature_value)

# Convert sets to lists for JSON serialization
unique_values = {k: list(v) for k, v in unique_values.items()}

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values, indent=2))
# 1. Gene Expression Data Availability
# Based on series title and summary, this appears to be a study of immune cell samples
# involving RNA gene expression analysis, not just miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# Looking at sample characteristics, trait (celiac disease) status isn't explicitly 
# recorded in sample characteristics. Inferring from the treatment status would not
# be reliable. No age or gender info is available either.
trait_row = None
age_row = None  
gender_row = None

# Define conversion functions even though they won't be used for this dataset
def convert_trait(x):
    """Convert trait value to binary"""
    if not isinstance(x, str):
        return None
    val = x.split(':')[-1].strip().lower()
    if 'celiac' in val or 'cd' in val:
        return 1
    elif 'control' in val or 'healthy' in val:
        return 0
    return None

def convert_age(x):
    """Convert age value to float"""
    if not isinstance(x, str):
        return None
    val = x.split(':')[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(x):
    """Convert gender to binary"""
    if not isinstance(x, str):
        return None
    val = x.split(':')[-1].strip().lower()
    if 'f' in val or 'female' in val:
        return 0
    elif 'm' in val or 'male' in val:
        return 1
    return None

# 3. Save metadata
# Initial filtering - trait data not available 
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Looking at the identifiers, they start with "ILMN_" which indicates these are Illumina probe IDs,
# not standard gene symbols. Therefore these need to be mapped to human gene symbols.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Observe ID mappings: 'ID' column in annotation matches ILMN IDs in expression data, 'Symbol' contains gene symbols
prob_col = 'ID'  
gene_col = 'Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to probe-level measurements to get gene expression data 
gene_data = apply_gene_mapping(genetic_df, mapping_data)

# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Save metadata indicating dataset is not usable due to missing trait data
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False
)