p3/preprocess/Liver_Cancer/code/GSE164760.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE164760"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE164760"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE164760.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE164760.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE164760.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# According to the series title and summary, this dataset focuses on molecular characterization with expression arrays
is_gene_available = True  

# 2.1 Data Availability
# - Trait (NASH-HCC vs non-tumoral) can be inferred from tissue type at row 0
trait_row = 0
# - Age is not available in sample characteristics 
age_row = None  
# - Gender is not available in sample characteristics
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert tissue type to binary trait value.
    1: NASH-HCC tumor (case)
    0: NASH liver, non-tumoral NASH liver (control)
    None: Healthy liver, cirrhotic liver (excluded)
    """
    if not value or ':' not in value:
        return None
    tissue = value.split(':', 1)[1].strip().lower()
    if 'nash-hcc tumor' in tissue:
        return 1
    elif 'nash liver' in tissue:
        return 0
    else:
        return None

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

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

# 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_data_processed = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview the processed data
    print("Preview of processed clinical data:")
    print(preview_df(clinical_data_processed))
    
    # Save to CSV
    clinical_data_processed.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# The identifiers in format '11715100_at' appear to be Affymetrix probeset IDs
# rather than standard human gene symbols. They will need to be mapped to gene symbols.
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))
# 1. Identify columns for mapping
# In the annotation data, 'ID' contains the same probe IDs as in gene_expression data
# 'Gene Symbol' contains the corresponding gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

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

# 3. Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data_processed, gene_data)

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

# 4. Judge if features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Save cohort information 
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=trait_biased,
    df=linked_data,
    note="Expression array data of NASH-HCC patients and NASH controls. No age/gender information available."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)