p3/preprocess/Underweight/code/GSE131835.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Underweight"
cohort = "GSE131835"

# Input paths
in_trait_dir = "../DATA/GEO/Underweight"
in_cohort_dir = "../DATA/GEO/Underweight/GSE131835"

# Output paths
out_data_file = "./output/preprocess/3/Underweight/GSE131835.csv"
out_gene_data_file = "./output/preprocess/3/Underweight/gene_data/GSE131835.csv"
out_clinical_data_file = "./output/preprocess/3/Underweight/clinical_data/GSE131835.csv"
json_path = "./output/preprocess/3/Underweight/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the title and description, this dataset contains gene expression data from microarray
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# From sample characteristics:
# Row 1 shows "group" with CWS/CWL/CONTROL - can infer underweight status
trait_row = 1  
# Row 3 shows age
age_row = 3
# Row 2 shows gender
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert group info to binary underweight status"""
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().upper()
    # CWL (Cancer Weight Loss) indicates underweight
    if 'CWL' in value:
        return 1
    # CWS (Cancer Weight Stable) and CONTROL are not underweight
    elif 'CWS' in value or 'CONTROL' in value:
        return 0
    return None

def convert_age(x):
    """Convert age to continuous value"""
    if not x or ':' not in x:
        return None
    try:
        return float(x.split(':')[1].strip())
    except:
        return None

def convert_gender(x):
    """Convert gender to binary (0=female, 1=male)"""
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().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
if trait_row is not None:
    clinical_features = 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 results
    preview_df(clinical_features)
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The identifiers are in ENSEMBL format (ENSG...) with "_at" suffix
# These need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Extract gene mapping from annotation data
# ID column in annotation matches the probe IDs in expression data
# ORF column contains the gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')

# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# 1. Normalize gene symbols in 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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

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

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data and clinical information from Type 1 Diabetes patients."
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=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)