p3/preprocess/Underweight/code/GSE50982.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Underweight"
cohort = "GSE50982"

# Input paths
in_trait_dir = "../DATA/GEO/Underweight"
in_cohort_dir = "../DATA/GEO/Underweight/GSE50982"

# Output paths
out_data_file = "./output/preprocess/3/Underweight/GSE50982.csv"
out_gene_data_file = "./output/preprocess/3/Underweight/gene_data/GSE50982.csv"
out_clinical_data_file = "./output/preprocess/3/Underweight/clinical_data/GSE50982.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 - Yes (gene data from cell lines)
is_gene_available = True

# 2.1 Data availability
# Trait (underweight): Determined by knockdown days (Row 1) as knockdown causes cachexia (weight loss)
trait_row = 1  
# Age and gender not available (cell line study)
age_row = None
gender_row = None 

# 2.2 Data type conversion functions
def convert_trait(value: str) -> Optional[float]:
    """Convert knockdown days to binary underweight indicator"""
    try:
        if ':' in value:
            days = float(value.split(':')[1].strip())
            # Based on cachexia effect mentioned in background, classify >=8 days as underweight
            return 1.0 if days >= 8 else 0.0
    except:
        return None
    return None

def convert_age(value: str) -> Optional[float]:
    return None  # Not available

def convert_gender(value: str) -> Optional[float]:
    return None  # Not available

# 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. Extract clinical features
clinical_selected = 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
)

print("Preview of selected clinical features:")
print(preview_df(clinical_selected))

# Save clinical data
clinical_selected.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 gene IDs are Illumina probe IDs (starting with ILMN_), not standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file by filtering lines with specified prefixes
gene_annotation = get_gene_annotation(soft_file_path)

# Load gene mapping from extracted annotation, looking for relevant ID and symbol columns 
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Preview the mapping data
print("Gene mapping preview:")
print(preview_df(gene_mapping))
# 1 & 2: Get gene mapping from extracted annotation
# Based on previews, ID column contains ILMN_ identifiers matching expression data, and Symbol contains gene names
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3: Apply mapping to convert probe data to gene expression
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Preview first few genes and their expression
print("Gene expression data preview:")
print(preview_df(gene_data))

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 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)